Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer

Yosuke Iwatate; Hajime Yokota; Isamu Hoshino; Fumitaka Ishige; Naoki Kuwayama; Makiko Itami; Yasukuni Mori; Satoshi Chiba; Hidehito Arimitsu; Hiroo Yanagibashi; Wataru Takayama; Takashi Uno; Jason Lin; Yuki Nakamura; Yasutoshi Tatsumi; Osamu Shimozato; Hiroki Nagase

doi:10.1371/journal.pone.0268630

. 2022 Jun 1;17(6):e0268630. doi: 10.1371/journal.pone.0268630

Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer

Yosuke Iwatate ¹, Hajime Yokota ², Isamu Hoshino ^3,^*, Fumitaka Ishige ¹, Naoki Kuwayama ³, Makiko Itami ⁴, Yasukuni Mori ⁵, Satoshi Chiba ¹, Hidehito Arimitsu ¹, Hiroo Yanagibashi ¹, Wataru Takayama ¹, Takashi Uno ², Jason Lin ⁶, Yuki Nakamura ⁶, Yasutoshi Tatsumi ⁶, Osamu Shimozato ⁶, Hiroki Nagase ⁶

Editor: Eithne Costello⁷

PMCID: PMC9159604 PMID: 35648752

Abstract

Transcriptomic analysis of cancer samples helps identify the mechanism and molecular markers of cancer. However, transcriptomic analyses of pancreatic cancer from the Japanese population are lacking. Hence, in this study, we performed RNA sequencing of fresh and frozen pancreatic cancer tissues from 12 Japanese patients to identify genes critical for the clinical pathology of pancreatic cancer among the Japanese population. Additionally, we performed immunostaining of 107 pancreatic cancer samples to verify the results of RNA sequencing. Bioinformatics analysis of RNA sequencing data identified ITGB1 (Integrin beta 1) as an important gene for pancreatic cancer metastasis, progression, and prognosis. ITGB1 expression was verified using immunostaining. The results of RNA sequencing and immunostaining showed a significant correlation (r = 0.552, p = 0.118) in ITGB1 expression. Moreover, the ITGB1 high-expression group was associated with a significantly worse prognosis (p = 0.035) and recurrence rate (p = 0.028). We believe that ITGB1 may be used as a drug target for pancreatic cancer in the future.

Introduction

Pancreatic cancer is a lethal cancer type with a poor prognosis and severe recurrence rate. It has the fourth and seventh highest cancer-related mortality rate in Japan and the world, respectively [1,2]. The overall five-year survival rate of pancreatic cancer is 10%, and it increases to only 20% even after curative surgery, making it one of the most lethal cancer types [3–5]. Unfortunately, there are no established sensitive markers for predicting the recurrence and survival of pancreatic cancer, and no therapeutic target gene has been determined. Technological development has facilitated the understanding of cancer genomics, and high-throughput gene expression analysis has revolutionized cancer genetics in the last 15 years. Even for pancreatic cancer, large-scale genome analyses with next-generation sequencing (NGS) have been performed [6]. Transcriptomic analyses on a large sample size classified RNA signatures of pancreatic cancer into classical and basal-like types, and further into four subtypes: squamous, pancreatic progenitor, immunogenic, and aberrantly differentiated endocrine exocrine [7,8]. In recent years, public databases such as The Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus have been constructed, and the gene expression data obtained from them are of great value for understanding the molecular mechanism, diversity, diagnosis, and clinical outcomes of cancers, including pancreatic cancer.

However, transcriptomic analysis of pancreatic cancer samples from the East Asian and Japanese population are lacking. To understand and analyze the mechanism and molecular markers of pancreatic cancer among the Japanese population, we performed a transcriptomic analysis in 12 Japanese patients with pancreatic cancer and compared the results with the TCGA data. The target genes thought to be involved in prognosis and recurrence of pancreatic cancer were narrowed down. We aimed through this study to clarify the relationship between the expression of the target gene by sequencing and the protein expression by immunostaining, where the expression of the target gene was further verified through immunostaining of a large number of patient samples. We identified ITGB1 as an important gene in the progression of pancreatic cancer. Our findings suggest that a high ITGB1 expression could predict the prognosis and recurrence of pancreatic cancer. ITGB1 is a constituent of β subunits in integrin molecules [9]. Integrin is mainly present in the plasma membrane and plays a role in cell-cell adhesion, cell-extracellular matrix adhesion, and signal transduction [9,10]. The lack of such adhesion leads to the withdrawal of cell survival signals, resulting in an exfoliation-induced apoptotic process called "anoikis" [11]. Cancer cells are resistant to anoikis through certain integrin types, which is one of the key mechanisms for successful infiltration, migration, and metastasis [11]. It has been reported that high ITGB1 expression significantly correlated with the deterioration of prognosis in colorectal, breast, and lung cancers, but its correlation with pancreatic cancer remains controversial [12–17].

Materials and methods

Study population criteria

Between January 2013 and May 2018, 138 patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) after its surgical removal without neoadjuvant chemotherapy and preoperative radiation were included in the study at the Chiba Cancer Center in Japan. Total RNA was extracted from 15 patients, including nine frozen specimens stored in our institute’s biobank, and comprehensively analyzed by NGS. This study was approved by the Chiba Cancer Center Review Board (grant number H29-006). All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki Declaration of 1964 and its later amendments. Written informed consent was obtained from the patients for publication of this study and the accompanying clinicopathological data.

RNA sequencing

Total RNA was isolated from a frozen tissue block containing approximately 50–100 mg of PDAC tissue using the miRNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. Samples with an RNA integrity number (RIN value) of 7.0 or higher were used for RNA sequencing. The library for NGS was built with the Ion Proton ™ equipment (Thermo Fisher Scientific) using a 2 × 75 base pair (bp) pair-end protocol. Eight libraries were sequenced, and 34–60 million pairs were generated. The number of reads mapped to the annotated genomic function was quantified from the BAM file using the function number of the Subread package (http://subread.sourceforge.net/). Differential expression was determined via linear modeling based on Bioconductor (ver3.11) and the linear model for microarray data (LIMMA) [18]. Genes with p values <0.0001 were considered as "differential expressed genes" (DEGs), and gene set enrichment analysis (GSEA) was performed (https://www.gsea-msigdb.org/gsea/index. jsp). Pathway analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Additionally, we analyzed the protein–protein interactions of DEGs and visualized them with Cytoscape (ver 3.8.1) to identify the "hub genes.” The hub genes were pre-evaluated using an online software named R2: Genomics Analysis and Visualization Platform (https://hgserver1.amc.nl/cgi-bin/r2/main.cgi) using the gene expression and prognostic data from TCGA. To assess whether the expression of the selected hub gene correlates with other clinicopathological factors, including prognosis, immunohistochemistry (IHC) was used for verification.

Immunohistochemical analysis of ITGB1

ITGB1 levels were measured by IHC using mouse monoclonal anti-human ITGB1 protein antibody (4B7R, 1:100; Santa Cruz Biotechnology, Dallas, TX, USA). Five micrometers thick sections were obtained from formalin-fixed, paraffin-embedded tissues using a VENTANA Optiview DAB Universal Kit (Roche, Basel, Switzerland) and a VENTANA BenchMark ULTRA automated slide stainer (Roche, Bazel, Switzerland). Enzyme-induced antigen retrieval was performed using ISH Protease 1 (Roche, Basel, Switzerland) for 32 min at 36°C, and the primary antibody of ITGB1 was applied to the sample for 120 min at 36°C.

The percentage of stained tumor cells and the intensity of the staining for ITGB1 were evaluated by two pathologists. The expression status of these proteins (low/high) was determined by the IHC score as the product of the percentage and intensity of tumor cells with any membrane staining.

IHC scoring of ITGB1 and related definitions

ITGB1 staining is generally observed in vascular smooth muscle tissue, and the levels of staining in this area were considered as controls. In addition, the percentage of tumor cells stained was scored as follows: 0%, 0; >0 to ≤20%, 1; >20% to ≤40%, 2; >40% to ≤60%, 3; >60% to ≤80%, 4; >80%, 5; and 100%, 6. The staining intensity of tumor cells was scored from 0 to 3 as follows: no staining at all, 0; staining at an intensity lower than the control, 1; staining at the same level as the control, 2; staining at an intensity higher than the control, 3. The product of the scores for the percentage of stained tumor cells and the staining intensity was calculated, and ITGB1 expression in IHC of the tumor cell tissue was scored.

Spearman’s correlation coefficient values were used to examine the correlation between IHC expression scores and RNA-sequencing expression. Cases with an IHC expression score higher than the mean RNA expression level of ITGB1 using a regression line were defined as the high expression group of ITGB1.

Definitions of variables for clinicopathological factors and statistical analysis

The significance of the correlation between the RNA expression level of ITGB1 using RNA-seq and the IHC score of ITGB1 using immunohistochemistry was evaluated using the Spearman’s rank correlation coefficient (r, ρ). Furthermore, the significance of the difference between ITGB1 expression and some clinical and pathological variables was calculated using the χ² test, Fisher’s exact test, or the Mann–Whitney U test. Overall survival (OS) was defined as the time from surgery to the final observation of survival. Disease-free survival (DFS) was defined as the time between surgery and the confirmation of recurrence. Survival curves were created using the Kaplan–Meier method, and the log-rank test was used to assess significant differences and determine key factors. A multivariate analysis was performed using the Cox regression model. Statistical significance was set at p <0.05.

Results

Patient backgrounds

Between January 2013 to March 2018, 138 patients were pathologically diagnosed with PDAC after surgical removal. Of these, 114 patients underwent surgery without preoperative chemotherapy or radiation therapy. In three cases, intraductal papillary mucinous carcinoma (IPMC) with an infiltrative component was diagnosed, and the infiltration site was too small; therefore, the residual sample could not be evaluated. We excluded three cases because distant metastasis was detected during the operation or because it was complicated by multi-organ cancer. One more patient, who was referred from another hospital, was excluded because of recurrence of residual pancreatic cancer. Thus, a retrospective study was conducted on 107 of the 138 patients. The biobank at our hospital included frozen specimens for nine patients. For five patients, the biobank had stocked specimens of only cancer tissue but had both cancer and normal tissue stocked for the other four patients. Specimens for six other cases were obtained during the operation, making the total specimens available 15, of which, 10 were pairs and only 5 were cancer tissues. We attempted to extract RNA from 10 pairs of cancerous and normal tissues and five cases of cancer tissue alone. Out of those 10-pair specimens, only eight pairs and two cancer tissues passed the quality check with a RIN value ≥ 7.0. All five cases with only the cancer tissues showed RIN values ≥7.0. One pair of biobank specimens was excluded because both were possible normal pancreatic tissue. One pair of specimens obtained during the operation was excluded because both were possible cancer tissue. One pair of that was excluded because later, the pathological result was found to be adenosquamous carcinoma.

A total of 17 samples from 12 patients, including five pairs of cancer and normal tissues and seven samples of only cancer tissue, were subjected to NGS. RNA-sequencing results were verified by IHC using the above 107 samples. The observation period was from January 2013 to July 2020, with a median period of 804 days (58–2,481 days). The median age was 70 years (50–87 years). The male-to-female ratio was 60:47 (Table 1). Curative resection R0 occurred in 89 cases, and the histological types were good, moderate, and poor in 46, 53, and 8 cases, respectively (Table 1). Lymph node metastasis was observed in 76 patients. Among the common T-factors in the TNM classification by The Union for International Cancer Control (UICC) (8th edition), T2 (2 cm< max tumor diameter ≤4cm) was the most common (60 cases). In the TNM classification (UICC 8th), stage III was the most common (39 cases), followed by stage II (37 cases) (Table 1).

Table 1. Relationship between clinocopathological parameters and ITGB1 status.

	ITGB1 status				P value
Expression type	low N (%)		high N (%)
Sex
Male	35	(32.7%)	25	(23.4%)
Female	32	(29.9%)	15	(14.0%)	0.25^*
Age
70 (50–87)	69 (51–83)		74 (50–87)		0.037^**
Preoperative CEA
3.3 (0.5–47.3)	3.1 (0.5–28.5)		3.4 (0.8–47.3)		0.384^**
Preoperative CA19-9
137.4 (0–47588.2)	90.1 (0–19447)		481.8 (0–47588.2)		0.039^**
Operation type
PD	46	(43.0%)	24	(22.4%)
DP	21	(19.6%)	14	(13.1%)
TP	0	(0.0%)	2	(1.9%)	0.209^***
Cytology
negative	59	(55.1%)	34	(31.8%)
positive	8	(7.5%)	6	(5.6%)	0.65^*
Margin status
R0	58	(54.2%)	31	(29.0%)
R1	8	(7.5%)	8	(7.5%)
R2	1	(0.9%)	1	(0.9%)	0.423^***
Differenciation
well	30	(28.0%)	16	(15.0%)
moderate	31	(29.0%)	22	(20.6%)
poor	6	(5.6%)	2	(1.9%)	0.594^***
Interstitium type
int	62	(57.9%)	36	(33.6%)
med	1	(0.9%)	0	(0.0%)
sci	4	(3.7%)	4	(3.7%)	0.670^***
lympathic invasion
negative	19	(17.8%)	10	(9.3%)
positive	48	(44.9%)	30	(28.0%)	0.705^*
vascular invasion
negative	1	(0.9%)	0	(0.0%)
positive	66	(61.7%)	40	(37.4%)	0.438^*
neural invasion
negative	4	(3.7%)	2	(1.9%)
positive	63	(58.9%)	38	(35.5%)	0.722^*
Lymph node metastasis
negative	19	(17.8%)	12	(11.2%)
positive	48	(44.9%)	28	(26.2%)	0.856^*
p Max diameter (cm)
3.3 (1.0–11.6)	3.3 (1.5–8.5)		3.3 (1.0–11.6)		0.821^**
Postoperative adjuvant chemotherapy
yes	15	(14.0%)	12	(11.2%)
no	52	(48.6%)	28	(26.2%)	0.381^*
pT (UICC) 8th
T1	12	(11.2%)	7	(6.5%)
T2	38	(35.5%)	22	(20.6%)
T3	17	(15.9%)	11	(10.3%)	0.971^***
pStage (UICC 8th)
A	7	(6.5%)	5	(4.7%)
B	9	(8.4%)	5	(4.7%)
A	3	(2.8%)	2	(1.9%)
B	23	(21.5%)	14	(13.1%)
Ⅲ	25	(23.4%)	14	(13.1%)	0.996^***

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*The significance of the difference between ITGB1 and ITGAV expression and several clinical and pathologic variables was assessed by the χ² test.

**The significance of the difference between ITGB1 and ITGAV expression and several clinical and pathologic variables was assessed by the Mann–Whitney U test.

***The significance of the difference between ITGB1 and ITGAV expression and several clinical and pathologic variables was assessed by Fisher’s exact test.

RNA sequencing

Among the 11,272 mapped mRNAs, 314 genes were differentially expressed in cancer tissues compared to the adjacent normal tissues (S1 Fig). When these genes were analyzed by the KEGG pathway analysis using GSEA, the significant pathways detected were (in order): Extracellular matrix (ECM)-receptor interaction, focal adhesion, protein digestion and absorption, phagosome, and the phosphatidylinositol 3-kinase-alpha serine/threonine-protein kinase (PI3K-Akt) signaling pathways (S1 Table). The top five pathways included 37 DEGs, including ITGB1, collagen 4 alpha 1 (COL4A1), COL4A2, integrin alpha 5 (ITGA5), integrin alpha V (ITGAV), COL1A1, and COL1A2 (Fig 1). Network analysis using Cytoscape (ver. 3.8.0) was performed on these DEGs, and the hub gene was found to be ITGB1 (Fig 1). Examination of the relationship between gene expression and prognosis using the R2 platform showed that ITGB1 was significantly correlated with the prognosis of pancreatic cancer (p = 0.036), but COL4A1 (p = 0.084), COL4A2 (p = 0.121), and ITGA5 (p = 0.285) were not (Fig 2). ITGB1 expression (with p <0.05) was verified by immunostaining.

Fig 1 — In the DEGs mapped to the top five pathways, protein-protein interaction analysis was performed, and the network was constructed by Cytoscape (ver. 3.8.0). In this network, the DEGs are called nodes, and the correlated nodes are connected by lines called edges. Furthermore, in this network, the node with the most edges was called the hub gene, suggesting a clinically significant possibility. Second to the top hub genes with 22 and 21 edges, respectively, were selected from the network analysis. *ITGB1*, *COL4A1*, *COL4A2*, and *ITGA5* were detected.

Fig 2 — The four hub genes detected by protein-protein interaction analysis were pre-verified for prognosis using the R2 platform, an open-source external databank. Prognostic analysis with the R2 platform using TCGA showed *ITGB1* to be significantly involved in the deterioration of prognosis (p = 0.036). The expression of *COL4A1*, *COL4A2*, and *ITGA5* was not significantly correlated with poor prognosis (p = 0.084, 0.121. 0285).

IHC scoring of ITGB1

The stromal tissue of the tumor samples was stained uniformly for ITGB1 in all cases, with a slightly weaker intensity than that of the surrounding normal pancreatic tissue. The tumor cell IHC scores of ITGB1 were between 0–18 (median = 7) (Fig 3).

Fig 3 — For ITGB1 IHC analysis, the cell membrane and all vascular smooth muscle were stained in the positive control tissue. Staining levels in vascular smooth muscle were used as controls (Magnification = 160 X, 160 X, and 40 X, respectively).

Correlation between IHC score and RNA-sequencing

For ITGB1, the IHC score tended to correlate with RNA-seq expression, but the difference was not significant (r = 0.552, ρ = 0.476, p = 0.118). The median ITGB1 expression level was 9.22. Since the IHC score corresponding to the median ITGB1 expression level in NGS was 10.5 with the regression line, an IHC score ≥ 11 indicated high ITGB1 expression (Fig 4).

Fig 4 — IHCs scores were set on the X-axis, the RNA-sequencing expression levels were set on the Y-axis, and the correlation was graphed. Although the IHC score and RNA-sequencing expression tended to have a correlation, it was not significant (r = 0.552, ρ = 0.476, p = 0.118). For these relationships, a regression line was created, and the IHC score corresponding to the median RNA-expression level was calculated to be 10.5. Therefore, an IHC score ≥11 signified high *ITGB1* expression.

Relationship between IHC status and clinicopathological factors

High ITGB1 expression was observed in 40 patients (37.4%). The patients in the ITGB1 high-expression group were significantly older and had higher CA19-9 levels (p = 0.037 and 0.039, respectively), but other clinicopathological factors such as preoperative tumor marker levels and lymph node metastasis were not significantly different (Table 1).

Relationship between clinicopathological factors and the prognosis and recurrence of pancreatic cancer

The presence of the tumor marker CA19-9 was associated with significantly worse OS and DFS (CA19-9: p = 0.003 and <0.001, respectively). Similarly, positive nerve infiltration, tumor diameter, T factor, and lymph node metastasis worsened both OS and DFS. In addition, surgical procedure, operation time, bleeding volume, vascular infiltration, and postoperative adjuvant chemotherapy group significantly correlated with OS, and the histological type and lymphatic vessel infiltration correlated with DFS. (Table 2). In the ITGB1 high-expression groups, the prognosis of pancreatic cancer, along with lymph node metastasis, T factor, and tumor markers, was significantly worse (p = 0.035). Likewise, the ITGB1 high-expression group showed a significantly worse recurrence rate (p = 0.028) (Table 2) (Fig 5).

Table 2. Univariate analysis of prognostic factors with ITGB1 for OS and DFS.

Variable	No. of Patients (%)	Univariate analysis for OS		Univariate analysis for DFS
		Median (95% confidence interval)	Log-Rank	Median (95% confidence interval)	Log-Rank
		(days)	(P value)	(days)	(P value)
Gender
Male	60 (56.1)	864 (622–1065)	0.527	391 (260–575)	0.760
Female	47 (43.9)	990 (494–1324)		356 (223–498)
Age
≥ 70	55 (51.4)	1155 (730–1276)	0.299	408 (282–561)	0.949
< 70	52 (48.6)	804 (515–963)		277 (247–458)
Follow-up (days)
Median	804
Range	58–2481
preoperative CEA
≤ 3.3	54 (50.0)	1156 (730–1324)		402 (279–737)
> 3.3	53 (50.0)	804 (572–911)	0.110	302 (225–458)	0.400
preoperative CA-19-9
≤ 137.4	54 (50.0)	1175 (817–1487)		594 (373–777)
> 137.4	53 (50.0)	572 (393–866)	0.003	252 (164–306)	< 0.001
Operation type
PD	70 (65.4)	730 (534–942)		307 (256–455)
DP/TP	37 (34.6)	1324 (864–NA)	0.007	458 (263–832)	0.113
Operation time
≤ 311	55 (51.4)	1175 (817–1512)		428 (298–641)
> 311	52 (48.6)	711 (515–911)	0.003	280 (243–498)	0.087
Bleeding volume
≥ 600	54 (50.5)	1065 (7461512)		407 (282–575)
> 600	53 (49.5)	777 (560–990)	0.043	280 (243–498)	0.123
Cytology
CY0	91 (88.3)	905 (746–1175)		395 (298–526)
CY1	12 (11.7)	454 (251–NA)	0.159	208 (106–484)	0.056
Margin status
R0	86 (83.5)	864 (711–1175)		356 (268–498)
R1/R2	17 (16.5)	866 (455–1212)	0.612	380 (135–575)	0.117
Differentiation
Well	47 (43.9)	1187 (800–1512)		498 (282–839)
Moderate/Poor	60 (56.1)	777 (534–963)	0.055	298 (243–408)	0.035
Lymphatic invasion
Negative	29 (28.0)	1243 (746–1881)		746 (282–NA)
Positive	78 (72.0)	817 (615–979)	0.122	304 (253–408)	0.001
Neural invasion
Negative	6 (6.5)	NA (1175–NA)		NA (280–NA)	0.020
Positive	101 (93.5)	817 (656–990)	0.022	356 (263–458)
Vascular invasion
Negative (v0/1)	21 (19.6)	1512 (560–NA)		455 (279–1064)
Positive (v2/3)	85 (80.4)	823 (711–990)	0.039	356 (256–484)	0.147
Interstitium type
int	98 (91.6)	905 (735–1156)		360 (279–484)
med + sci	9 (8.4)	396 (248–1881)	0.223	209 (57–NA)	0.807
p Max diameter (cm)
≤ 3.3	57 (50.0)	1155 (804–1324)		561 (312–777)
>3.3	50 (50.0)	711 (454–866)	0.033	263 (160–356)	0.003
Lymph nodes
Negative	31 (29.0)	1512 (990–NA)		962 (455–NA)
Positive	76 (71.0)	735 (534–866)	< 0.001	271 (209–373)	< 0.001
T factor (UICC 8th)
T1/2	79 (76.7)	990 (804–1276)		455 (302–641)
T3	28 (26.2)	541 (304–864)	0.010	217 (144–343)	0.024
Postoperative adjuvant chemotherapy
yes	80 (74.8)	942 (804–1243)		395 (298–526)
no	27 (25.2)	599 (248–963)	0.045	225 (106–455)	0.143
Stage (UICC 8th)
A	12 (11.2)
B	14 (13.1)
A	5 (4.7)
B	37 (34.6)	1187 (817–1487 (Ⅰ,Ⅱ))	< 0.001	498 (312–764 (Ⅰ,Ⅱ))	< 0.001
Ⅲ	39 (36.4)	560 (362–823 (Ⅲ))	(Ⅰ,ⅡvsⅢ)	256 (160–386 (Ⅲ))	(Ⅰ,ⅡvsⅢ)
ITGB1 status
low	67 (62.6)	1155 (800–1276)		458 (308–737)
high	40 (37.4)	656 (447–866)	0.035	247 (170–312)	0.028

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Fig 5 — We performed immunostaining for ITGB1 in 107 patients diagnosed with PDAC who underwent radical resection without preoperative chemotherapy. Forty cases had an IHC score ≥11. The Kaplan–Meier curves for overall and recurrence-free survival are shown. IHC scores ≥11 significantly worsened survival time and recurrence-free survival time (p = 0.035, 0.028).

Evaluation of prognosis and recurrence predictors by multivariate analysis

All the following factors—ITGB1 expression, CA19-9, operation time, operation type, bleeding volume, vascular invasion, neural invasion, lymph node metastasis, tumor diameter, and T factor—were significantly associated with poor prognosis of pancreatic cancer. However, the tumor diameter was excluded because it was confounded with the T factor. Multivariate analysis performed with eight of these factors showed that ITGB1 expression, surgical procedure, nerve infiltration, T factor, and lymph node metastasis were independent prognostic factors (Table 3). Similarly, ITGB1, ITGAV, CA19-9, differentiation, lymphatic invasion, neural invasion, tumor diameter, T factor, and lymph node metastasis were all significantly correlated with the pancreatic cancer recurrence rate, and multivariate analysis with eight of these factors showed that ITGB1, neural invasion, T factor, and lymph node metastasis were independent recurrence factors (Table 3).

Table 3. Multivariate analysis of prognostic factors for OS and DFS.

	OS			DFS
Variables	Hazard Ratio	95% Confidence Limit	P value	Hazard Ratio	95% Confidence Limit	P value
preoperative CA-19-9
≤ 137.4 (n = 54)	1			1
> 137.4 (n = 53)	1.761	1.065–2.932	0.028	1.717	0.981–3.020	0.058
Operation type
PD (n = 70)	1
DP/TP (n = 37)	0.393	0.199–0.768	0.006	NA
Operation time
≤ 311 (n = 55)	1
> 311 (n = 52)	1.027	0.547–1.883	0.930	NA
Differentiation
Well (n = 47)				1
Moderate/Poor (n = 60)	NA			1.273	0.770–2.409	0.303
Lymphatic invasion
Negative (n = 29)				1
Positive (n = 78)	NA			1.805	0.875–3.999	0.112
Neural invasion
Negative (n = 6)	1			1
Positive (n = 101)	3.960	1.086–25.789	0.035	5.323	1.358–36.153	0.014
Lymph node
negative (n = 31)	1			1
positive (n = 76)	2.694	1.464–5.254	0.001	3.015	1.560–4.760	<0.001
T factor (UICC 8th)
T1/2 (n = 79)	1			1
T3 (n = 28)	2.326	1.317–4.014	0.004	2.126	1.171–3.794	0.014
ITGB1 status
low (n = 64)	1			1
high (n = 43)	1.912	1.102–3.297	0.022	1.914	1.110–3.262	0.020

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Discussion

In this study, we were able to understand how the dynamics of gene expression in cancer tissues are associated with clinicopathological factors. We evaluated the expression of ITGB1, a factor that has been reported to contribute to the infiltration and metastasis of various carcinomas. Using transcriptome analysis of pancreatic cancer tissues, we confirmed ITGB1 to be an independent prognostic factor in pancreatic cancer.

ITGB1 is a constituent of integrin molecules. It forms heterodimers with β subunits consisting of integrin β chains and α subunits consisting of integrin α chains [9]. Integrin is mainly present in the plasma membrane. It is involved in cell-cell adhesion, cell-extracellular matrix adhesion, and signal transduction. It has been confirmed that there are eight types of β subunits and 18 types of α subunits, and ITGB1 forms β subunits and dimers with various α subunits which adhere to collagen, fibronectin, and vitronectin. These connective tissue proteins, in turn, constitute the interstitium and laminin and form the basement membrane [9,10]. While it was reported that it functioned as a cell by “construction of scaffolds” with integrin and by “receive of survival signal” through adhesions with integrin, loss of these scaffolds causes an exfoliation-induced apoptotic process called "anoikis" [11]. Cancer cells have been reported to avoid "anoikis" through integrins, which are involved in proliferation, migration, infiltration, and metastasis [11]. In pancreatic cancer, some reports indicated that ITGB1 is distributed as α2β1 and α5β1 in tumor cells and binds to the basement membrane and extracellular matrix [19]. It also regulates cytokine secretion, activates intracellular signal transduction, causes cell proliferation and infiltration, and regulates protein production in the matrix [19,20].

High expression of ITGB1 is associated with a poor prognosis of colorectal, lung, and breast cancer, cancer recurrence, and cancer angiogenesis [12–17]. The same is true for pancreatic cancer, and a few studies reported that high protein and gene expression of ITGB1 is positively correlated with a poor cancer prognosis [21–26]. A meta-analysis was performed by summarizing these studies [27]. The meta-analysis summarized reports of the association between ITGB1 expression and prognosis. Among the accumulated reports, two reports of immunohistochemical staining for pancreatic cancer were found [25,26]. Of these, the study by Sawai et al. used 78 pancreatic cancer patient specimens and investigated the association between ITGB1 expression and prognosis by immunohistological staining [26]. Their results, unlike ours, did not show a significant correlation between ITGB1 expression and prognosis [26]. However, in their report, about 20 postoperative cases of stage IV simultaneous liver metastasis were included, and the background of the patients was significantly different from that of ours, which targeted radical resection cases [26]. Also, the method of evaluating immunohistological staining was different between us and them. Yang et al. Targeted only R0, R1 resectable pancreatic cancer, and our study was consistent with the target cases [25]. In addition, as a result of investigating the relationship between ITGB1 expression and prognosis in 63 cases, the prognosis was poor in the high expression group as in our result [25]. In our study, the number of target cases was about twice as many, and the results conformed to their results.

The known preoperative tumor markers CA19-9 and CEA, which are potential prognostic factors, have cutoffs of 37 U/mL and 3 U/mL or 5 U/mL, respectively [28]. It was reported that high preoperative marker levels can be utilized as prognostic factors but not as therapeutic targets. In this study, we divided the median into two groups, and the inspection cutoffs for CA19-9 and CEA at our facility were 37 U/mL and 5 U/mL, respectively. When examined, high CA19-9 values were significantly correlated with DFS (OS; CA19-9>37.0, CEA>3.0, CEA>5.0 p = 0.051, 0.079, and 0.233, respectively, DFS; CA19-9>37.0, CEA>3.0, CEA>5.0 p = 0.001, 0.286, and 0.356, respectively), but in the multivariate analysis, CA19-9 was not an independent factor (p = 0.141). The high expression of the ITGB1 protein in tumor cells is reported to be 32.4% [21], but no clear threshold is known for the high and low expression of ITGB1 in cancer tissues [29–33].

In this study, deterioration of OS and DFS was observed in the high ITGB1 expression group of tumor cells. In the interstitial area, staining was uniformly observed in all cases, and the pancreatic stellate cells and fibroblasts were stained. In pancreatic cancer, it has been reported that high expression of ITGB1 is involved in the migration of cancer cells [34], and that high expression of ITGB1 and ITGA3 confers resistance to gemcitabine by increasing integrin α3β1 signaling [35]. Furthermore, integrin is involved in promoting infiltration and metastasis in lung and breast cancer; hence, the therapeutic strategies targeting integrins for cancer treatment are being developed [36,37]. Integrin-targeted treatment may facilitate the treatment of pancreatic cancer, as ITGB1 has been reported to inhibit cell proliferation, infiltration, and migration in pancreatic cancer [24,37–40].

Various integrin antagonists, such as α5β1 and αVβ3, are in the research stages, and their antitumor effects have been reported in breast cancer in vitro [29,41]. In clinical trials, there are currently no reports showing that a single integrin inhibitor is effective, but they are expected to be effective in combination with multiple agents such as immune checkpoint inhibitors (NCT00195278 and NCT04508179) [42,43]. It is expected that ITGB1 will contribute to markers and treatment in pancreatic cancer if the development, research, and clinical application of these drugs progress in the future.

However, our results were different from another similar study. The transcriptome study by Bailey et al. focused on specimens with ≥40% tumor cells and performed deep sequencing of 40% or less, and clarified the relationship between gene mutation and gene expression, and clustered and typed gene expression patterns, and KRAS, TP53, CDKN2A, and SMAD4 were defined as gene mutations by exome analysis [6,8]. Their pathway analysis in exsome results are also different from those of our study, showing the WNT signaling, TGF-β signaling, and cell cycle as the top pathways [8]. A meta-analysis of PDAC transcriptome analysis including tissue microarray demonstrated results that are similar to ours, reporting that ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, and cancer pathways were significant pathways as well [44,45]. We assume that this difference in pathway analysis might be attributed to the fact that our analysis was influenced by the interstitium as the tumor tissue has more stroma compared to the normal tissues surrounding the tumor. Furthermore, our study had some limitations. The small number of samples used for sequencing may have been insufficient to verify the correlation with IHC. Furthermore, this was a retrospective study conducted at a single institution. For more accurate results, future studies need to be conducted prospectively and with more samples.

Conclusion

Bioinformatics analysis of RNA sequencing data for pancreatic cancer identified ITGB1 as an important hub gene. Immunohistochemical staining with multiple samples showed that both DFS and OS were significantly shorter in the groups showing high ITGB1 expression and were independent predictors of prognosis and recurrence in multivariate analysis. In addition, there has never been a report showing a causal relationship between mRNA expression by NGS and protein expression by IHC in the gene expression of ITGB1 for PDAC, and this may also be a significant report.

Supporting information

S1 Fig. Heatmap for 314 DEGs.

Genes with differential expression between the PDAC tissue and its adjacent pancreatic tissue were mapped and visualized on a heat map.

(TIF)

Click here for additional data file.^{(26.7MB, tif)}

S1 Table. Results for KEGG pathway analysis.

KEGG pathway analysis was performed on 314 DEGs, and a P value of 0.01 or less was considered significant, and 20 pathways were detected.

(XLSX)

Click here for additional data file.^{(12.8KB, xlsx)}

Data Availability

All RNA-seq results are available from Gene Expression Omnibus (accession number GSE196009).

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0268630.r001

Decision Letter 0

Eithne Costello

4 Jan 2022

PONE-D-21-36900Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancerPLOS ONE

Dear Dr. Hoshino,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.<ul> <li>

1. In your covering letter, you state that ‘there is a lack of transcriptomic data for pancreatic cancer tissues from the East Asian and Japanese population. Hence, in this study, we performed next generation sequencing of pancreatic cancer samples from the Japanese patients, and further validated the sequencing results with immunohistochemical analysis’.

However, the gene that you have chosen to follow up on (ITGB1) has been specifically studied in a Japanese population (which you cite). Taniuchi K, Furihata M, Naganuma S, Sakaguchi M, Saibara T. Overexpression of PODXL/ITGB1 and BCL7B/ITGB1 accurately predicts unfavourable prognosis compared to the TNM staging system in postoperative pancreatic cancer patients. PLOS ONE. 2019;14: e0217920.

I cannot find a clear statement in the methods section as to the name of the hospital from which your samples were obtained. Is there overlap between your patients/samples from those of the Taniuchi PLOS ONE. 2019 study?

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Reviewer #1: PLOS one

Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer.

Using transcriptomic analysis Iwatate et al. have demonstrated that ITGB1 is highly expressed in a subset of patients with pancreatic cancer and this expression correlates with a poor prognosis. The authors used immunohistochemical analysis to support this finding.

The authors, state that ITGB1 is ‘an important gene for pancreatic cancer metastasis, progression and prognosis’ and that it ‘may be used as a drug target for pancreatic cancer’. These statements are somewhat supported by the data shown here, and is backed up by previously published work from other groups, but more in vitro and in vivo work would ultimately be needed to test these theories.

This work isn’t novel as it has previously been shown that ITGB1 is associated with poor survival previously (Sun et al. 2018), however, I appreciate that the work done here has been carried out on the under-sequenced Japanese population.

I have outlined some major and minor points below that would need to be addressed before this manuscript is accepted.

Major points

• Tables 1, 2 and 3 are missing from the manuscript and would need to be reviewed before acceptance.

• Concurrent with recent work by Sun et al. 2018 (Prognostic value of increased integrin-beta 1 expression in solid cancers; a meta-analysis). It would be good to show on the data set here the correlation with ITGB1 and OS (this may be shown in the missing figures), and discuss any differences.

• Figure 3. Images are of poor quality, with no scale bar making it difficult to interpret what is happening. The legend says that magnification was 160X – this doesn’t look to be correct. More description both on the figure and in the legend is required here.

• Figure 3. It had been mentioned that the tumour cell IHC scores were between 0-18. This data is not shown. Which sample set was this carried out on? The retrospective 107 patient samples or the 17 samples that were sequenced. Additionally, according to the methods, the maximum histoscore that could be achieved would be >80% (5) x staining higher than the control (3) = 15. However in the next the scores were between 0-18. This needs clarifying.

Minor points

• Ethics statement is missing from the author checklist (although it is present in the materials and methods.

• Sequencing data is not publicly available, with no reason given.

• ‘Transcriptomic analysis of pancreatic cancer samples from the East Asian and Japanese population are lacking’. I appreciate that this is a novel data set that has been sequenced here, however, how does it compare to previous data sets? Are there specific genes/pathways differentially expressed here? Could this set be used for this?

• I’m not sure why the ‘traditional’ method of determining a histoscore wasn’t used here (intensity of 1-3 and % of tissue stained, giving a maximum histoscore of 300, compared to 15).

• Figure 2. Text mentions that ITGB1 was significantly correlated with the prognosis of pancreatic cancer and COL4A1 and COL4A2 were not. Data isn’t shown.

• Figure 4. The r values for the correlation between the RNA-seq and the IHC do not match up between figure and text (p=0.552 and p=0.542).

• Figure 4. It is unclear to what data the p values are relating. Additionally, 9.07 was mentioned on the graph, with no explanation in the figure legend. I’m assuming mean expression?

• Discussion – difference between the authors paper and that of Bailey et.al; the authors claim that the difference may be due to differences in the stromal content of the samples. Samples in the Bailey paper had a high epithelial content ≥40% (not 50% as stated in this manuscript). What is the stromal content of the samples used here?

• Overall the paper would benefit from being proof read

Typos

• RNA sequencing; the authors referred to a p value of 1/10000. The standard nomenclature would be p<0.0001

• IHC scoring of ITGB1 and related definitions; ‘Cases with an IHC expression score higher than the mean RNA expression level of ITGB1… Here it should be italicised due to references RNA expression.

• Patient backgrounds; IPMC is mentioned fir the first time, but hasn’t been defined.

• RNA sequencing; ‘Cytoscape was performed on these DEGs and the hub gene was found to be ITGB1…’ Here it should be italicised. And again in the last sentence of this section.

• Discussion; ‘The study for transcriptome inby Bailey et al.’ ‘inby’ as a typo and et al should be italicised.

Reviewer #2: The work submitted by Iwatate et al. evaluates the role of ITGB1 as a prognostic marker for pancreatic cancer using clinical data as well as transcriptomic analysis by RNAseq and protein expression by IHC. Results are interesting and promising, particularly due to the limited access to clinical samples especially for this type of tumour. Findings are in line with previous work published by others on ITGB1, including other types of cancer, adding value to results obtained using a Japanese cohort. The methodology used is, in general, appropriate and the conclusion is supported by the results presented.

There are, however, a number of points which would need clarification before publication.

-Some extra information on ITGB1 (e.g. its biological function, role/dysregulation in cancer, scheme on pathways regulated by ITGB1 etc) would need to be included in the introduction section to facilitate the understanding of the manuscript. Some general ideas appear in the discussion, but further details need to be added to the introduction, including its clinical relevance.

-Sadly, I could not find Tables 1-3 mentioned in the manuscript in the online system nor in the PDF of the manuscript. Please double check Tables are included in the main text as they are key to follow the results section. Did the authors classify their samples on classical or basal-like types to correlate this with the RNAseq results? Did the authors indicate the stage of disease in the table and its correlation with ITBG1 expression? And the type of postoperative adjuvant chemotherapy?

-All figure legends need to be improved for the readers to be able to understand the figures.

-Results: The authors acknowledge as a limitation of their study the low number of samples analyzed by RNAseq. Different publications have supported the idea that RNAseq can also be performed in fixed tissues (e.g. https://pubmed.ncbi.nlm.nih.gov/31059554/, among others). Because a good correlation between RNAseq data and IHC results could not be accomplished in this study. Did the authors try to perform RNAseq in fixed tissue samples to evaluate if the results were more similar to the obtained IHC data? Please, if possible, add these data in the revised manuscript, and elaborate this in the discussion section, indicating if the observed differences could have been due to comparing RNAseq results obtained from fresh or frozen tissue versus formalin-fixed tissue for IHC. Compare these results with other published data.

-Please include a heatmap showing the RNAseq results for the 12 samples analysed.

-R2 platform analysis (page 11): did the authors also examine ITGAV, COLA1 and COLA2? Please include these details as done for the rest of DEGs.

-Figure 2: Please include Kaplan Meier curves for the rest of genes, not only for ITGB1.

-Quantification of ITGB1 level and intensity in IHC seems to be very dependent on the pathologist evaluating the samples. It is not clear if this could have had an impact on the results obtained by the authors. Did the authors think of performing western blotting to quantify protein expression in their samples and validate if they match the IHC data?

-Fig 3 IHC: the quality of the image showing staining in tumour tissue and adjacent normal pancreas tissue is not great. Please revise it and include further samples in the revised manuscript. For example, samples from different stages of the disease, with high and low ITGB1expression etc. Scale bars are missing in Fig 3. Please include them.

-The Discussion section would benefit from extra information on current and potential/promising prognostic markers for pancreatic cancer used in the clinic (including further details on CA19-9, such as what are considered normal or high levels etc). Also, please expand the information on clinical trials targeting integrins (page 17), including NCT number.

Minor comments:

-Acronyms need to be explained in the text the first time they appear (e.g. ECM, ITGB1, COL etc)

-Abstract: please clarify that the 12 samples analysed by RNAseq were fresh or frozen samples and not fixed tissue obtained from naïve (untreated) patients. Also please indicate that immunostaining was perform only for ITGB1 and not for all the genes identified by RNAseq.

-Page 7, line 6: please indicate what clinicopathological factors authors are referring to.

-Please indicate the concentration of the ITGB1 antibody used for IHC stainings.

-Please explain what T-factors or T2 are (page 11).

-Page 17, line 15: please clarify what the authors mean by “by case stratification”. Also, this affirmation “Although the influence of ITGB1 in pancreatic cancer has not been reported, it is expected that ITGB1 will contribute to markers and treatment in pancreatic cancer” is not clear as the authors previously highlight that the role of ITGB1 in pancreatic cancer has been previously described by others.

-Please revise the English grammar before re-submitting the manuscript. Thank you.

**********

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PLoS One. 2022 Jun 1;17(6):e0268630. doi: 10.1371/journal.pone.0268630.r002

Author response to Decision Letter 0

11 Feb 2022

Dear Dr. Hoshino,

•

→Comments from our author

Thank you very　much　for your understanding.　

As you said, I feel that you are right.　

We knew that large-scale comprehensive research using next-generation sequencers was being conducted in the world (in U.S.A., Australia and so on) for pancreatic cancer. However, since there are few data using the next-generation sequencer for pancreatic cancer, especially in Japan, including East Asia, we thought that the study in the population in Japan would be useful this time. Furthermore, we decided to set a cutoff by clarifying the correlation between the results of the next-generation sequencer and the expression of immunostaining. As a result, we believe that the results of immunostaining were obtained as an evaluation with an objective quantitative scale rather than the evaluation of qualitative results such as the presence or absence of staining. The results were similar to those of Taniuchi et al., But by quantifying them using a next-generation sequencer or immunostaining, the usefulness of ITGB1 could be supplemented as a more objective result than the results of Taniuchi et al. I am confident that I was able to do it.

・I cannot find a clear statement in the methods section as to the name of the hospital from which your samples were obtained. Is there overlap between your patients/samples from those of the Taniuchi PLOS ONE. 2019 study?

Please state exactly where your samples are form in the Methods Section.

→Comments from our author

Thank you very much for your suggestion.

I mentioned in the Materials and Methods section that all samples were taken at the Chiba Cancer Center.

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: PLOS one

Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer.

I have outlined some major and minor points below that would need to be addressed before this manuscript is accepted.

Major points

• Tables 1, 2 and 3 are missing from the manuscript and would need to be reviewed before acceptance.

→Comments from our author

Thank you for your advice. I must sincerely apologize.

We have added Tables 1, 2 and 3.

→Comments from our author

Thank you for your suggestion.

We quoted the "Quanwu Sun, et al Prognostic value of increased integrin-beta 1 expression in solid cancers: a meta-analysis. Onco Targets Ther. 2018; 11: 1787–1799.” meta-analysis and added it to the Discussion as follows. We have also added a figure (Fig. 5) for the results of our OS and DFS.

A meta-analysis was performed by summarizing these studies [27].The meta-analysis that summarized reports of the association between ITGB1 expression and prognosis. Among the accumulated reports, two reports of immunohistochemical staining for pancreatic cancer were found [25,26]. Of these, the study by Sawai et al. used 78 pancreatic cancer patient specimens and investigated the association between ITGB1 expression and prognosis by immunohistological staining [25]. Their results, unlike ours, did not show a significant correlation between ITGB1 expression and prognosis. However, in their report, about 20 postoperative cases of stage IV simultaneous liver metastasis were included, and the background of the patients was significantly different from that of ours, which targeted radical resection cases. Also, the method of evaluating immunohistological staining was different between us and them. Yang et al. Targeted only R0, R1 resectable pancreatic cancer, and our study was consistent with the target cases [26]. In addition, as a result of investigating the relationship between ITGB1 expression and prognosis in 63 cases, the prognosis was poor in the high expression group as in our result [26]. In our study, the number of target cases was about twice as many, and the results conformed to their results.

→Comments from our author

Thank you for your advice.

Also, I am very sorry. The micrographs of high and low expression of ITGB1 subjected to immunostaining were 160 X, and the contrast photographs with the adjacent normal pancreatic tissue were 40 X. I have attached a scale for the sake of clarity.

Also, the deterioration of image quality is considered to be a problem in the journal review process. The image quality of the original figure can be sufficiently confirmed to the cellular level. If necessary, you need to contact the publisher to confirm the image quality of the figure.

→Comments from our author

Thank you for your advice.

All cases of ITGB1 immunostaining have been successfully performed. We are very sorry. 100% = 6. I did not mention it, so I added it　in manuscript.

Minor points

• Ethics statement is missing from the author checklist (although it is present in the materials and methods.

→Comments from our author

Thank you very much. We have added an Ethics Statement to the checklist.

• Sequencing data is not publicly available, with no reason given.

→Comments from our author

Thank you very much. We are uploading RNA-seq results to Gene Expression Omnibus. The 　accession number is GSE196009.

→Comments from our author

RAW data cannot be obtained from TCGA, etc. at this facility due to ethical review. Processing data is public data and can be obtained. Since it is not raw data, it is not possible to actually calculate the Differential Expression Genes (DEGs) between this study and the TCGA study. In addition, TCGA has only 4 data on adjacent pancreatic tissue, which makes it difficult to calculate the same amount of DEGs as we do. Although it is microarray data, there is a meta-analysis of DEGs identification and pathway analysis by comparison between normal pancreas and pancreatic cancer as in our study. In that study, the integrin family and collagen were identified as in the results of this study, and they are also accumulated in ECM-Receptor Interaction, PI3K-Akt Signaling Pathway, Focal Adhesion, Pathways in Cancer, Pancreatic Secretion, Metabolic Pathways, etc. This is partly consistent with the research in.

Sevcan Atay Integrated transcriptome meta-analysis of pancreatic ductal adenocarcinoma and matched adjacent pancreatic tissues. PeerJ 8: e10141 DOI 10.7717 / peerj.10141

Vandana Sandhu, PhD et al. Meta-Analysis of 1,200 Transcriptomic Profiles Identifies a Prognostic Model for Pancreatic Ductal Adenocarcinoma, Clin Cancer Inform. 2019: DOI https://doi.org/10.1200/CCI.18.00102

• I’m not sure why the ‘traditional’ method of determining a histoscore wasn’t used here (intensity of 1-3 and % of tissue stained, giving a maximum histoscore of 300, compared to 15).

→Comments from our author

Thank you very much. We think it is reasonable that you have pointed out. We also considered using the classic H score (% * intensity) you pointed out, but this time we have referred to　other scoring : e.g. Allred scores and immune responses score (IRS). We used scoring that divides every 20% with reference to these scoring.　Nickolay Fedchenko Janin Reifenrath　： Different approaches for interpretation and reporting of immunohistochemistry analysis results in the bone tissue - a review　 Diagn Pathol.　2014 Nov 29;9:221. doi: 10.1186/s13000-014-0221-9.

• Figure 2. Text mentions that ITGB1 was significantly correlated with the prognosis of pancreatic cancer and COL4A1 and COL4A2 were not. Data isn’t shown.

→Comments from our author

Thank you very much. We added COL4A2 and COL4A2 Kaplan-Maier curves to Fig.2.

• Figure 4. The r values for the correlation between the RNA-seq and the IHC do not match up between figure and text (p=0.552 and p=0.542).

→Comments from our author

Thank you for your advice.

Since r = 0.552, We changed the figure accordingly.

• Figure 4. It is unclear to what data the p values are relating. Additionally, 9.07 was mentioned on the graph, with no explanation in the figure legend. I’m assuming mean expression?

→Comments from our author

Thank you very much. As you said, 9.07 is the average expression level. However, due to data variability, we have adopted the median nonparametric this time. We corrected it and added it to Figure legends. And in order to evaluate the significance of the correlation between the RNA expression level of ITGB1 using RNA-seq and the IHC score of ITGB1 using immunohistochemistry, we calculated the Spearman’s rank correlation coefficient (r, ρ). We have described these in the sections "Correlation between IHC score and RNA-sequencing" and "Definitions of variables for clinicopathological factors and statistical analysis".

→Comments from our author

Thank you very much. Pancreatic cancer has long been said to be a tumor with many stroma, which has made it difficult to determine expression data and genomic data, but Bailey et al. presented more reliable data by extracting the genome from the many tissues with few stroma and many tumor cells.

We are sorry for the lack of words. As you said, we think so too.

What Bailey et al. performed is a deep sequence of exome, which only clusters RNA transcriptional networks and RNA expression. The reason for the difference in pathway between the results of exome by Bailey et al. and our mRNA expression is that since we are comparing cancer tissue with adjacent normal pancreas and cancer tissue has more stromal tissue than normal pancreas, we believe that the results of the stromal tissue may be over-reflected.

The metadata of the expression analysis including the microarray shows the same pathway analysis result as ours.

I would appreciate it if you could refer to it.

We also collected samples from the part of the specimen where the high content of tumor cells was confirmed to be 40% or more by HE staining, and sequenced them.

Sevcan Atay Integrated transcriptome meta-analysis of pancreatic ductal adenocarcinoma and matched adjacent pancreatic tissues. PeerJ 8: e10141 DOI 10.7717 / peerj.10141

Based on the above, we have rewritten the discussion.

• Overall the paper would benefit from being proof read

→Comments from our author

Thank you. We requested English proofreading again.

Typos

• RNA sequencing; the authors referred to a p value of 1/10000. The standard nomenclature would be p<0.0001

→Comments from our author

Thank you very much. We have corrected it as above.

→Comments from our author

Thank you very much. We have corrected the all "ITGB1" in the section of “IHC scoring of ITGB1 and related definitions” to italics.

• Patient backgrounds; IPMC is mentioned fir the first time, but hasn’t been defined.

→Comments from our author

Thank you very much. Regarding the first IPMC we mentioned, we have made the following corrections.

IPMC　→　intraductal papillary mucinous carcinoma (IPMC)

• RNA sequencing; ‘Cytoscape was performed on these DEGs and the hub gene was found to be ITGB1…’ Here it should be italicised. And again in the last sentence of this section.

→Comments from our author

Thank you very much. We have corrected the pointed out "ITGB1" to italics.

• Discussion; ‘The study for transcriptome inby Bailey et al.’ ‘inby’ as a typo and et al should be italicised.

→Comments from our author

Thank you very much. We have corrected as above.

There are, however, a number of points which would need clarification before publication.

→Comments from our author

Thank you for your suggestion. We have added the following to the introduction.

ITGB1 is a constituent of β subunits in integrin molecules [9]. Integrin is mainly present in the plasma membrane and plays role of cell-cell adhesion, cell-extracellular matrix adhesion, and signal transduction [9, 10]. These lacks of adhesion cause the withdrawal of cell survival signals, resulting in exfoliation-induced apoptosis called "anoikis"[11]. Cancer cells are resistant to anoikis through certain types of integrins and are recognized as one of the key mechanisms for successful infiltration, migration and metastasis [11]. It has been reported that high expression of ITGB1 significantly correlates with deterioration of prognosis in colorectal cancer, breast cancer, lung cancer, etc., but it is controversial in pancreatic cancer[12-17].

→Comments from our author

Thank you for your support. I am very sorry. Table 1-3 was not attached on the previous manuscript, so we will attach it with this revise. We did not classify it as a classic type or a base-like type. Instead, we examined the correlation between the RNA expression level quantified by count per million (CPM) and the expression level by immunostaining in ITGB1 using Spearman's rank correlation coefficient. Postoperative chemotherapy is only with or without S-1. We also show this in Table 1.　

-All figure legends need to be improved for the readers to be able to understand the figures.

→Comments from our author

Thank you for your suggestion.　All the legends have been rewritten in detail. The legend of the figure has been greatly revised. We would appreciate it if you could accept it.

→Comments from our author

Thank you for your advice.

We think that you are right. We have previously attempted RNA extraction from FFPE. However, mRNA prepared from FFPE older than 1 year could hardly meet the quality check, and mRNA suitable for the conditions could be extracted from frozen specimens older than 1 year. This is probably due to pancreatic proteolytic enzymes and specimen deterioration depends on storage conditions.　Currently, it has passed since the time of the experiment, and all FFPEs have passed more than one year.

No additional verification was possible. As you said, We think it is undeniable that the difference between FFPE and frozen specimens may lead to the discrepancy of mRNA expression. These pursuits will be our future task.

-Please include a heatmap showing the RNAseq results for the 12 samples analysed.

→Comments from our author

Thank you very much. We detected 314 DEGs in 17 samples of 12 patients, created these heatmaps, and added them to the supplement (S1 Fig).

-R2 platform analysis (page 11): did the authors also examine ITGAV, COL1A1 and COL1A2? Please include these details as done for the rest of DEGs.

→Comments from our author

Thank you very much.

The rest of DEGs genes also analyzed this　time were COL1A1, COL1A2, and ITGAV and so on, so we verified these on the R2 platform. Only ITGAV was significantly correlated with prognosis, (COL1A1, COL1A2, and ITGAV,　P　=　0.098,0.174,0.033,respectively)　but this time it was not included in the analysis because it was not a high-ranking hub gene.

-Figure 2: Please include Kaplan Meier curves for the rest of genes, not only for ITGB1.

→Comments from our author

Thank you very much.

Second to the top hub genes analyzed this time were ITGB1, COL4A1, COL4A2, and ITGA5, so we verified these on the R2 platform and added them to Fig2.

→Comments from our author

Thank you very much. As you pointed out, We feel that you are right.

When it comes to protein quantification, we think it makes more sense to do a Western plot. However, we could not extract mRNA with satisfactory quality except for cryopreserved specimens and raw specimens. And currently, half of the frozen and raw specimens have been used up for RNA extraction. We also tried to do it with FFPE, but we couldn't do it because we didn't have any experienced staff to do Western plot from FFPE in the pancreas. We would like to keep these issues as future issues.

→Comments from our author

Thank you for your advice.

The image quality has been improved, including photographs of tumor tissue and adjacent normal pancreatic tissue, and high-quality histological photographs with high and low expression of ITGB1 have been added. We have also added a scale to the tissue photo.

→Comments from our author

Thank you very much.

We feel that you are right. There is a certain opinion about the tumor marker (CA19-9 CEA) that is generally known for pancreatic cancer and its prognostic significance, and we compared it with our data with references and entered it in "Discussion" as follows.

The known preoperative tumor markers CA19-9 and CEA, which are potential prognostic factors, have cutoffs of 37 U / ml and 3 U / ml or 5 U / ml, respectively [26]. It was said that high preoperative marker levels can be a prognostic factor, but they cannot be therapeutic targets. In this study, we divided the median into two groups, but at our facility, the inspection cutoffs for CA19-9 and CEA were 37U / ml and 5.0U / ml, respectively. When examined, the high value of CA19-9 was significantly correlated with DFS (OS; CA19-9＞37.0, CEA＞3,0, CEA＞5,0 P = 　0.051, 0.079, 0.233, respectively, DFS; CA19-9＞37.0, CEA＞3,0, CEA＞5,0 P = 0.001, 0.286, 0.356, respectively), but in multivariate analysis, CA19-9 was not an independent factor (P = 0.141).

Marius Distler　et al. Preoperative CEA and CA 19-9 are prognostic markers for survival after curative resection for ductal adenocarcinoma of the pancreas – A retrospective tumor marker prognostic study　 International Journal of Surgery, 2013-12-01, Volume 11, Issue 10, Pages 1067-1072

Regarding the results and status of clinical trials of ITGB1, we have added a description including the NCT number to "Discussion" and further references as shown below.

In clinical trials, there are currently no reports showing that a single integrin inhibitor is effective, but they are expected to be effective in combination with multiple agents such as immune checkpoint inhibitors (NCT00195278, NCT04508179) [39, 40]

R J Slack et al. Emerging therapeutic opportunities for integrin inhibitors　 Nat Rev Drug Discov. 2022 Jan;21(1):60-78. doi: 10.1038/s41573-021-00284-4.

Minor comments:

-Acronyms need to be explained in the text the first time they appear (e.g. ECM, ITGB1, COL etc)

→Comments from our author

Thank you for your advice. We have explained acronyms such as ECM, PI3K-Akt, ITGB1, COL, ITGA5, ITGAV, etc. without using abbreviations.

→Comments from our author

Thank you very much. We emphasized in the "Abstract" section that we did not use fixative tissue and that immunostaining was performed only on ITGB1.

-Page 7, line 6: please indicate what clinicopathological factors authors are referring to.

→Comments from our author

Thank you very much for your point out.

It was written in a bad way, so I rewrote it drastically.

-Please indicate the concentration of the ITGB1 antibody used for IHC stainings.

→Comments from our author

Thank you very much.

According to the instruction manual, it is diluted 100 times. I have described it in "Materials and Methods".

-Please explain what T-factors or T2 are (page 11).

→Comments from our author

Thank you very much.

We explained about the T factor.

→Comments from our author

Thank you for your advice.

“By case stratification” was an ambiguous expression, so we removed it.

Similarly, the expression “Although the influence of ITGB1 in pancreatic cancer has not been reported,” was deleted because it was considered inappropriate.

-Please revise the English grammar before re-submitting the manuscript. Thank you.

→Comments from our author

Thank you. We requested English proofreading again.

________________________________________

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PLoS One. doi: 10.1371/journal.pone.0268630.r003

Decision Letter 1

Eithne Costello

4 May 2022

Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer

PONE-D-21-36900R1

Dear Dr. Hoshino,

I wish to apologise for the delay in providing you with a decision, and I thank you for your patience.

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Eithne Costello

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank you for the many modifications you have made to your manuscript.

May I please ask that you ensure that supplementary (S1 Fig) is included in the resubmission, because I cannot find it in the current version.

Secondly, reviewer one points out a minor typographical error, that I would ask you to correct, i.e. please change integinand to integrin and.

I am happy with the quality of figure 3, thank you for your modifications to that.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: No

**********

6. Review Comments to the Author

Reviewer #1: The authors have addressed all my concerns, with the exception of the image quality of figure 3.

The authors propose that the image quality is ‘considered to be a problem in the journal review process’ that I should contact the publisher to confirm image quality.

I’ve not received a reply from the publisher regarding this figure, so cannot comment on the material within this image or conclusions derived from it.

Typo;

Page 24 in the discussion;

While it was reported that it functioned as a cell by ‘’construction of scaffolds’’ with integinand

Change in integrin and…

If the issue with image quality is resolved, I’d be happy to take another quick look at this section and recommend it for publication.

**********

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Reviewer #1: No

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PLoS One. doi: 10.1371/journal.pone.0268630.r004

Acceptance letter

Eithne Costello

23 May 2022

PONE-D-21-36900R1

Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer

Dear Dr. Hoshino:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Eithne Costello

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 Fig. Heatmap for 314 DEGs.

Genes with differential expression between the PDAC tissue and its adjacent pancreatic tissue were mapped and visualized on a heat map.

(TIF)

Click here for additional data file.^{(26.7MB, tif)}

S1 Table. Results for KEGG pathway analysis.

KEGG pathway analysis was performed on 314 DEGs, and a P value of 0.01 or less was considered significant, and 20 pathways were detected.

(XLSX)

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Data Availability Statement

All RNA-seq results are available from Gene Expression Omnibus (accession number GSE196009).

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PERMALINK

Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer

Yosuke Iwatate

Hajime Yokota

Isamu Hoshino

Fumitaka Ishige

Naoki Kuwayama

Makiko Itami

Yasukuni Mori

Satoshi Chiba

Hidehito Arimitsu

Hiroo Yanagibashi

Wataru Takayama

Takashi Uno

Jason Lin

Yuki Nakamura

Yasutoshi Tatsumi

Osamu Shimozato

Hiroki Nagase

Roles

Abstract

Introduction

Materials and methods

Study population criteria

RNA sequencing

Immunohistochemical analysis of ITGB1

IHC scoring of ITGB1 and related definitions

Definitions of variables for clinicopathological factors and statistical analysis

Results

Patient backgrounds

Table 1. Relationship between clinocopathological parameters and ITGB1 status.

RNA sequencing

Fig 1. Network analysis in the top five pathways.

Fig 2. Kaplan–Meier curve for pre-validation of the hub genes by the R2 platform.

IHC scoring of ITGB1

Fig 3. IHC of ITGB1 in pancreatic cancer tissue.

Correlation between IHC score and RNA-sequencing

Fig 4. Correlation between IHGB1 IHC scoring and RNA-seq.

Relationship between IHC status and clinicopathological factors

Relationship between clinicopathological factors and the prognosis and recurrence of pancreatic cancer

Table 2. Univariate analysis of prognostic factors with ITGB1 for OS and DFS.

Fig 5. Kaplan–Meier curve for overall survival and recurrence-free survival in ITGB1.

Evaluation of prognosis and recurrence predictors by multivariate analysis

Table 3. Multivariate analysis of prognostic factors for OS and DFS.

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Eithne Costello

Roles

Author response to Decision Letter 0

Decision Letter 1

Eithne Costello

Roles

Acceptance letter

Eithne Costello

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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