Comparative transcriptome of normal and cancer-associated fibroblasts

Apoorva Abikar; Mohammad Mehaboob Subhani Mustafa; Radhika Rajiv Athalye; Namratha Nadig; Ninad Tamboli; Vinod Babu; Ramaiah Keshavamurthy; Prathibha Ranganathan

doi:10.1186/s12885-024-13006-x

. 2024 Oct 5;24:1231. doi: 10.1186/s12885-024-13006-x

Comparative transcriptome of normal and cancer-associated fibroblasts

Apoorva Abikar ^1,³, Mohammad Mehaboob Subhani Mustafa ¹, Radhika Rajiv Athalye ¹, Namratha Nadig ¹, Ninad Tamboli ², Vinod Babu ², Ramaiah Keshavamurthy ², Prathibha Ranganathan ^1,^3,^✉

PMCID: PMC11456241 PMID: 39369238

Abstract

Background

The characteristics of a tumor are largely determined by its interaction with the surrounding micro-environment (TME). TME consists of both cellular and non-cellular components. Cancer-associated fibroblasts (CAFs) are a major component of the TME. They are a source of many secreted factors that influence the survival and progression of tumors as well as their response to drugs. Identification of markers either overexpressed in CAFs or unique to CAFs would pave the way for novel therapeutic strategies that in combination with conventional chemotherapy are likely to have better patient outcome.

Methods

Fibroblasts have been derived from Benign Prostatic Hyperplasia (BPH) and prostate cancer. RNA from these has been used to perform a transcriptome analysis in order to get a comparative profile of normal and cancer-associated fibroblasts.

Results

The study has identified 818 differentially expressed mRNAs and 17 lincRNAs between normal and cancer-associated fibroblasts. Also, 15 potential lincRNA-miRNA-mRNA combinations have been identified which may be potential biomarkers.

Conclusions

This study identified differentially expressed markers between normal and cancer-associated fibroblasts that would help in targeted therapy against CAFs/derived factors, in combination with conventional therapy. However, this would in future need more experimental validation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-024-13006-x.

Keywords: Tumor microenvironment, Cancer-associated fibroblasts, Chemoresistance, Non-coding RNA, LINCRNA, Prostate cancer

Introduction

The tumor microenvironment (TME) is a complex and dynamic ecosystem, which is shaped by the interactions between the tumor cells and the non-cancerous cells as well as the extra-cellular matrix surrounding the tumor. Although the framework and composition of the TME may vary according to the tumor type, some hallmarks of the TME remain the same. The TME is comprised of a variety of cell-types such as immune cells (T cells, B cells, Tregs, Neutrophils, Macrophages, Dendritic cells, Natural Killer cells, Mast cells etc.) cancer-associated fibroblasts (CAFs), endothelial cells (ECs), pericytes, adipocytes and neurons (Reviewed in [1]and references therin). The acellular components of the TME includes mostly secreted factors such as growth factors, cytokines, extracellular matrix (ECM) proteins, and metabolites [2]. The dynamic and reciprocal interactions between the tumor and its microenvironment influence cancer cell survival, local invasion, metastasis [3, 4], immune surveillance, angiogenesis [5] as well as response to therapy [6, 7].

One of the major cell types in the TME is cancer-associated fibroblasts. CAFs are a major source of growth factors, cytokines, and other signaling molecules, which impact cancer cell behavior [6]. When subjected to chemotherapy, along with the cancer cells, the CAFs also are subjected to changes. These therapies are likely to stimulate CAFs to release factors that could influence the stemness, metabolic status, signaling cascades, etc. within the tumor, which can prevent cancer cell eradication and perhaps cause recurrence [8]. In prostate cancer models, it has been observed that there is therapy induced activation of Wnt signal which can result in drug resistance [9, 10]. In pancreatic cancer, CAFs protect cancer cells from gemcitabine-induced cell death by activating NF-κB through IL-1β and IL‐1 receptor‐associated kinase 4 (IRAK4) [11]. In ESCC, cisplatin resistance is conferred by IL‐6/CXCR7 axis, where IL-6 is mainly secreted by the CAFs [12]. Transcriptome and proteome analysis from different models have emphasized the role of interleukins secreted by CAFs on conferring therapy resistance in various models (reviewed in [8]).

Cancer stem cells (CSC) have multiple mechanisms to overcome chemotherapy and TME has a significant influence in maintenance of CSCs. IL-17, secreted by CAFs and TGF-b signaling in CAFs have shown to influence the stemness of the CSCs [13, 14]. Besides, SHH signaling and its interaction with HIF-1a are observed to enhance the CSC properties [15, 16]. In a breast cancer model, CAF-derived miR-221 activated an ER^low/Notch^high feed-forward loop responsible for the generation of CD133^high CSCs [17]. Multiple studies have shown that CAF derived Interleukins, Wnt as well as ncRNAs influence the CSC population and hence therapeutic resistance (reviewed in [8]).

Metabolic changes in CAFs have received significant attention over the last few years. PI3K/AKT pathway in cancer cells, has been observed to induce Warburg effect in CAFs through cytoplasmic translocation of the nuclear G-protein‐coupled estrogen receptor (GPER) and aberrant activation of the GPER signaling pathway. CAFs in turn deliver lactate transporters to cancer cells, resulting in a coupled energy metabolism process that can increase drug resistance [18]. These and other metabolic changes need to be explored further for their role in conferring resistance.

CAFs are pivotal in driving cancer progression through their involvement in processes such as extracellular matrix (ECM) deposition and remodeling, extensive communication with cancer cells, promoting epithelial-to-mesenchymal transition (EMT), facilitating invasion, metastasis, and even contributing to therapy resistance [19]. CAFs are also recognized for their involvement in developing resistance to anti-cancer therapy by providing a protective environment for tumor cells. There exists a symbiotic relationship between tumor cells and CAFs, wherein CAFs provide the necessary resources for tumor cell growth and survival, thereby contributing to the development of a chemoresistant phenotype [20]. Considering the pleiotropic effects of the tumor microenvironment, particularly CAFs, an insight into the specific factors responsible for therapeutic resistance can potentially pave the way for newer and more effective strategies for treatment. The major hurdle in this direction is the lack of distinguishing biomarkers for CAFs that would allow for their exclusive targeting. There is a high heterogeneity of CAF functions- both pro-tumorigenic and anti-tumorigenic within the same tumor [21]. Hence targeting the CAFs/derived factors has to be done with extreme caution to avoid adverse effects. Additionally, targeting CAFs might lead to significant clinical benefits, as pro-tumorigenic CAFs can support tumor progression, but they may not be indispensable for tumor growth and survival. In other words, tumor cells may not solely depend on the presence of CAFs. CAF-targeted therapy in combination with other chemotherapeutic drugs is likely to improve treatment outcomes.

Our study has used a transcriptome analysis to identify differentially expressed genes and non-coding RNAs between normal and cancer-associated fibroblasts on a human prostate model.

Materials and methods

Clinical specimen collection

All patient samples were collected from the Institute of Nephro-Urology (Department of Urology), Bengaluru. The study was approved by the Institutional Ethics committee of both the institutions and informed consent has been taken from all the participants. The identity of the participants has been kept anonymous. Samples were taken either by Transrectal Ultrasound scan (TRUS) or Transurethral resection of the prostate (TURP) methods. Classification of the samples as benign or malignant was done by pathologists as per standard criteria.

Culturing of fibroblasts

Surgical or biopsy specimens were rinsed thoroughly in sterile saline and transferred to transport media (RPMI 1640 (Gibco, Cat No: 23400-021) with 2X PenStrep (Gibco, Cat No: 15140122). Subsequently, these specimens were rinsed thoroughly with RPMI media containing antibiotics, minced into fine pieces, and transferred to culture flasks keeping sufficient distance between each piece for the cells to migrate out. Media was changed periodically. Once the fibroblasts migrated out of the tissues, cells were transferred to fresh flasks and cultured in RPMI supplemented with FBS.

RNA isolation

Total RNA was isolated from the cultured fibroblasts using a Qiagen RNeasy kit (Cat No: 74104) according to the manufacturer’s instructions. RNA was quantified on nanodrop (Thermo Scientific™ NanoDrop™). The quality of RNA samples was assessed by running them on 1% agarose gel.

RNA sequencing

RNA sequencing was outsourced to Wipro Life Science Labs, Bengaluru. Quality assessment was done using Agilent TapeStation and all samples had RIN > 9 (Supplemenatry file). Samples were further taken for library preparation and RNA sequencing using the Illumina platform (NextSeq 2000). (The raw data files for this data set is available on GEO, accession number GSE270705).

RNA sequencing analysis workflow

The quality assessment of the data was performed for base quality and contamination by sequencing artifacts. The adapters were trimmed and poor-quality sequences were filtered using Trim Galore. Trimmed sequence reads were mapped to reference genome (Assembly: hg38, GRCh38.p12 (GCA_000001405.27), Dec. 2017, Data Source: UCSC Genome Browser, Weblink:http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/analysisSet/hg38.analysisSet.fa.gz) with splice aware alignment tool STAR. R subread R package was used to get feature-specific expression counts. Low-count features across the samples were detected and removed using the NOISeq R package followed by expression count normalization with the TMM method (from the NOISeq R package). Differential expression analysis was performed with the NOISeq R package where the group information was used to define biological replicates. Genes/transcripts (mRNA and lincRNA) were considered differentially expressed when they showed at least a log2 fold change of 1.5 between normal and CAFs (Supplementary Fig. 1).

qRT-PCR analysis

2 µg RNA was converted to cDNA using Verso cDNA synthesis kit (Thermo Fisher Scientific Cat no: AB1453A). 20ng RNA equivalent cDNA was used for the PCR reactions. qRT-PCR was carried out using the SYBR FAST Universal 2XqPCR Master Mix, (Roche, Cat no: KK4601). Normalization was done using RPL35. The fold changes with respect to expression level in control samples was calculated by the ddCt method. The experiment was done on 3 control samples and 2 CAF samples and the average fold change with SD has been tabulated.

LINCRNA functional annotation

NPInter v5.0 (http://bigdata.ibp.ac.cn/npinter5/) [22, 23] is a database that provides collective information about the multidimensional interactions of ncRNAs (lincRNA, miRNA, circRNA, etc.) with protein, RNA, and DNA. This database contains information about RNA interactions based on literature mining and high-throughput sequencing data with functional annotation [22].

The list of differentially expressed lincRNAs from our experiment was fed into NPInter v5.0 database and segregated according to the interactors (proteins, mRNA, and ncRNA).

The miRNAs obtained from the lincRNA-miRNA (ncRNA) interaction (from NPInter v5.0 database) were further subjected to the miRDB database (https://mirdb.org/) [24] to predict its mRNA targets (Supplementary Fig. 2).

Results

Differential expression of genes between normal and cancer-associated fibroblasts

We have identified 818 genes and 17 long intergenic non-coding RNAs (lincRNAs) that exhibit differential expression between normal fibroblasts and CAFs with a minimum log2 fold of 1.5. Of these, 380 genes and 7 lincRNAs were found to be overexpressed (Table 1), while 438 genes and 10 lincRNAs were under-expressed (Table 2) in CAFs as compared to normal fibroblasts.

Table 1.

List of overexpressed mRNAs and LINCRNAs in cancer-associated fibroblasts compared to normal fibroblasts

Sl No	Gene ID	Log2FC	Symbol
1	ENSG00000184937	9.338678226	WT1
2	ENSG00000120093	9.066529369	HOXB3
3	ENSG00000182742	8.440665024	HOXB4
4	ENSG00000106483	7.457728693	SFRP4
5	ENSG00000183242	7.266975633	WT1-AS
6	ENSG00000165507	6.8004577	DEPP1
7	ENSG00000163364	6.193836171	LINC01116
8	ENSG00000175879	5.293002219	HOXD8
9	ENSG00000156466	5.080737946	GDF6
10	ENSG00000169418	4.968358544	NPR1
11	ENSG00000188783	4.644111631	PRELP
12	ENSG00000198774	4.609020395	RASSF9
13	ENSG00000131471	4.565470197	AOC3
14	ENSG00000106819	4.519034135	ASPN
15	ENSG00000075275	4.434781935	CELSR1
16	ENSG00000196616	4.350248405	ADH1B
17	ENSG00000146374	4.301235355	RSPO3
18	ENSG00000146038	4.29610124	DCDC2
19	ENSG00000112936	4.254281304	C7
20	ENSG00000244694	4.169584725	PTCHD4
21	ENSG00000135914	4.126018242	HTR2B
22	ENSG00000136235	4.080788268	GPNMB
23	ENSG00000005471	3.893427681	ABCB4
24	ENSG00000177363	3.886142424	LRRN4CL
25	ENSG00000086289	3.817819494	EPDR1
26	ENSG00000225684	3.807004332	FAM225B
27	ENSG00000146938	3.720873944	NLGN4X
28	ENSG00000110076	3.67301846	NRXN2
29	ENSG00000205221	3.625265335	VIT
30	ENSG00000139910	3.612874671	NOVA1
31	ENSG00000137507	3.593886089	LRRC32
32	ENSG00000167306	3.581243704	MYO5B
33	ENSG00000164161	3.576706765	HHIP
34	ENSG00000164318	3.569735487	EGFLAM
35	ENSG00000231528	3.492219584	FAM225A
36	ENSG00000172264	3.490561901	MACROD2
37	ENSG00000180777	3.487256802	ANKRD30B
38	ENSG00000138449	3.435323115	SLC40A1
39	ENSG00000131370	3.430490091	SH3BP5
40	ENSG00000173917	3.379890194	HOXB2
41	ENSG00000100302	3.374791795	RASD2
42	ENSG00000157214	3.373774391	STEAP2
43	ENSG00000260552	3.332515698	COSMOC
44	ENSG00000248144	3.324284207	ADH1C
45	ENSG00000072041	3.304587859	SLC6A15
46	ENSG00000138135	3.286841035	CH25H
47	ENSG00000183682	3.268320942	BMP8A
48	ENSG00000096696	3.242335703	DSP
49	ENSG00000145242	3.231906415	EPHA5
50	ENSG00000078081	3.222529548	LAMP3
51	ENSG00000101680	3.21461969	LAMA1
52	ENSG00000166923	3.181087552	GREM1
53	ENSG00000171119	3.178048407	NRTN
54	ENSG00000115252	3.1689023	PDE1A
55	ENSG00000129467	3.164502988	ADCY4
56	ENSG00000145819	3.148256737	ARHGAP26
57	ENSG00000106809	3.147514635	OGN
58	ENSG00000055732	3.103481133	MCOLN3
59	ENSG00000135643	3.087942208	KCNMB4
60	ENSG00000065320	3.080524368	NTN1
61	ENSG00000070193	3.076239219	FGF10
62	ENSG00000267414	3.070509607	SETBP1-DT
63	ENSG00000074370	3.024375298	ATP2A3
64	ENSG00000152217	3.010426863	SETBP1
65	ENSG00000179954	2.984447115	SSC5D
66	ENSG00000016082	2.979521208	ISL1
67	ENSG00000136040	2.979012692	PLXNC1
68	ENSG00000235092	2.971718084	ID2-AS1
69	ENSG00000163794	2.951338436	UCN
70	ENSG00000244242	2.945313304	IFITM10
71	ENSG00000112096	2.940026071	SOD2
72	ENSG00000121005	2.931365251	CRISPLD1
73	ENSG00000139364	2.918854199	TMEM132B
74	ENSG00000164106	2.915361292	SCRG1
75	ENSG00000197971	2.87570475	MBP
76	ENSG00000231007	2.875103234	CDC20P1
77	ENSG00000106624	2.854540897	AEBP1
78	ENSG00000101115	2.849787273	SALL4
79	ENSG00000006210	2.847602797	CX3CL1
80	ENSG00000168427	2.808700829	KLHL30
81	ENSG00000182463	2.807335587	TSHZ2
82	ENSG00000189409	2.805577626	MMP23B
83	ENSG00000117122	2.802217897	MFAP2
84	ENSG00000165959	2.758592315	CLMN
85	ENSG00000131634	2.75181617	TMEM204
86	ENSG00000168477	2.742397705	TNXB
87	ENSG00000248290	2.724225106	TNXA
88	ENSG00000215914	2.718914487	MMP23A
89	ENSG00000186868	2.711375311	MAPT
90	ENSG00000170345	2.692482283	FOS
91	ENSG00000154258	2.690792336	ABCA9
92	ENSG00000180481	2.678866617	GLIPR1L2
93	ENSG00000133083	2.661308127	DCLK1
94	ENSG00000082482	2.659723066	KCNK2
95	ENSG00000048052	2.658813636	HDAC9
96	ENSG00000162551	2.644404231	ALPL
97	ENSG00000173805	2.640886128	HAP1
98	ENSG00000189056	2.639778484	RELN
99	ENSG00000253661	2.63880641	ZFHX4-AS1
100	ENSG00000171791	2.610282773	BCL2
101	ENSG00000144837	2.601860367	PLA1A
102	ENSG00000169184	2.599944099	MN1
103	ENSG00000089820	2.574305154	ARHGAP4
104	ENSG00000181634	2.56427507	TNFSF15
105	ENSG00000178015	2.560445983	GPR150
106	ENSG00000167216	2.537221974	KATNAL2
107	ENSG00000028277	2.532129932	POU2F2
108	ENSG00000178081	2.529858375	ULK4P3
109	ENSG00000004776	2.527119447	HSPB6
110	ENSG00000116675	2.523980391	DNAJC6
111	ENSG00000156804	2.52126043	FBXO32
112	ENSG00000166664	2.502047488	CHRFAM7A
113	ENSG00000184292	2.4903466	TACSTD2
114	ENSG00000137942	2.486706943	FNBP1L
115	ENSG00000163132	2.461845915	MSX1
116	ENSG00000230148	2.455525439	HOXB-AS1
117	ENSG00000079931	2.449156028	MOXD1
118	ENSG00000187479	2.449071496	C11orf96
119	ENSG00000172348	2.448876698	RCAN2
120	ENSG00000092929	2.445627941	UNC13D
121	ENSG00000186340	2.420950706	THBS2
122	ENSG00000185567	2.416942281	AHNAK2
123	ENSG00000143387	2.414727514	CTSK
124	ENSG00000165124	2.407244712	SVEP1
125	ENSG00000182379	2.402168936	NXPH4
126	ENSG00000123700	2.399277025	KCNJ2
127	ENSG00000268883	2.391696562	PNMA6B
128	ENSG00000135929	2.390048724	CYP27A1
129	ENSG00000166592	2.388259231	RRAD
130	ENSG00000180155	2.384488858	LYNX1
131	ENSG00000099960	2.384307936	SLC7A4
132	ENSG00000160013	2.37946615	PTGIR
133	ENSG00000152049	2.371373176	KCNE4
134	ENSG00000117586	2.367762203	TNFSF4
135	ENSG00000188112	2.366857463	C6orf132
136	ENSG00000125246	2.366405093	CLYBL
137	ENSG00000170873	2.365388741	MTSS1
138	ENSG00000185250	2.364877963	PPIL6
139	ENSG00000184985	2.360497315	SORCS2
140	ENSG00000053524	2.358290643	MCF2L2
141	ENSG00000064655	2.356374576	EYA2
142	ENSG00000166341	2.353827343	DCHS1
143	ENSG00000139117	2.331754284	CPNE8
144	ENSG00000164647	2.331093887	STEAP1
145	ENSG00000121898	2.329546823	CPXM2
146	ENSG00000184349	2.326907614	EFNA5
147	ENSG00000155629	2.319366832	PIK3AP1
148	ENSG00000153094	2.313590878	BCL2L11
149	ENSG00000073849	2.312193881	ST6GAL1
150	ENSG00000145911	2.307535495	N4BP3
151	ENSG00000179104	2.306651777	TMTC2
152	ENSG00000133110	2.304364639	POSTN
153	ENSG00000011201	2.300441534	ANOS1
154	ENSG00000027075	2.299717173	PRKCH
155	ENSG00000085563	2.297166128	ABCB1
156	ENSG00000150907	2.296836959	FOXO1
157	ENSG00000138028	2.295512338	CGREF1
158	ENSG00000122877	2.289440613	EGR2
159	ENSG00000182175	2.28805904	RGMA
160	ENSG00000270441	2.286773813	LAMB2P1
161	ENSG00000116396	2.282150715	KCNC4
162	ENSG00000136960	2.266626397	ENPP2
163	ENSG00000248587	2.262386263	GDNF-AS1
164	ENSG00000185345	2.258116067	PRKN
165	ENSG00000148426	2.256779282	PROSER2
166	ENSG00000061918	2.247439143	GUCY1B1
167	ENSG00000092068	2.24098848	SLC7A8
168	ENSG00000275395	2.234570285	FCGBP
169	ENSG00000151490	2.20727056	PTPRO
170	ENSG00000139597	2.207034399	N4BP2L1
171	ENSG00000247157	2.204595176	LINC01252
172	ENSG00000138646	2.203203888	HERC5
173	ENSG00000172403	2.201543978	SYNPO2
174	ENSG00000136404	2.191272623	TM6SF1
175	ENSG00000146555	2.18336527	SDK1
176	ENSG00000168621	2.178626681	GDNF
177	ENSG00000105825	2.174426101	TFPI2
178	ENSG00000064309	2.174214004	CDON
179	ENSG00000140945	2.169223331	CDH13
180	ENSG00000109610	2.157112704	SOD3
181	ENSG00000164742	2.156264764	ADCY1
182	ENSG00000141837	2.154368705	CACNA1A
183	ENSG00000146950	2.149426889	SHROOM2
184	ENSG00000204967	2.149380077	PCDHA4
185	ENSG00000115457	2.148909168	IGFBP2
186	ENSG00000060718	2.147967361	COL11A1
187	ENSG00000175344	2.146649503	CHRNA7
188	ENSG00000121316	2.129055023	PLBD1
189	ENSG00000170775	2.127857405	GPR37
190	ENSG00000176723	2.122935457	ZNF843
191	ENSG00000132561	2.117237938	MATN2
192	ENSG00000132554	2.115887216	RGS22
193	ENSG00000223485	2.113157959	LINC01615
194	ENSG00000162817	2.110368888	C1orf115
195	ENSG00000088881	2.109274054	EBF4
196	ENSG00000188372	2.105592321	ZP3
197	ENSG00000074181	2.102490156	NOTCH3
198	ENSG00000235961	2.101952544	PNMA6A
199	ENSG00000167992	2.093637143	VWCE
200	ENSG00000138650	2.089573305	PCDH10
201	ENSG00000138759	2.085680757	FRAS1
202	ENSG00000197461	2.081122778	PDGFA
203	ENSG00000266714	2.075813496	MYO15B
204	ENSG00000238103	2.074829611	RPL9P7
205	ENSG00000077157	2.074613986	PPP1R12B
206	ENSG00000102032	2.07383953	RENBP
207	ENSG00000228903	2.069376405	RASA4CP
208	ENSG00000251396	2.068860492	LINC01301
209	ENSG00000151623	2.067577773	NR3C2
210	ENSG00000125510	2.053409396	OPRL1
211	ENSG00000245248	2.051615405	USP2-AS1
212	ENSG00000211445	2.051018294	GPX3
213	ENSG00000251141	2.046164026	MRPS30-DT
214	ENSG00000267365	2.045033739	KCNJ2-AS1
215	ENSG00000228221	2.04070205	LINC00578
216	ENSG00000203867	2.032344927	RBM20
217	ENSG00000174945	2.020272513	AMZ1
218	ENSG00000115604	2.009952757	IL18R1
219	ENSG00000135604	2.00924409	STX11
220	ENSG00000161835	1.989514328	TAMALIN
221	ENSG00000138678	1.980588605	GPAT3
222	ENSG00000099953	1.97665952	MMP11
223	ENSG00000136237	1.966980838	RAPGEF5
224	ENSG00000247317	1.961468975	LY6E-DT
225	ENSG00000091137	1.961199971	SLC26A4
226	ENSG00000133106	1.955593857	EPSTI1
227	ENSG00000067606	1.951176918	PRKCZ
228	ENSG00000188042	1.949712405	ARL4C
229	ENSG00000121769	1.946715273	FABP3
230	ENSG00000053328	1.946150549	METTL24
231	ENSG00000119227	1.944119988	PIGZ
232	ENSG00000242265	1.943038605	PEG10
233	ENSG00000158270	1.942012211	COLEC12
234	ENSG00000090339	1.939652022	ICAM1
235	ENSG00000120051	1.937608095	CFAP58
236	ENSG00000186564	1.934041687	FOXD2
237	ENSG00000170667	1.931915044	RASA4B
238	ENSG00000171812	1.928787106	COL8A2
239	ENSG00000171298	1.918638878	GAA
240	ENSG00000136244	1.914540053	IL6
241	ENSG00000123689	1.914379557	G0S2
242	ENSG00000116183	1.910021948	PAPPA2
243	ENSG00000070159	1.909849322	PTPN3
244	ENSG00000177989	1.907647864	ODF3B
245	ENSG00000105808	1.901189602	RASA4
246	ENSG00000143494	1.883805272	VASH2
247	ENSG00000124212	1.882029068	PTGIS
248	ENSG00000017427	1.881716853	IGF1
249	ENSG00000126709	1.877126382	IFI6
250	ENSG00000054938	1.87640479	CHRDL2
251	ENSG00000160781	1.876267747	PAQR6
252	ENSG00000185522	1.873518829	LMNTD2
253	ENSG00000184489	1.87301321	PTP4A3
254	ENSG00000164440	1.87150095	TXLNB
255	ENSG00000196220	1.870403189	SRGAP3
256	ENSG00000236609	1.86738356	ZNF853
257	ENSG00000124107	1.860586158	SLPI
258	ENSG00000213397	1.860217516	HAUS7
259	ENSG00000230453	1.859718833	ANKRD18B
260	ENSG00000111058	1.85627795	ACSS3
261	ENSG00000111728	1.854599204	ST8SIA1
262	ENSG00000167191	1.851559449	GPRC5B
263	ENSG00000154262	1.838953254	ABCA6
264	ENSG00000236404	1.838660652	VLDLR-AS1
265	ENSG00000205464	1.83481979	ATP6AP1L
266	ENSG00000173890	1.833899338	GPR160
267	ENSG00000163393	1.831146569	SLC22A15
268	ENSG00000083067	1.830961882	TRPM3
269	ENSG00000183160	1.826191137	TMEM119
270	ENSG00000152518	1.825645378	ZFP36L2
271	ENSG00000151322	1.824665719	NPAS3
272	ENSG00000087076	1.823705208	HSD17B14
273	ENSG00000198947	1.818442888	DMD
274	ENSG00000103485	1.818412324	QPRT
275	ENSG00000079337	1.817533512	RAPGEF3
276	ENSG00000261087	1.817317143	ZNNT1
277	ENSG00000137809	1.814823464	ITGA11
278	ENSG00000242759	1.812015961	LINC00882
279	ENSG00000158321	1.784811351	AUTS2
280	ENSG00000104856	1.780951678	RELB
281	ENSG00000167994	1.778158789	RAB3IL1
282	ENSG00000166482	1.777896815	MFAP4
283	ENSG00000165379	1.769235888	LRFN5
284	ENSG00000138829	1.768396718	FBN2
285	ENSG00000091536	1.763382884	MYO15A
286	ENSG00000261247	1.760534831	GOLGA8T
287	ENSG00000159871	1.753151378	LYPD5
288	ENSG00000258057	1.750339825	BCDIN3D-AS1
289	ENSG00000141337	1.749006586	ARSG
290	ENSG00000103742	1.745113925	IGDCC4
291	ENSG00000145675	1.74040115	PIK3R1
292	ENSG00000122176	1.737285546	FMOD
293	ENSG00000254109	1.735471082	RBPMS-AS1
294	ENSG00000204991	1.726456592	SPIRE2
295	ENSG00000112149	1.723550217	CD83
296	ENSG00000137573	1.718910062	SULF1
297	ENSG00000102385	1.71638324	DRP2
298	ENSG00000123358	1.711240172	NR4A1
299	ENSG00000136231	1.701661804	IGF2BP3
300	ENSG00000196189	1.698833406	SEMA4A
301	ENSG00000235169	1.698395404	SMIM1
302	ENSG00000129951	1.697726994	PLPPR3
303	ENSG00000121577	1.696736786	POPDC2
304	ENSG00000136048	1.696574651	DRAM1
305	ENSG00000155093	1.695863189	PTPRN2
306	ENSG00000146021	1.690145464	KLHL3
307	ENSG00000138606	1.685675264	SHF
308	ENSG00000163071	1.683331706	SPATA18
309	ENSG00000182667	1.681535708	NTM
310	ENSG00000175147	1.675397396	TMEM51-AS1
311	ENSG00000131094	1.671463514	C1QL1
312	ENSG00000185950	1.671407837	IRS2
313	ENSG00000147813	1.667824825	NAPRT
314	ENSG00000248932	1.66420747	COPB2-DT
315	ENSG00000214530	1.663364842	STARD10
316	ENSG00000185551	1.661064554	NR2F2
317	ENSG00000101447	1.657930905	FAM83D
318	ENSG00000010810	1.655154361	FYN
319	ENSG00000188613	1.654322702	NANOS1
320	ENSG00000266405	1.652390581	CBX3P2
321	ENSG00000135709	1.651900323	KIAA0513
322	ENSG00000105696	1.648631374	TMEM59L
323	ENSG00000130203	1.645917049	APOE
324	ENSG00000196843	1.633503171	ARID5A
325	ENSG00000105227	1.63140042	PRX
326	ENSG00000149131	1.627990138	SERPING1
327	ENSG00000257556	1.627356063	LINC02298
328	ENSG00000154721	1.625716346	JAM2
329	ENSG00000099822	1.623752672	HCN2
330	ENSG00000184500	1.62244634	PROS1
331	ENSG00000196972	1.618142282	SMIM10L2B
332	ENSG00000151468	1.61503098	CCDC3
333	ENSG00000105464	1.614595823	GRIN2D
334	ENSG00000231160	1.612536565	KLF3-AS1
335	ENSG00000137103	1.607356621	TMEM8B
336	ENSG00000152804	1.606407263	HHEX
337	ENSG00000177406	1.604754837	NINJ2-AS1
338	ENSG00000137193	1.596972091	PIM1
339	ENSG00000064763	1.59577321	FAR2
340	ENSG00000167617	1.593511925	CDC42EP5
341	ENSG00000255052	1.592666461	FAM66D
342	ENSG00000228960	1.59238393	OR2A9P
343	ENSG00000162944	1.587186115	RFTN2
344	ENSG00000253537	1.584488009	PCDHGA7
345	ENSG00000184160	1.583566455	ADRA2C
346	ENSG00000227825	1.582312593	SLC9A7P1
347	ENSG00000130304	1.576658392	SLC27A1
348	ENSG00000182575	1.573523412	NXPH3
349	ENSG00000111961	1.571843412	SASH1
350	ENSG00000105327	1.571154429	BBC3
351	ENSG00000168405	1.570501551	CMAHP
352	ENSG00000091986	1.567946901	CCDC80
353	ENSG00000115257	1.567912566	PCSK4
354	ENSG00000130513	1.565481009	GDF15
355	ENSG00000165171	1.561243614	METTL27
356	ENSG00000105088	1.559271564	OLFM2
357	ENSG00000198885	1.55549775	ITPRIPL1
358	ENSG00000164542	1.547170796	KIAA0895
359	ENSG00000176658	1.545536167	MYO1D
360	ENSG00000105792	1.542664406	CFAP69
361	ENSG00000164099	1.54213594	PRSS12
362	ENSG00000166387	1.540636661	PPFIBP2
363	ENSG00000114698	1.535920603	PLSCR4
364	ENSG00000205978	1.532307659	NYNRIN
365	ENSG00000226278	1.530617518	PSPHP1
366	ENSG00000086991	1.528440753	NOX4
367	ENSG00000131831	1.526695735	RAI2
368	ENSG00000107562	1.52621975	CXCL12
369	ENSG00000141458	1.523071138	NPC1
370	ENSG00000172164	1.522391588	SNTB1
371	ENSG00000125965	1.521256956	GDF5
372	ENSG00000246985	1.520714773	SOCS2-AS1
373	ENSG00000147852	1.519022363	VLDLR
374	ENSG00000171877	1.517043034	FRMD5
375	ENSG00000116661	1.51618235	FBXO2
376	ENSG00000068831	1.515474863	RASGRP2
377	ENSG00000142156	1.514396814	COL6A1
378	ENSG00000233297	1.514139138	RASA4DP
379	ENSG00000064989	1.512822057	CALCRL
380	ENSG00000182218	1.512646505	HHIPL1
381	ENSG00000198270	1.512176831	TMEM116
382	ENSG00000143382	1.508260028	ADAMTSL4
383	ENSG00000104883	1.507314695	PEX11G
384	ENSG00000205309	1.507255711	NT5M
385	ENSG00000077942	1.506829199	FBLN1
386	ENSG00000246174	1.502096816	KCTD21-AS1
387	ENSG00000173262	1.501298345	SLC2A14

Open in a new tab

List of mRNAs and LINCRNAs overexpressed in cancer associated fibroblasts as compared to normal fibroblasts. The list shows the GeneID, relative fold change values (Log₂FC), and gene symbol

Table 2.

List of underexpressed mRNAs and LINCRNAs in cancer-associated fibroblasts compared to normal fibroblasts

Sl No	Gene ID	Log2FC	Symbol
1	ENSG00000164093	-6.244899052	PITX2
2	ENSG00000110693	-5.381564985	SOX6
3	ENSG00000171246	-4.942383728	NPTX1
4	ENSG00000206432	-4.86079173	TMEM200C
5	ENSG00000241213	-4.692994122	LINC02024
6	ENSG00000189057	-4.682640569	FAM111B
7	ENSG00000186493	-4.5887501	C5orf38
8	ENSG00000170561	-4.489265562	IRX2
9	ENSG00000065328	-4.416019329	MCM10
10	ENSG00000213412	-4.299519526	HNRNPA1P33
11	ENSG00000277775	-4.280564426	H3C7
12	ENSG00000102755	-4.232129614	FLT1
13	ENSG00000171848	-4.211927711	RRM2
14	ENSG00000117525	-4.152266428	F3
15	ENSG00000129173	-4.071649074	E2F8
16	ENSG00000093009	-3.993665744	CDC45
17	ENSG00000111816	-3.972942656	FRK
18	ENSG00000148773	-3.972536202	MKI67
19	ENSG00000174371	-3.960698368	EXO1
20	ENSG00000109805	-3.956700161	NCAPG
21	ENSG00000007968	-3.919560667	E2F2
22	ENSG00000176049	-3.896811197	JAKMIP2
23	ENSG00000109272	-3.8826734	PF4V1
24	ENSG00000169607	-3.856035805	CKAP2L
25	ENSG00000072571	-3.853814872	HMMR
26	ENSG00000151150	-3.845533978	ANK3
27	ENSG00000131153	-3.815504744	GINS2
28	ENSG00000137812	-3.757294903	KNL1
29	ENSG00000122952	-3.742224789	ZWINT
30	ENSG00000166803	-3.696740654	PCLAF
31	ENSG00000286522	-3.665033009	H3C2
32	ENSG00000203811	-3.649365262	H3C14
33	ENSG00000203852	-3.645437574	H3C15
34	ENSG00000092853	-3.613728714	CLSPN
35	ENSG00000112984	-3.590191849	KIF20A
36	ENSG00000171241	-3.583589781	SHCBP1
37	ENSG00000121152	-3.583173772	NCAPH
38	ENSG00000143476	-3.558596552	DTL
39	ENSG00000152936	-3.557494062	LMNTD1
40	ENSG00000165244	-3.557493273	ZNF367
41	ENSG00000165490	-3.538556887	DDIAS
42	ENSG00000237649	-3.53147714	KIFC1
43	ENSG00000278048	-3.530960644	U2
44	ENSG00000158402	-3.511625284	CDC25C
45	ENSG00000105011	-3.506471129	ASF1B
46	ENSG00000140534	-3.501930836	TICRR
47	ENSG00000138180	-3.497178172	CEP55
48	ENSG00000134690	-3.487940972	CDCA8
49	ENSG00000118193	-3.487807903	KIF14
50	ENSG00000163638	-3.463794075	ADAMTS9
51	ENSG00000117724	-3.460261143	CENPF
52	ENSG00000183598	-3.4355466	H3C13
53	ENSG00000075218	-3.43277683	GTSE1
54	ENSG00000166851	-3.413367724	PLK1
55	ENSG00000123485	-3.395070088	HJURP
56	ENSG00000011426	-3.392069201	ANLN
57	ENSG00000090889	-3.389812639	KIF4A
58	ENSG00000085840	-3.38031696	ORC1
59	ENSG00000163808	-3.374288656	KIF15
60	ENSG00000138185	-3.373426081	ENTPD1
61	ENSG00000142945	-3.330213652	KIF2C
62	ENSG00000238297	-3.323221421	U3
63	ENSG00000068078	-3.315141798	FGFR3
64	ENSG00000117399	-3.31378088	CDC20
65	ENSG00000112742	-3.310189708	TTK
66	ENSG00000170312	-3.308358731	CDK1
67	ENSG00000171320	-3.306520946	ESCO2
68	ENSG00000146670	-3.297527548	CDCA5
69	ENSG00000145386	-3.282458069	CCNA2
70	ENSG00000166670	-3.278837829	MMP10
71	ENSG00000197565	-3.264304177	COL4A6
72	ENSG00000089685	-3.2634763	BIRC5
73	ENSG00000168078	-3.251635481	PBK
74	ENSG00000183856	-3.236451775	IQGAP3
75	ENSG00000145681	-3.235820469	HAPLN1
76	ENSG00000109072	-3.229022684	VTN
77	ENSG00000273703	-3.228440353	H2BC14
78	ENSG00000066279	-3.224990555	ASPM
79	ENSG00000184357	-3.215486063	H1-5
80	ENSG00000197299	-3.203543875	BLM
81	ENSG00000100162	-3.202097229	CENPM
82	ENSG00000175305	-3.179881376	CCNE2
83	ENSG00000075702	-3.177596664	WDR62
84	ENSG00000011332	-3.169273735	DPF1
85	ENSG00000196584	-3.158286898	XRCC2
86	ENSG00000276368	-3.147207462	H2AC14
87	ENSG00000134057	-3.146004943	CCNB1
88	ENSG00000173320	-3.143419433	STOX2
89	ENSG00000213967	-3.137208731	ZNF726
90	ENSG00000094804	-3.131644437	CDC6
91	ENSG00000228065	-3.120855365	LINC01515
92	ENSG00000051341	-3.118355075	POLQ
93	ENSG00000128713	-3.100005442	HOXD11
94	ENSG00000131747	-3.09069934	TOP2A
95	ENSG00000150551	-3.082272486	LYPD1
96	ENSG00000184661	-3.067972849	CDCA2
97	ENSG00000157456	-3.063797851	CCNB2
98	ENSG00000281344	-3.063460876	HELLPAR
99	ENSG00000142731	-3.063276977	PLK4
100	ENSG00000111665	-3.060335172	CDCA3
101	ENSG00000087586	-3.042747952	AURKA
102	ENSG00000055813	-3.03369872	CCDC85A
103	ENSG00000164303	-3.027403057	ENPP6
104	ENSG00000058866	-3.027090567	DGKG
105	ENSG00000275713	-3.024102577	H2BC9
106	ENSG00000151725	-3.020026322	CENPU
107	ENSG00000196747	-3.007422105	H2AC13
108	ENSG00000276043	-3.005041466	UHRF1
109	ENSG00000166292	-2.99208271	TMEM100
110	ENSG00000241322	-2.984502753	CDRT1
111	ENSG00000111247	-2.983780538	RAD51AP1
112	ENSG00000129195	-2.977832945	PIMREG
113	ENSG00000135451	-2.970340448	TROAP
114	ENSG00000120149	-2.970262942	MSX2
115	ENSG00000164045	-2.958067112	CDC25A
116	ENSG00000076382	-2.950383677	SPAG5
117	ENSG00000009694	-2.94033935	TENM1
118	ENSG00000170160	-2.934268013	CCDC144A
119	ENSG00000287080	-2.929684086	H3C3
120	ENSG00000169679	-2.911200736	BUB1
121	ENSG00000227145	-2.902745099	IL21-AS1
122	ENSG00000127423	-2.89847034	AUNIP
123	ENSG00000101412	-2.892510405	E2F1
124	ENSG00000146410	-2.866283497	MTFR2
125	ENSG00000154920	-2.857083834	EME1
126	ENSG00000068489	-2.845006407	PRR11
127	ENSG00000274641	-2.842803264	H2BC17
128	ENSG00000183850	-2.818220455	ZNF730
129	ENSG00000276410	-2.814513008	H2BC3
130	ENSG00000261618	-2.811451265	LINC02605
131	ENSG00000013810	-2.805846132	TACC3
132	ENSG00000285294	-2.799537368	LINC00842
133	ENSG00000100583	-2.789283935	SAMD15
134	ENSG00000103522	-2.776203635	IL21R
135	ENSG00000163293	-2.761734242	NIPAL1
136	ENSG00000138778	-2.749900931	CENPE
137	ENSG00000185008	-2.739539975	ROBO2
138	ENSG00000164379	-2.732731209	FOXQ1
139	ENSG00000167513	-2.728991813	CDT1
140	ENSG00000137310	-2.728793599	TCF19
141	ENSG00000144278	-2.719986041	GALNT13
142	ENSG00000277224	-2.717203571	H2BC7
143	ENSG00000111206	-2.706187922	FOXM1
144	ENSG00000128656	-2.701917579	CHN1
145	ENSG00000240809	-2.637140463	CAP1P1
146	ENSG00000265190	-2.628615159	ANXA8
147	ENSG00000102384	-2.627033976	CENPI
148	ENSG00000113368	-2.621078132	LMNB1
149	ENSG00000276903	-2.601525749	H2AC16
150	ENSG00000226953	-2.595177536	NCKAP5-AS2
151	ENSG00000110900	-2.590720608	TSPAN11
152	ENSG00000197385	-2.580835912	ZNF860
153	ENSG00000180875	-2.579367873	GREM2
154	ENSG00000122966	-2.568012417	CIT
155	ENSG00000138669	-2.560074549	PRKG2
156	ENSG00000167900	-2.558677147	TK1
157	ENSG00000138160	-2.553197127	KIF11
158	ENSG00000134516	-2.552103309	DOCK2
159	ENSG00000144554	-2.55141711	FANCD2
160	ENSG00000275126	-2.55107583	H4C13
161	ENSG00000160223	-2.533341384	ICOSLG
162	ENSG00000165084	-2.532201212	C8orf34
163	ENSG00000222898	-2.531267474	RN7SKP97
164	ENSG00000071539	-2.527860312	TRIP13
165	ENSG00000162062	-2.52734652	TEDC2
166	ENSG00000117600	-2.513999972	PLPPR4
167	ENSG00000138182	-2.505030881	KIF20B
168	ENSG00000080986	-2.502549503	NDC80
169	ENSG00000176208	-2.49026074	ATAD5
170	ENSG00000165891	-2.482123055	E2F7
171	ENSG00000186638	-2.46529789	KIF24
172	ENSG00000088756	-2.458646519	ARHGAP28
173	ENSG00000215784	-2.451244682	FAM72D
174	ENSG00000179750	-2.433420816	APOBEC3B
175	ENSG00000186310	-2.432964986	NAP1L3
176	ENSG00000162383	-2.431717139	SLC1A7
177	ENSG00000167600	-2.424938393	CYP2S1
178	ENSG00000154839	-2.424822921	SKA1
179	ENSG00000101003	-2.422973689	GINS1
180	ENSG00000263513	-2.413532558	FAM72C
181	ENSG00000164109	-2.410394716	MAD2L1
182	ENSG00000184374	-2.406516904	COLEC10
183	ENSG00000088325	-2.405096136	TPX2
184	ENSG00000275591	-2.397269203	XKR5
185	ENSG00000264230	-2.396476922	ANXA8L1
186	ENSG00000109674	-2.383348062	NEIL3
187	ENSG00000124882	-2.381427676	EREG
188	ENSG00000123219	-2.373815709	CENPK
189	ENSG00000179219	-2.371344708	LINC00311
190	ENSG00000121904	-2.371312009	CSMD2
191	ENSG00000128052	-2.369999586	KDR
192	ENSG00000120594	-2.368417063	PLXDC2
193	ENSG00000230300	-2.366705146	STARD13-IT1
194	ENSG00000278588	-2.366083657	H2BC10
195	ENSG00000188662	-2.360625481	H1-9P
196	ENSG00000187741	-2.359151573	FANCA
197	ENSG00000162654	-2.358912627	GBP4
198	ENSG00000250853	-2.34035427	RNF138P1
199	ENSG00000248228	-2.324670799	SLIT2-IT1
200	ENSG00000135476	-2.32046519	ESPL1
201	ENSG00000185697	-2.316698718	MYBL1
202	ENSG00000171517	-2.316431745	LPAR3
203	ENSG00000166451	-2.308405325	CENPN
204	ENSG00000184571	-2.302100309	PIWIL3
205	ENSG00000179071	-2.298141854	CCDC89
206	ENSG00000161888	-2.296025131	SPC24
207	ENSG00000227911	-2.269397065	LINC02344
208	ENSG00000117461	-2.26639294	PIK3R3
209	ENSG00000187796	-2.266276673	CARD9
210	ENSG00000164850	-2.26346959	GPER1
211	ENSG00000100479	-2.263305406	POLE2
212	ENSG00000236532	-2.262159728	LINC01695
213	ENSG00000104368	-2.261503355	PLAT
214	ENSG00000112852	-2.255468306	PCDHB2
215	ENSG00000137807	-2.254277149	KIF23
216	ENSG00000144395	-2.252773294	CCDC150
217	ENSG00000152056	-2.244603112	AP1S3
218	ENSG00000183762	-2.234862927	KREMEN1
219	ENSG00000064042	-2.233728432	LIMCH1
220	ENSG00000100526	-2.226118872	CDKN3
221	ENSG00000207597	-2.215978624	MIR490
222	ENSG00000124635	-2.214462467	H2BC11
223	ENSG00000196081	-2.207624252	ZNF724
224	ENSG00000133119	-2.204999878	RFC3
225	ENSG00000012048	-2.203512532	BRCA1
226	ENSG00000278828	-2.197741828	H3C10
227	ENSG00000160957	-2.18914984	RECQL4
228	ENSG00000188610	-2.174967519	FAM72B
229	ENSG00000164087	-2.157773664	POC1A
230	ENSG00000147536	-2.154684112	GINS4
231	ENSG00000236824	-2.154204019	BCYRN1
232	ENSG00000073111	-2.152293569	MCM2
233	ENSG00000107984	-2.137106037	DKK1
234	ENSG00000272674	-2.122271421	PCDHB16
235	ENSG00000163535	-2.122050767	SGO2
236	ENSG00000187583	-2.103701739	PLEKHN1
237	ENSG00000149968	-2.090233185	MMP3
238	ENSG00000161800	-2.088807861	RACGAP1
239	ENSG00000162063	-2.083000456	CCNF
240	ENSG00000216819	-2.070905578	TUBB2BP1
241	ENSG00000234383	-2.070115996	CTBP2P8
242	ENSG00000164611	-2.062477973	PTTG1
243	ENSG00000169247	-2.060263012	SH3TC2
244	ENSG00000163507	-2.058945689	CIP2A
245	ENSG00000183763	-2.05764095	TRAIP
246	ENSG00000137135	-2.048019992	ARHGEF39
247	ENSG00000168496	-2.03889776	FEN1
248	ENSG00000204176	-2.034676644	SYT15
249	ENSG00000144476	-2.028952615	ACKR3
250	ENSG00000139618	-2.026828243	BRCA2
251	ENSG00000229989	-2.016615466	MIR181A1HG
252	ENSG00000248019	-2.004491042	FAM13A-AS1
253	ENSG00000004139	-2.003314083	SARM1
254	ENSG00000240891	-2.003055186	PLCXD2
255	ENSG00000274290	-1.998795168	H2BC6
256	ENSG00000198692	-1.99256031	EIF1AY
257	ENSG00000104147	-1.988069738	OIP5
258	ENSG00000274997	-1.984906449	H2AC12
259	ENSG00000137267	-1.984524033	TUBB2A
260	ENSG00000185760	-1.982236088	KCNQ5
261	ENSG00000247498	-1.981575492	GPRC5D-AS1
262	ENSG00000012504	-1.980799777	NR1H4
263	ENSG00000285077	-1.973914944	ARHGAP11B
264	ENSG00000135480	-1.973232661	KRT7
265	ENSG00000144596	-1.96748205	GRIP2
266	ENSG00000196787	-1.96740556	H2AC11
267	ENSG00000091651	-1.96729841	ORC6
268	ENSG00000131002	-1.967122587	TXLNGY
269	ENSG00000145861	-1.964234961	C1QTNF2
270	ENSG00000248909	-1.962396435	HMGB1P21
271	ENSG00000163491	-1.955677642	NEK10
272	ENSG00000196550	-1.950290978	FAM72A
273	ENSG00000136122	-1.950251078	BORA
274	ENSG00000002746	-1.94138728	HECW1
275	ENSG00000106537	-1.940201039	TSPAN13
276	ENSG00000112029	-1.93283442	FBXO5
277	ENSG00000051180	-1.932410512	RAD51
278	ENSG00000100297	-1.92766499	MCM5
279	ENSG00000231672	-1.922759998	DIRC3
280	ENSG00000281641	-1.919964204	SAMD12-AS1
281	ENSG00000079616	-1.912111909	KIF22
282	ENSG00000231566	-1.910488729	LINC02595
283	ENSG00000131470	-1.908189689	PSMC3IP
284	ENSG00000123473	-1.899760576	STIL
285	ENSG00000171408	-1.89840943	PDE7B
286	ENSG00000170624	-1.893532203	SGCD
287	ENSG00000214391	-1.88992187	TUBAP2
288	ENSG00000197275	-1.883502038	RAD54B
289	ENSG00000184988	-1.882777859	TMEM106A
290	ENSG00000111057	-1.871847275	KRT18
291	ENSG00000160229	-1.870820281	ZNF66
292	ENSG00000230417	-1.864206963	LINC00595
293	ENSG00000277075	-1.862953731	H2AC8
294	ENSG00000214826	-1.86155004	DDX12P
295	ENSG00000168675	-1.859533634	LDLRAD4
296	ENSG00000136492	-1.856840499	BRIP1
297	ENSG00000134007	-1.855147363	ADAM20
298	ENSG00000168389	-1.840331218	MFSD2A
299	ENSG00000124575	-1.839002223	H1-3
300	ENSG00000144583	-1.838682115	MARCHF4
301	ENSG00000040275	-1.83690946	SPDL1
302	ENSG00000258947	-1.835374336	TUBB3
303	ENSG00000274210	-1.834128746	RNVU1-27
304	ENSG00000164251	-1.833060737	F2RL1
305	ENSG00000077152	-1.828725777	UBE2T
306	ENSG00000100739	-1.828412728	BDKRB1
307	ENSG00000146006	-1.826278356	LRRTM2
308	ENSG00000149548	-1.825503232	CCDC15
309	ENSG00000157193	-1.817142483	LRP8
310	ENSG00000173894	-1.810408037	CBX2
311	ENSG00000137285	-1.808867329	TUBB2B
312	ENSG00000125637	-1.808350388	PSD4
313	ENSG00000158769	-1.806788653	F11R
314	ENSG00000091409	-1.797159103	ITGA6
315	ENSG00000122378	-1.795265991	PRXL2A
316	ENSG00000160949	-1.795003337	TONSL
317	ENSG00000259571	-1.794121641	BLID
318	ENSG00000013573	-1.783801829	DDX11
319	ENSG00000067646	-1.781317817	ZFY
320	ENSG00000228485	-1.77609442	GRK5-IT1
321	ENSG00000185130	-1.775345169	H2BC13
322	ENSG00000135119	-1.774610837	RNFT2
323	ENSG00000168961	-1.770421979	LGALS9
324	ENSG00000167553	-1.769893028	TUBA1C
325	ENSG00000136108	-1.767305561	CKAP2
326	ENSG00000275221	-1.755721524	H2AC15
327	ENSG00000138772	-1.755464656	ANXA3
328	ENSG00000257167	-1.755365934	TMPO-AS1
329	ENSG00000119969	-1.75451338	HELLS
330	ENSG00000166396	-1.748843248	SERPINB7
331	ENSG00000113070	-1.7485423	HBEGF
332	ENSG00000253669	-1.74727492	GASAL1
333	ENSG00000203668	-1.74710226	CHML
334	ENSG00000121621	-1.746743236	KIF18A
335	ENSG00000143942	-1.742138349	CHAC2
336	ENSG00000123416	-1.741078217	TUBA1B
337	ENSG00000178718	-1.738739341	RPP25
338	ENSG00000135333	-1.737844738	EPHA7
339	ENSG00000127586	-1.737108616	CHTF18
340	ENSG00000182481	-1.732656694	KPNA2
341	ENSG00000138092	-1.727166693	CENPO
342	ENSG00000135111	-1.726943068	TBX3
343	ENSG00000003137	-1.726195132	CYP26B1
344	ENSG00000050438	-1.72618536	SLC4A8
345	ENSG00000104738	-1.725725954	MCM4
346	ENSG00000224080	-1.725379805	UBE2FP1
347	ENSG00000143248	-1.724632404	RGS5
348	ENSG00000181938	-1.72455685	GINS3
349	ENSG00000278463	-1.724423695	H2AC4
350	ENSG00000273802	-1.719926453	H2BC8
351	ENSG00000272610	-1.718830162	MAGI1-IT1
352	ENSG00000276180	-1.715080282	H4C9
353	ENSG00000080493	-1.711929929	SLC4A4
354	ENSG00000146918	-1.7111589	NCAPG2
355	ENSG00000125968	-1.706827003	ID1
356	ENSG00000165304	-1.706565279	MELK
357	ENSG00000093072	-1.705766917	ADA2
358	ENSG00000000460	-1.704399842	C1orf112
359	ENSG00000203814	-1.700700847	H2BC18
360	ENSG00000188229	-1.700547122	TUBB4B
361	ENSG00000182010	-1.693661881	RTKN2
362	ENSG00000203772	-1.693224918	SPRN
363	ENSG00000228716	-1.692307878	DHFR
364	ENSG00000233695	-1.683313672	GAS6-AS1
365	ENSG00000240583	-1.681814582	AQP1
366	ENSG00000226887	-1.680658527	ERVMER34-1
367	ENSG00000162981	-1.680244743	LRATD1
368	ENSG00000155754	-1.674128704	C2CD6
369	ENSG00000187764	-1.671707352	SEMA4D
370	ENSG00000144354	-1.666069051	CDCA7
371	ENSG00000140525	-1.665511953	FANCI
372	ENSG00000198826	-1.662405338	ARHGAP11A
373	ENSG00000114374	-1.66111163	USP9Y
374	ENSG00000115687	-1.660913272	PASK
375	ENSG00000138641	-1.65658816	HERC3
376	ENSG00000225479	-1.650007239	PLCB1-IT1
377	ENSG00000171227	-1.648342861	TMEM37
378	ENSG00000069011	-1.646996033	PITX1
379	ENSG00000189423	-1.645504091	USP32P3
380	ENSG00000106018	-1.644722116	VIPR2
381	ENSG00000125885	-1.643965568	MCM8
382	ENSG00000108106	-1.641794507	UBE2S
383	ENSG00000116741	-1.640261558	RGS2
384	ENSG00000019582	-1.637655701	CD74
385	ENSG00000128944	-1.631589834	KNSTRN
386	ENSG00000205085	-1.629799155	FAM71F2
387	ENSG00000213551	-1.629540955	DNAJC9
388	ENSG00000197646	-1.628944055	PDCD1LG2
389	ENSG00000284770	-1.6281093	TBCE
390	ENSG00000175592	-1.627413319	FOSL1
391	ENSG00000169255	-1.626377071	B3GALNT1
392	ENSG00000256940	-1.624938759	PPP1R14B-AS1
393	ENSG00000092470	-1.624777727	WDR76
394	ENSG00000170396	-1.62145823	ZNF804A
395	ENSG00000157150	-1.621389149	TIMP4
396	ENSG00000180998	-1.620767241	GPR137C
397	ENSG00000164342	-1.61063564	TLR3
398	ENSG00000185347	-1.608083729	TEDC1
399	ENSG00000185480	-1.601104078	PARPBP
400	ENSG00000128266	-1.600461913	GNAZ
401	ENSG00000138741	-1.600042092	TRPC3
402	ENSG00000184524	-1.599335578	CEND1
403	ENSG00000101945	-1.599239127	SUV39H1
404	ENSG00000167670	-1.597489348	CHAF1A
405	ENSG00000120645	-1.595159204	IQSEC3
406	ENSG00000274618	-1.592827393	H4C6
407	ENSG00000166845	-1.586194556	C18orf54
408	ENSG00000171951	-1.584038434	SCG2
409	ENSG00000230397	-1.577283347	SPTLC1P1
410	ENSG00000236397	-1.570378467	DDX11L2
411	ENSG00000279094	-1.568089067	LINC01670
412	ENSG00000161692	-1.564584462	DBF4B
413	ENSG00000154898	-1.564062897	CCDC144CP
414	ENSG00000113805	-1.563519837	CNTN3
415	ENSG00000156968	-1.563032371	MPV17L
416	ENSG00000138658	-1.561650456	ZGRF1
417	ENSG00000153956	-1.559769347	CACNA2D1
418	ENSG00000072201	-1.556754524	LNX1
419	ENSG00000184635	-1.554031763	ZNF93
420	ENSG00000076706	-1.550103307	MCAM
421	ENSG00000253919	-1.547621411	THAP12P7
422	ENSG00000117632	-1.546633613	STMN1
423	ENSG00000169116	-1.545012275	PARM1
424	ENSG00000134198	-1.54426042	TSPAN2
425	ENSG00000145022	-1.540861873	TCTA
426	ENSG00000137872	-1.538495127	SEMA6D
427	ENSG00000151718	-1.538300366	WWC2
428	ENSG00000248483	-1.536573033	POU5F2
429	ENSG00000279078	-1.53537251	SND1-IT1
430	ENSG00000275714	-1.535151777	H3C1
431	ENSG00000054277	-1.534516353	OPN3
432	ENSG00000228817	-1.532377146	BACH1-IT2
433	ENSG00000106462	-1.530310813	EZH2
434	ENSG00000152953	-1.529327592	STK32B
435	ENSG00000084710	-1.526437542	EFR3B
436	ENSG00000183878	-1.526148211	UTY
437	ENSG00000164619	-1.522526665	BMPER
438	ENSG00000145569	-1.522204027	OTULINL
439	ENSG00000124610	-1.517949223	H1-1
440	ENSG00000196118	-1.512333496	CCDC189
441	ENSG00000126215	-1.510389055	XRCC3
442	ENSG00000233966	-1.509072823	UBE2SP1
443	ENSG00000154127	-1.508168812	UBASH3B
444	ENSG00000176225	-1.505171168	RTTN
445	ENSG00000166446	-1.504994005	CDYL2
446	ENSG00000270276	-1.503539737	H4C15
447	ENSG00000128536	-1.50202659	CDHR3
448	ENSG00000041353	-1.501903367	RAB27B

Open in a new tab

List of mRNAs and LINCRNAs under-expressed in cancer-associated fibroblasts as compared to normal fibroblasts. The list shows the GeneID, relative fold change values (Log₂FC), and gene symbol

qRT-PCR analysis of selected genes

Few genes from the differentially expressed gene list were selected for validation by qRT-PCR. Table 3 shows the list of genes that have been validated along with the fold changes. In the genes that have been validated, data from RNA-seq and qRT-PCR show the same pattern although fold changes are different.

Table 3.

Selected genes validated by qRT-PCR

Gene symbol	Log₂FC (RNA-seq)	Average FC in qRT-PCR with S.D
SFRP4	7.457728693	35.45 ± 6.46
DEPP1	6.8004577	48.37 ± 3.134
GDF6	5.080737946	58.09 ± 4.793
NPR1	4.968358544	42.38 ± 4.161
LINC00842	-2.799537368	0.023 ± 0.003

Open in a new tab

List of differentially expressed genes validated by qRT-PCR. The list shows the Gene symbol, fold change with respect to control in RNA-seq and fold change with respect to control in qRT-PCR

LINCRNA target prediction using npinter v5.0

LincRNA targets were predicted for differentially expressed lincRNAs in normal compared to CAFs using NP Inter V5.0. This analysis yielded 263 RNA-binding proteins (RBPs) (Table 4), 31 microRNAs (miRNAs), 1 non-coding (ncRNA) (Table 5) and 2 messenger RNA (mRNA) (Table 6) associated with a total of 14 lincRNAs. Also, there were 3 novel lincRNAs (LINC02344, LINC01670, and LINC02605) for which no data was found in the above database.

Table 4.

List of LINCRNAs interacting with proteins

NON CODE/ ENSEMBL ID	LINCRNA	Interacting Component	UniProt ID	Type
NONHSAG010513	LINC01252	IGF2BP3	O00425	protein
NONHSAG010513	LINC01252	SOX2	P48431	protein
NONHSAG010513	LINC01252	IGF2BP1	Q9NZI8	protein
NONHSAG010513	LINC01252	AGO2	Q9UKV8	protein
ENSG00000163364	LINC01116	ZC3HAV1	Q7Z2W4	protein
ENSG00000163364	LINC01116	IGF2BP3	O00425	protein
ENSG00000163364	LINC01116	CRNKL1	Q9BZJ0	protein
ENSG00000163364	LINC01116	IGF2BP1	Q9NZI8	protein
ENSG00000163364	LINC01116	DHX36	Q9H2U1	protein
ENSG00000163364	LINC01116	EZH2	A0A090N8E9	protein
ENSG00000223485	LINC01615	IGF2BP3	O00425	protein
NONHSAG099482	LINC01301	A1CF	Q9NQ94	protein
NONHSAG099482	LINC01301	A1CF	Q9NQ94	protein
NONHSAG099482	LINC01301	TRIM25	Q14258	protein
NONHSAG099482	LINC01301	ZC3HAV1	Q7Z2W4	protein
NONHSAG099482	LINC01301	ZMAT3	Q9HA38	protein
NONHSAG099482	LINC01301	ZMAT3	Q9HA38	protein
NONHSAG099482	LINC01301	SP1	P08047	protein
NONHSAG099482	LINC01301	IGF2BP3	O00425	protein
NONHSAG099482	LINC01301	RBMX	P38159	protein
NONHSAG099482	LINC01301	SOX2	P48431	protein
NONHSAG099482	LINC01301	U2AF1	Q01081	protein
NONHSAG099482	LINC01301	ILF2	Q12905	protein
NONHSAG099482	LINC01301	IGF2BP1	Q9NZI8	protein
NONHSAG099482	LINC01301	AGO2	Q9UKV8	protein
NONHSAG099482	LINC01301	DHX36	Q9H2U1	protein
NONHSAG099482	LINC01301	DHX36	Q9H2U1	protein
NONHSAG016089	LINC02298	DMD	A0A075B6G3	protein
NONHSAG016089	LINC02298	TRIM25	Q14258	protein
NONHSAG016089	LINC02298	IGF2BP3	O00425	protein
NONHSAG016089	LINC02298	SOX2	P48431	protein
NONHSAG016089	LINC02298	IGF2BP1	Q9NZI8	protein
NONHSAG016089	LINC02298	DHX36	Q9H2U1	protein
NONHSAG035677	LINC00882	A1CF	Q9NQ94	protein
NONHSAG035677	LINC00882	A1CF	Q9NQ94	protein
NONHSAG035677	LINC00882	FUBP1	Q96AE4	protein
NONHSAG035677	LINC00882	KHDRBS2	Q5VWX1	protein
NONHSAG035677	LINC00882	PUS10	Q3MIT2	protein
NONHSAG035677	LINC00882	TRIM25	Q14258	protein
NONHSAG035677	LINC00882	ZC3HAV1	Q7Z2W4	protein
NONHSAG035677	LINC00882	ZMAT3	Q9HA38	protein
NONHSAG035677	LINC00882	ZMAT3	Q9HA38	protein
NONHSAG035677	LINC00882	SP1	P08047	protein
NONHSAG035677	LINC00882	WDR4	P57081	protein
NONHSAG035677	LINC00882	SRSF1	Q07955	protein
NONHSAG035677	LINC00882	IGF2BP3	O00425	protein
NONHSAG035677	LINC00882	RBMX	P38159	protein
NONHSAG035677	LINC00882	SOX2	P48431	protein
NONHSAG035677	LINC00882	HNRNPM	P52272	protein
NONHSAG035677	LINC00882	U2AF1	Q01081	protein
NONHSAG035677	LINC00882	ILF2	Q12905	protein
NONHSAG035677	LINC00882	ELAVL1	Q15717	protein
NONHSAG035677	LINC00882	KHSRP	Q92945	protein
NONHSAG035677	LINC00882	CRNKL1	Q9BZJ0	protein
NONHSAG035677	LINC00882	IGF2BP1	Q9NZI8	protein
NONHSAG035677	LINC00882	AGO2	Q9UKV8	protein
NONHSAG035677	LINC00882	AGO2	Q9UKV8	protein
NONHSAG035677	LINC00882	DHX36	Q9H2U1	protein
NONHSAG035677	LINC00882	DHX36	Q9H2U1	protein
NONHSAG035677	LINC00882	Rbfox1	Q9NWB1	protein
ENSG00000228221	LINC00578	KHDRBS2	Q5VWX1	protein
ENSG00000228221	LINC00578	PUS10	Q3MIT2	protein
ENSG00000228221	LINC00578	TRIM25	Q14258	protein
ENSG00000228221	LINC00578	USF2	Q15853	protein
ENSG00000228221	LINC00578	ZC3HAV1	Q7Z2W4	protein
ENSG00000228221	LINC00578	ZMAT3	Q9HA38	protein
ENSG00000228221	LINC00578	ZMAT3	Q9HA38	protein
ENSG00000228221	LINC00578	SP1	P08047	protein
ENSG00000228221	LINC00578	CPSF1	Q10570	protein
ENSG00000228221	LINC00578	IGF2BP3	O00425	protein
ENSG00000228221	LINC00578	SOX2	P48431	protein
ENSG00000228221	LINC00578	CPSF7	Q8N684	protein
ENSG00000228221	LINC00578	CRNKL1	Q9BZJ0	protein
ENSG00000228221	LINC00578	IGF2BP1	Q9NZI8	protein
ENSG00000228221	LINC00578	AGO2	Q9UKV8	protein
ENSG00000228221	LINC00578	AGO2	Q9UKV8	protein
ENSG00000228221	LINC00578	AGO2	Q9UKV8	protein
ENSG00000228221	LINC00578	PTBP1	P26599	protein
ENSG00000228221	LINC00578	DHX36	Q9H2U1	protein
ENSG00000228221	LINC00578	DHX36	Q9H2U1	protein
NONHSAG036724	LINC00578	ADAR	P55265	protein
NONHSAG036724	LINC00578	WDR33	Q9C0J8	protein
NONHSAG036724	LINC00578	TARBP2	Q15633	protein
NONHSAG036724	LINC00578	RBM6	P78332	protein
NONHSAG036724	LINC00578	RBM10	P98175	protein
NONHSAG036724	LINC00578	UPF1	Q92900	protein
NONHSAG036724	LINC00578	hnRNPA2B1	O88569	protein
NONHSAG036724	LINC00578	FUS	P35637	protein
NONHSAG036727	LINC00578	HNRNPU	Q00839	protein
NONHSAG036727	LINC00578	DDX3X	O00571	protein
NONHSAG036727	LINC00578	EIF2C1	Q9UL18	protein
NONHSAG036727	LINC00578	AGO2	Q9UKV8	protein
NONHSAG036727	LINC00578	AGO3	Q9H9G7	protein
NONHSAG036727	LINC00578	AGO4	Q9HCK5	protein
NONHSAG036727	LINC00578	MBNL2	Q5VZF2	protein
NONHSAG036727	LINC00578	MOV10	P22626	protein
NONHSAG036727	LINC00578	SPPL3	Q8TCT6	protein
NONHSAG036727	LINC00578	HNRPG	P38159	protein
NONHSAG036727	LINC00578	YTDC1	Q96MU7	protein
NONHSAG036727	LINC00578	MBNL1	Q9NR56	protein
NONHSAG036727	LINC00578	FBL	P22087	protein
NONHSAG036727	LINC00578	RNMT	O43148	protein
NONHSAG036727	LINC00578	DBHS	Q15233	protein
NONHSAG036727	LINC00578	PSF	P23246	protein
NONHSAG036727	LINC00578	HDAC9	Q9UKV0	protein
NONHSAG036727	LINC00578	SMARCA4	P51532	protein
NONHSAG036727	LINC00578	SSB	P05455	protein
NONHSAG036727	LINC00578	EWSR1	Q01844	protein
ENSG00000231566	LINC02595	DCP1B	Q8IZD4	protein
ENSG00000231566	LINC02595	FUBP1	Q96AE4	protein
ENSG00000231566	LINC02595	TRIM25	Q14258	protein
ENSG00000231566	LINC02595	ZMAT3	Q9HA38	protein
ENSG00000231566	LINC02595	DDX6	P26196	protein
ENSG00000231566	LINC02595	AGO1	Q9UL18	protein
ENSG00000231566	LINC02595	FUS	P35637	protein
ENSG00000231566	LINC02595	EWSR1	Q01844	protein
ENSG00000231566	LINC02595	TIA1	P31483	protein
ENSG00000231566	LINC02595	RBMX	P38159	protein
ENSG00000231566	LINC02595	ILF2	Q12905	protein
ENSG00000231566	LINC02595	YTHDF2	Q9Y5A9	protein
ENSG00000231566	LINC02595	FUBP3	Q96I24	protein
ENSG00000231566	LINC02595	DHX36	Q9H2U1	protein
ENSG00000231566	LINC02595	DHX36	Q9H2U1	protein
ENSG00000231566	LINC02595	Rbfox1	Q9NWB1	protein
NONHSAG006313	LINC00595	WDR33	Q9C0J8	protein
NONHSAG006313	LINC00595	RTCB	Q9Y3I0	protein
NONHSAG006313	LINC00595	RBM6	P78332	protein
NONHSAG006313	LINC00595	RBM10	P98175	protein
NONHSAG006313	LINC00595	UPF1	Q92900	protein
NONHSAG006313	LINC00595	hnRNPA2B1	O88569	protein
NONHSAG006313	LINC00595	FUS	P35637	protein
NONHSAG060614	LINC00595	EIF2C1	Q9UL18	protein
NONHSAG060614	LINC00595	AGO2	Q9UKV8	protein
NONHSAG060614	LINC00595	AGO3	Q9H9G7	protein
NONHSAG060614	LINC00595	AGO4	Q9HCK5	protein
NONHSAG060614	LINC00595	MBNL2	Q5VZF2	protein
NONHSAG060614	LINC00595	MOV10	P22626	protein
NONHSAG060614	LINC00595	FBL	P22087	protein
NONHSAG060614	LINC00595	RBFOX2	O43251	protein
NONHSAG060614	LINC00595	DBHS	Q15233	protein
NONHSAG060614	LINC00595	PSF	P23246	protein
NONHSAG060614	LINC00595	TRIM25	Q14258	protein
NONHSAG060614	LINC00595	ZC3HAV1	Q7Z2W4	protein
NONHSAG060614	LINC00595	ZMAT3	Q9HA38	protein
NONHSAG060614	LINC00595	ZMAT3	Q9HA38	protein
NONHSAG060614	LINC00595	SERBP1	Q8NC51	protein
NONHSAG060614	LINC00595	IGF2BP3	O00425	protein
NONHSAG060614	LINC00595	BRCA1	P38398	protein
NONHSAG060614	LINC00595	SOX2	P48431	protein
NONHSAG060614	LINC00595	IGF2BP1	Q9NZI8	protein
NONHSAG060614	LINC00595	AGO2	Q9UKV8	protein
NONHSAG060614	LINC00595	AGO2	Q9UKV8	protein
NONHSAG060614	LINC00595	AGO2	Q9UKV8	protein
NONHSAG060614	LINC00595	DHX36	Q9H2U1	protein
NONHSAG060614	LINC00595	DHX36	Q9H2U1	protein
NONHSAG032603	LINC01695	ZMAT3	Q9HA38	protein
NONHSAG032603	LINC01695	SP1	P08047	protein
NONHSAG032603	LINC01695	IGF2BP3	O00425	protein
NONHSAG032603	LINC01695	SOX2	P48431	protein
NONHSAG032603	LINC01695	IGF2BP1	Q9NZI8	protein
NONHSAG032603	LINC01695	DHX36	Q9H2U1	protein
NONHSAG032603	LINC01695	DHX36	Q9H2U1	protein
NONHSAG020184	LINC00311	WDR33	Q9C0J8	protein
NONHSAG020184	LINC00311	TARDBP	A0A087WZM1	protein
NONHSAG020184	LINC00311	HNRNPF	P52597	protein
NONHSAG020184	LINC00311	CSTF2	P33240	protein
NONHSAG020184	LINC00311	RTCB	Q9Y3I0	protein
NONHSAG020184	LINC00311	TIAL1	E7ETC0	protein
NONHSAG020184	LINC00311	TDP-43	-	protein
NONHSAG020184	LINC00311	TARBP2	Q15633	protein
NONHSAG020184	LINC00311	RBM6	P78332	protein
NONHSAG020184	LINC00311	RBM10	P98175	protein
NONHSAG020184	LINC00311	MOV10	P22626	protein
NONHSAG020184	LINC00311	UPF1	Q92900	protein
NONHSAG020184	LINC00311	hnRNPA2B1	O88569	protein
NONHSAG020184	LINC00311	FUS	P35637	protein
NONHSAG020184	LINC00311	CSTF2T	Q9H0L4	protein
NONHSAG020184	LINC00311	FAM120A	Q9NZB2	protein
NONHSAG020184	LINC00311	NCBP2	P52298	protein
NONHSAG020184	LINC00311	TAF15	Q92804	protein
NONHSAG020184	LINC00311	TIA1	P31483	protein
NONHSAG020184	LINC00311	AGGF1	Q8N302	protein
NONHSAG020184	LINC00311	EWSR1	Q01844	protein
NONHSAG020184	LINC00311	DBHS	Q15233	protein
NONHSAG020184	LINC00311	SLTM	Q9NWH9	protein
NONHSAG020184	LINC00311	T2FA	P35269	protein
NONHSAG020184	LINC00311	EIF2C1	Q9UL18	protein
NONHSAG020184	LINC00311	AGO2	Q9UKV8	protein
NONHSAG020184	LINC00311	AGO3	Q9H9G7	protein
NONHSAG020184	LINC00311	AGO4	Q9HCK5	protein
NONHSAG020184	LINC00311	FBL	P22087	protein
NONHSAG020184	LINC00311	RBFOX2	O43251	protein
NONHSAG020184	LINC00311	HNRNPA1	P09651	protein
NONHSAG020184	LINC00311	U2AF2	P26368	protein
NONHSAG020184	LINC00311	PSF	P23246	protein
NONHSAG020184	LINC00311	TRIM25	Q14258	protein
NONHSAG020184	LINC00311	ZC3HAV1	Q7Z2W4	protein
NONHSAG020184	LINC00311	IGF2BP3	O00425	protein
NONHSAG020184	LINC00311	SOX2	P48431	protein
NONHSAG020184	LINC00311	FTO	Q9C0B1	protein
NONHSAG020184	LINC00311	DHX36	Q9H2U1	protein
ENSG00000241213	LINC02024	ZC3HAV1	Q7Z2W4	protein
ENSG00000241213	LINC02024	SOX2	P48431	protein
ENSG00000241213	LINC02024	IGF2BP1	Q9NZI8	protein
NONHSAG006038	LINC01515	A1CF	Q9NQ94	protein
NONHSAG006038	LINC01515	A1CF	Q9NQ94	protein
NONHSAG006038	LINC01515	AIMP1	Q12904	protein
NONHSAG006038	LINC01515	EXOSC10	Q01780	protein
NONHSAG006038	LINC01515	FUBP1	Q96AE4	protein
NONHSAG006038	LINC01515	FUBP1	Q96AE4	protein
NONHSAG006038	LINC01515	KHDRBS2	Q5VWX1	protein
NONHSAG006038	LINC01515	KHDRBS2	Q5VWX1	protein
NONHSAG006038	LINC01515	METTL1	Q9UBP6	protein
NONHSAG006038	LINC01515	SCAF8	Q9UPN6	protein
NONHSAG006038	LINC01515	TRIM25	Q14258	protein
NONHSAG006038	LINC01515	USF2	Q15853	protein
NONHSAG006038	LINC01515	ZMAT3	Q9HA38	protein
NONHSAG006038	LINC01515	ZMAT3	Q9HA38	protein
NONHSAG006038	LINC01515	SP1	P08047	protein
NONHSAG006038	LINC01515	WDR4	P57081	protein
NONHSAG006038	LINC01515	CPSF1	Q10570	protein
NONHSAG006038	LINC01515	hnRNPD	Q14103	protein
NONHSAG006038	LINC01515	Rbfox2	O43251	protein
NONHSAG006038	LINC01515	IGF2BP3	O00425	protein
NONHSAG006038	LINC01515	SNRPA	P09012	protein
NONHSAG006038	LINC01515	RBMX	P38159	protein
NONHSAG006038	LINC01515	SOX2	P48431	protein
NONHSAG006038	LINC01515	ELAVL1	Q15717	protein
NONHSAG006038	LINC01515	CPSF7	Q8N684	protein
NONHSAG006038	LINC01515	IGF2BP1	Q9NZI8	protein
NONHSAG006038	LINC01515	AGO2	Q9UKV8	protein
NONHSAG006038	LINC01515	DHX36	Q9H2U1	protein
NONHSAG006038	LINC01515	DHX36	Q9H2U1	protein
NONHSAG006038	LINC01515	Rbfox1	Q9NWB1	protein
-	LINC00842	QKI	Q96PU8	protein
-	LINC00842	SFRS2	Q01130	protein
-	LINC00842	YTHDF1	Q9BYJ9	protein
-	LINC00842	MBNL2	Q5VZF2	protein
-	LINC00842	MOV10	P22626	protein
-	LINC00842	YTDC1	Q96MU7	protein
-	LINC00842	FBL	P22087	protein
-	LINC00842	ACIN1	Q9UKV3	protein
-	LINC00842	HNRNPA1	P09651	protein
-	LINC00842	UPF1	Q92900	protein
-	LINC00842	DBHS	Q15233	protein
-	LINC00842	PSF	P23246	protein
-	LINC00842	HDAC9	Q9UKV0	protein
-	LINC00842	SMARCA4	P51532	protein
-	LINC00842	EWSR1	Q01844	protein
NONHSAG005796	LINC00842	ADAR	P55265	protein
NONHSAG005796	LINC00842	WDR33	Q9C0J8	protein
NONHSAG005796	LINC00842	SFRS1	Q07955	protein
NONHSAG005796	LINC00842	ELAVL1	P70372	protein
NONHSAG005796	LINC00842	AGO2	Q8CJG0	protein
NONHSAG005796	LINC00842	HNRNPU	Q00839	protein
NONHSAG005796	LINC00842	CSTF2	P33240	protein
NONHSAG005796	LINC00842	hnRNPC	P07910	protein
NONHSAG005796	LINC00842	TARBP2	Q15633	protein
NONHSAG005796	LINC00842	RBM6	P78332	protein
NONHSAG005796	LINC00842	RBM10	P98175	protein
NONHSAG005796	LINC00842	UPF1	Q92900	protein
NONHSAG005796	LINC00842	hnRNPA2B1	O88569	protein
NONHSAG005796	LINC00842	FUS	P35637	protein

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The list shows Ensembl/NON CODE ID, LINCRNA and UniProt ID and type of the interacting components

Table 5.

List of LINCRNAs interacting with ncRNAs

NON CODE/ ENSEMBL ID	LINCRNA	Interacting Component	miRNA ID	Type
ENSG00000163364	LINC01116	miR-203	MI0000283	miRNA
ENSG00000163364	LINC01116	miR-3141	MI0014165	miRNA
ENSG00000163364	LINC01116	miR-744-5p	MI0005559	miRNA
ENSG00000163364	LINC01116	miR-744-5p	MI0005559	miRNA
ENSG00000163364	LINC01116	miR-3612	MI0016002	miRNA
ENSG00000163364	LINC01116	miR-744-5p	MI0005559	miRNA
NONHSAG035677	LINC00882	miR-214-3p	MI0000290	miRNA
NONHSAG020184	LINC00311	hsa-mir-125a-3p	MI0000469	miRNA
NONHSAG020184	LINC00311	hsa-miR-125b-5p	MI0000446	miRNA
NONHSAG020184	LINC00311	hsa-mir-150	MI0000479	miRNA
NONHSAG020184	LINC00311	hsa-miR-296-3p	MI0000747	miRNA
NONHSAG020184	LINC00311	hsa-miR-4319	MI0015848	miRNA
NONHSAG020184	LINC00311	hsa-miR-455-3p	MI0003513	miRNA
NONHSAG020184	LINC00311	hsa-miR-129-5p	MI0000252	miRNA
NONHSAG020184	LINC00311	hsa-miR-129-2-3p	MI0000473	miRNA
NONHSAG020184	LINC00311	hsa-miR-532-3p	MI0003205	miRNA
NONHSAG005796	LINC00842	hsa-miR-1224-3p	MI0003764	miRNA
NONHSAG005796	LINC00842	hsa-miR-378a-5p	MI0000786	miRNA
NONHSAG005796	LINC00842	hsa-miR-378b	MI0014154	miRNA
NONHSAG005796	LINC00842	hsa-miR-378c	MI0015825	miRNA
NONHSAG005796	LINC00842	hsa-miR-378d	MI0016749	miRNA
NONHSAG005796	LINC00842	hsa-miR-378e	MI0016750	miRNA
NONHSAG005796	LINC00842	hsa-miR-378f	MI0016756	miRNA
NONHSAG005796	LINC00842	hsa-miR-378i	MI0016902	miRNA
NONHSAG005796	LINC00842	hsa-miR-422a	MI0001444	miRNA
NONHSAG005796	LINC00842	hsa-miR-665	MI0005563	miRNA
NONHSAG005796	LINC00842	hsa-miR-1197	MI0006656	miRNA
NONHSAG005796	LINC00842	hsa-miR-199a-5p	MI0000242	miRNA
NONHSAG005796	LINC00842	hsa-miR-199b-5p	MI0000282	miRNA
NONHSAG005796	LINC00842	hsa-miR-335-3p	MI0000816	miRNA
NONHSAG005796	LINC00842	hsa-miR-378h	MI0016808	miRNA
NONHSAG020184.2	LINC00311	SNHG4	ENSG00000281398	ncRNA

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The list shows Ensembl/NON CODE ID, LINCRNA and miRNA ID and type of the interacting components

Table 6.

List of LINCRNAs interacting with mRNAs

NON CODE/ ENSEMBL ID	LINCRNA	Interacting Component	ENSEMBL ID	Type
ENSG00000163364	LINC01116	MYC	ENSG00000136997	mRNA
NONHSAG020184.2	LINC00311	KIAA0513	ENSG00000135709	mRNA

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The list shows Ensembl/NON CODE ID, LINCRNA and type of the interacting components

Prediction of targets of the miRNAs

The 24 miRNAs obtained from the above analysis targets were fed into miRDB for mRNA target prediction. mRNA targets with a score of >/= 95 were chosen. We have identified 288 mRNA targets associated with the input of 24 miRNAs.

The predicted mRNA targets were cross-referenced with our dataset to identify the common mRNAs. Subsequently, we established lincRNA-miRNA-mRNA combinations Table 7.

Table 7.

List of LINCRNA-miRNA-mRNA combinations

LINCRNA	Log2FC (for LINCRNA)	miRNA	mRNA target	Log2FC (for mRNA)
LINC00882	1.812015961	miR-214-3p	ATP2A3	3.024375
			PIM1	1.596972
			FBXO32	2.52126
LINC00311	-2.371344708	hsa-mir-125a-3p	BRCA1	-2.203512532
LINC00311	-2.371344708	hsa-mir-125a-3p	SH3TC2	-2.060263012
LINC00842	-2.799537368	hsa-miR-1224-3p	CDC25A	-2.958067112
			KDR	-2.369999586
			SLC4A8	-1.72618536
		hsa-miR-199a-5p	HAPLN1	-3.235820469
		hsa-miR-199b-5p	HAPLN1	-3.235820469
		hsa-miR-335-3p	MCAM	-1.550103307
			UTY	-1.526148211
			CCDC85A	-3.03369872
			KCNQ5	-1.982236088
			EPHA7	-1.737844738

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The list shows differentially expressed LINCRNA, relative fold change values of LINCRNA (Log₂FC (for LINCRNA)), miRNA, mRNA target and relative fold change values of target mRNA (Log₂FC (for mRNA)) in the same data set

Discussion

The effect of microenvironment on the initiation, maintenance and progression of solid tumors has been established beyond doubt [25]. CAFs, which constitute a significant component of the TME are a major source of secreted factors. Interaction with the CAF and derived factors not only play a significant role in promoting tumorigenesis and metastasis but also influence the response of the tumor to drugs [10, 12]. It is likely that in response to chemotherapeutic drugs, some pro-tumorigenic actions of CAFs may be activated, which in turn aid the tumor cells in escaping from the drug challenge. Studies on prostate cancer have shown that tumor cells grown in the presence of CAFs or CAF-derived factors show much higher tolerance to drugs as compared to tumor cells grown alone [26]. Also, cells grown with CAF-derived factors have a higher potential to metastasize [27]. These highlight the possibility of targeting CAF/derived factors for therapeutic purposes.

Attempts have been made to target various components of the TME, such as ECM, exosomes, CAFs, immune cells, vascular cells etc [28].

Angiotensin II receptor agonists such as Losartan, Candersartan, etc. have been shown to reduce mortality in gastro-esophageal cancer patients [29]. Losartan and its analogs reduce the secretion of collagen I by interfering with transforming growth factor-β (TGF-β) signalling. This improves the delivery of chemotherapeutics to tumor cells [30, 31]. Ronespartat (SST0001), a heparanase inhibitor has shown promising results in inhibiting tumor growth when used alone or in combination at different phases of clinical trials [32, 33]. Considering the important role of Matrix Metalloproteinases (MMPs) and collagen cross-linkers in ECM remodelling several drugs have been tried to modulate MMP activity Ex: Incyclinide, JNJ0966, Fab 3369 [34–38]. However extreme caution has to be exercised while dealing with ECM as it can also promote metastases.

Several anti—angiogenic agents such as Bevacizumab, Apatinib, anti-VEGF antibodies in various combinations along with other chemotherapeutic agents such as paclitaxel and carboplatin have shown promising results in clinical trials (NCT02885753, NCT03100955). Multiple therapeutic strategies targeting the immune system, such as inhibition of macrophage recruitment and differentiation into the pro-tumoral TAMs Ex: anti-CSF-1R neutralizing antibodies or small molecule inhibitors, antibody anti-CD204, targeted-folate-receptor beta (FRβ) [39–41]; targeting chronic inflammation using IL-1R antagonists such as Anakinra (Kineret), anti-IL-1β monoclonal antibody [42, 43]; activating the anti-tumoral activity of the TME by used of GM-CSF [44], immune checkpoint therapies such as CTLA-4 and PD-1 [45, 46] have shown promise of better prognosis.

CAF being the most abundant cell type in the TME would be attractive targets for TME therapy. Fibroblast activation protein α (FAP), a membrane bound serine protease has been targeted for therapy in combination with a variety of drugs. However, these approaches have not been very successful. It is very likely due to the fact that FAP is not specific to CAFs, but also seen in normal fibroblasts. More qualitative and quantitative comparisons between normal and cancer associated fibroblasts are required to identify more effective ways of therapeutic targeting [47, 48]. Our study has tried to address this lacuna.

CAFs are distinguished from normal fibroblasts by their contractile characteristics, and metabolic and transcriptomic activity [49, 50]. Also, they are shown to express higher levels of FAP, alpha SMA and vimentin [49, 51, 52]. However, till date, there are no known unique markers of CAF. Identification of such markers becomes extremely essential if CAFs/derived factors are to be targeted for therapy, particularly because there are both pro- and anti-tumor properties of these factors. In this study, we have used the NGS platform to do a comparative analysis of fibroblasts derived from non-malignant (BPH) and cancerous prostate. This study has identified 818 genes differentially expressed between normal and cancer-associated fibroblasts. Also, there are 17 lncRNAs which show differential expression.

Long Intergenic Non-Coding RNAs (lincRNAs) are RNA molecules exceeding 200 nucleotides, lacking protein-coding functions and non-overlapping with annotated coding genes. They impact gene expression by modulating chromatin structure, regulating transcription of nearby and distant genes, and interacting with DNA, RNA, and proteins [53–55]. In cancer patients, differential lncRNA expression has been correlated with the overall survival (OS), metastasis, as well as tumor stage/grade [56–58]. LncRNAs have been detected in body fluids like plasma, serum, and urine using real-time PCR. One of the reasons lncRNAs are suitable as cancer diagnostic and prognostic biomarkers is their remarkable stability while circulating in body fluids, particularly when enclosed within exosomes or apoptotic bodies [59]. These characteristics of lncRNA make them attractive candidates for biomarkers. These biomarkers offer a minimally invasive alternative to conventional biopsies [60]. These markers can also be used to predict the prognosis of cancer patients, assess the risk of tumor metastasis and recurrence after surgery, and also to evaluate the success of therapeutic intervention. The distinct expression profiles of cancer-associated lncRNAs, which can vary significantly among different types of cancer, hold promise as efficient tumor biomarkers in various body fluids [57, 58, 61] (Supplementary Table 1 showing LncRNA as a prognostic and diagnostic marker in different cancers and Supplementary Fig. 3 showing tissue specific LNCRNA as potential biomarkers).

Despite the fact that lincRNAs are good biomarkers, targeting lincRNA or other ncRNA for therapeutic purposes have been extremely challenging. One of the reasons being very low conservation of lncRNAs across species. A small number of lncRNAs which are conserved between humans and mice have been discovered, while many human lncRNAs are absent in mice [62, 63].

Although it has been observed that lincRNAs show specific expression patterns in cancers, the heterogeneity in tumors makes it difficult to target them. Some studies have used in-silico approaches to identify lincRNA-miRNA-mRNA combinations. For example, a study has shown the influence of LOC101928304/miR-490-3p/LRRC2, a lincRNA-miRNA-mRNA axis on Atrial Fibrillation (AF). The levels of LOC101928304 and LRRC were elevated whereas miR-490-3p exhibited a decreased expression in the myocardial tissue of AF patients [64]. However, there is not much experimental data available. Given the advantages of using lincRNAs as biomarkers and also the difficulties in targeting them for therapeutic intervention, identifying a combination of lincRNA-miRNA-mRNA may provide better options for targeting. In this study, we have predicted the targets of the differentially expressed lincRNAs and identified 15 lincRNA-miRNA-mRNA combinations. This would help in understanding the mechanism of action of these RNAs as well as identifying strategies for therapeutic targeting. However, this would in future need more experimental validation.

Supplementary information

12885_2024_13006_MOESM1_ESM.xlsx^{(13.1KB, xlsx)}

Supplementary Material 1: Supplementary table 1: LNCRNAs as cancer prognostic and/or diagnostic marker. The table shows the type of cancer, LNCRNA involved, LNCRNA expression, and their relevance as prognostic or diagnostic markers [65–89]

12885_2024_13006_MOESM2_ESM.docx^{(1.9MB, docx)}

Supplementary Material 2: Supplementary file. Shows the quality assessment of all the RNA samples using Tapestation

12885_2024_13006_MOESM3_ESM.png^{(71.6KB, png)}

Supplementary Material 3: Supplementary Fig 1. RNA sequencing analysis workflow

12885_2024_13006_MOESM4_ESM.png^{(91.9KB, png)}

Supplementary Material 4: Supplementary Fig 2. LINCRNA analysis workflow

12885_2024_13006_MOESM5_ESM.png^{(301KB, png)}

Supplementary Material 5: Supplementary Fig 3 Tissue-specific LNCRNA as potential markers

Abbreviations

TME: Tumor microenvironment
CAFs: Cancer-associated fibroblasts
BPH: Benign Prostate Hyperplasia
LINCRNA: Non-Intergenic Non-Coding RNA
TCGA: The Cancer Genome Atlas
ECM: Extracellular matrix
TRUS: Transrectal Ultrasound Scan
TURP: Transurethral Resection of the Prostate
RPMI: Roswell Park Memorial Institute
PenStrep: Penicillin Streptomycin
RIN: RNA Integrity Number
ncRNA: Non-Coding RNA
LNCRNA: Long Non-Coding RNA
LINCRNA: Long-intergenic Non-Coding RNA
miRNA: MicroRNA
circRNA: CircularRNA
mRNA: Messenger RNA
FAP: Fibroblast Activation Protein
SMA: Smooth Muscle Actin
OS: Overall Survival

Authors' contributions

AA analyzed the transcriptomic data, prepared the manuscript draft with all figures and tables. MSM, RRA, and NN helped in the collection of patient samples, deriving the fibroblasts and preliminary characterization. VB and NT helped in the collection of patient samples and clinical/pathological evaluation. RK helped co-ordinate all the patient-related work and helped in procuring funding. PR conceived and strategized the study, procured the funding, finalized the manuscript. All authors reviewed and approved the manuscript

Clinical trial number

Not applicable.

Funding

This study was supported by the Indian Council for Medical Research, Govt of India (2019 − 0937). AA is supported by Lady Tata Memorial Trust Fellowship. The authors thank the Centre for Human Genetics, Bengaluru, and the Institute of Nephro-Urology, Bengaluru for all the support during the course of this study.

Availability of data and materials

The datasets generated and analyzed during this study are available on GEO, Accession Number GSE270705.

Declarations

Ethics approval and consent to participate

The study was approved by the Institutional Ethics Committee of both the participating institutions (CHG/077(b)/IEC/2019-20/001 and EC/01/2019). Informed consent has been obtained from all participants whose tissue samples have been used in this study. The identity of the patients has been kept confidential.

The study has been conducted in accordance with the Declaration of Helsinki.

This study presented here was funded by the Indian Council for Medical Research, Govt of India (2019 − 0937), granted to PR.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12885_2024_13006_MOESM1_ESM.xlsx^{(13.1KB, xlsx)}

12885_2024_13006_MOESM2_ESM.docx^{(1.9MB, docx)}

Supplementary Material 2: Supplementary file. Shows the quality assessment of all the RNA samples using Tapestation

12885_2024_13006_MOESM3_ESM.png^{(71.6KB, png)}

Supplementary Material 3: Supplementary Fig 1. RNA sequencing analysis workflow

12885_2024_13006_MOESM4_ESM.png^{(91.9KB, png)}

Supplementary Material 4: Supplementary Fig 2. LINCRNA analysis workflow

12885_2024_13006_MOESM5_ESM.png^{(301KB, png)}

Supplementary Material 5: Supplementary Fig 3 Tissue-specific LNCRNA as potential markers

Data Availability Statement

The datasets generated and analyzed during this study are available on GEO, Accession Number GSE270705.

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[CR25] 25.Joyce JA, Pollard JW. Microenvironmental regulation of metastasis. Nat Rev Cancer. 2009;9(4):239–52. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[CR35] 35.Gu Y, et al. Inhibition of breast cancer cell extracellular matrix degradative activity by chemically modified tetracyclines. Ann Med. 2005;37(6):450–60. [DOI] [PubMed] [Google Scholar]

[CR36] 36.Fingleton B. CMT-3. CollaGenex. Curr Opin Investig Drugs. 2003;4(12):1460–7. [PubMed] [Google Scholar]

[CR37] 37.Scannevin RH, et al. Discovery of a highly selective chemical inhibitor of matrix metalloproteinase-9 (MMP-9) that allosterically inhibits zymogen activation. J Biol Chem. 2017;292(43):17963–74. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[CR40] 40.Noy R, Pollard JW. Tumor-associated macrophages: from mechanisms to therapy. Immunity. 2014;41(1):49–61. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR41] 41.Komohara Y, et al. Tumor-associated macrophages: potential therapeutic targets for anti-cancer therapy. Adv Drug Deliv Rev. 2016;99Pt B:p180-185. [DOI] [PubMed] [Google Scholar]

[CR42] 42.Holen I, et al. IL-1 drives breast cancer growth and bone metastasis in vivo. Oncotarget. 2016;7(46):75571–84. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR43] 43.Tulotta C, Ottewell P. The role of IL-1B in breast cancer bone metastasis. Endocr Relat Cancer. 2018;25(7):R421-434. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR44] 44.Yan WL, et al. Activation of GM-CSF and TLR2 signaling synergistically enhances antigen-specific antitumor immunity and modulates the tumor microenvironment. J Immunother Cancer. 2021;9(10):e002758. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR45] 45.Shiravand Y, et al. Immune checkpoint inhibitors in cancer therapy. Curr Oncol. 2022;29(5):3044–60. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR46] 46.Boohaker RJ, et al. Rational design and development of a peptide inhibitor for the PD-1/PD-L1 interaction. Cancer Lett. 2018;434:11–21. [DOI] [PubMed] [Google Scholar]

[CR47] 47.Ishii N, et al. Conophylline suppresses pancreatic cancer desmoplasia and cancer-promoting cytokines produced by cancer-associated fibroblasts. Cancer Sci. 2019;110(1):334–44. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR48] 48.Sounni NE, Noel A. Targeting the tumor microenvironment for cancer therapy. Clin Chem. 2013;59(1):85–93. [DOI] [PubMed] [Google Scholar]

[CR49] 49.Xing F, Saidou J, Watabe K. Cancer associated fibroblasts (CAFs) in tumor microenvironment. Front Biosci (Landmark Ed). 2010;15(1):166–79. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR50] 50.De Wever O, et al. Stromal myofibroblasts are drivers of invasive cancer growth. Int J Cancer. 2008;123(10):2229–38. [DOI] [PubMed] [Google Scholar]

[CR51] 51.Muchlińska A, et al. Alpha-smooth muscle actin-positive cancer-associated fibroblasts secreting osteopontin promote growth of luminal breast cancer. Cell Mol Biol Lett. 2022;27(1):45. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR52] 52.Gilardi L, et al. Imaging Cancer-Associated fibroblasts (CAFs) with FAPi PET. Biomedicines. 2022;10(3):523. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR53] 53.Ti W, Wang J, Cheng Y. The interaction between ong non-coding RNAs and Cancer-Associated fibroblasts in lung cancer. Front Cell Dev Biol. 2021;9:714125. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR54] 54.Ransohoff JD, Wei Y, Khavari PA. The functions and unique features of long intergenic non-coding RNA. Nat Rev Mol Cell Biol. 2018;19(3):143–57. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Comparative transcriptome of normal and cancer-associated fibroblasts

Apoorva Abikar

Mohammad Mehaboob Subhani Mustafa

Radhika Rajiv Athalye

Namratha Nadig

Ninad Tamboli

Vinod Babu

Ramaiah Keshavamurthy

Prathibha Ranganathan

Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Introduction

Materials and methods

Clinical specimen collection

Culturing of fibroblasts

RNA isolation

RNA sequencing

RNA sequencing analysis workflow

qRT-PCR analysis

LINCRNA functional annotation

Results

Differential expression of genes between normal and cancer-associated fibroblasts

Table 1.

Table 2.

qRT-PCR analysis of selected genes

Table 3.

LINCRNA target prediction using npinter v5.0

Table 4.

Table 5.

Table 6.

Prediction of targets of the miRNAs

Table 7.

Discussion

Supplementary information

Abbreviations

Authors' contributions

Clinical trial number

Funding

Availability of data and materials

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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