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. 2020 Feb 5;10(3):92. doi: 10.1007/s13205-020-2078-9

Expression profiling revealed keratins and interleukins as potential biomarkers in squamous cell carcinoma of horn in Indian bullocks (Bos indicus)

Ketankumar Panchal 1, Prakash Koringa 1,✉, Pritesh Sabara 1, Dhruv Bhatia 1, Subhash Jakhesara 1, Chaitanya Joshi 1,2
PMCID: PMC7000593  PMID: 32089987

Abstract

Horn cancer is most prevalent in Bos indicus and poorly defined genetic landscape makes disease diagnosis and treatment difficult. In this study, RNA-Seq and data analysis using CLC Genomics Workbench was employed to identify biomarkers associated with horn cancer. As a result, a total of 149 genes were found significant differentially expressed in horn cancer samples compared to horn normal samples. The study revealed ‘keratins’ and ‘interleukins’ as apex groups of significant differentially expressed genes (DEGs). Functional analysis showed that the upregulated keratins support metastasis of tumor via cell proliferation, migration, and affecting cell stability, while downregulated interleukins along with other associated chemokine receptors deprive the immune response to tumor posing clear path for metastasis of horn cancer. Combi-action of both the group facilitates the tumor microenvironment to reproduce tumorigenesis. Analysis of pathways enriched in DEGs and exemplified protein–protein interaction network indicated actual role of DEGs in horn cancer at a fine level. Important effect of deregulated expression of keratin and interleukin genes in horn cancer enrolling their candidacy as potential biomarkers for horn cancer prognosis. This study appraises the possibility to mitigate horn cancer at fine resolution to extract attainable identification of prognostic molecular portraits.

Electronic supplementary material

The online version of this article (10.1007/s13205-020-2078-9) contains supplementary material, which is available to authorized users.

Keywords: Horn cancer, Bos indicus, Transcriptome, Keratins, Interleukins

Introduction

Currently, in India, Bos indicus cattle is associated with milk production and agricultural labour, but its productivity declines along with economic loss due to sporadically occurring malignant and metastasizing tumor of horn, a disease condition known as horn cancer (HC) accounting 83.34% of total tumor incidence (Singh and Singh 2005). The total incidence (95%) was reported to be higher in bullock (Naik et al. 1970). Additionally, Joshi et al. (2009) reported that HC incidences are more frequent in bullocks of Kankrej breed than other zebu cattle, crossbred, nondescript cattle, and uncastrated male animals of same breed. The predisposing factors assume to be the higher weight of large beautiful crescent shape horn, continuous exposure to sunlight during fieldwork as well as hormonal changes associated with castration. The only available treatment of amputation affects lessening work capacity with the economic value drain of affected animals.

Horn cancer is a squamous cell carcinoma resulted from pseudo-stratified columnar epithelium of the horn core mucosa. Additionally, less scientific attention towards HC in Bos indicus resulted in lack of a complete cellular catalogue of horn core epithelium and absence of cancer-specific markers (Jakhesara et al. 2013) as compared to the well-studied transcriptomes of humans and mice (Consortium) which indicate that repertoire of bovine transcriptome is still far from being investigated at higher resolution. Comprehensive transcriptome sequencing of cancerous and normal tissue and their sophisticated bioinformatics analysis have the potential to uncover molecular portraits which include not only differentially expressed genes but also novel genes, transcript along with expressed isoform and splice variants which may help in prediction of horn cancer prognosis. Previous reports of HC transcriptome (Tripathi et al. 2012; Jakhesara et al. 2013; Koringa et al. 2016) can help to address the question of abnormalities in gene and associated transcripts.

Based on previous bioinformatic (Yu et al. 2008), biochemical (Czernobilsky et al. 1984) and, immunological (Thomas et al. 1999) studies, a sizable volume of information has been derived concerning keratin expression in cancer. More than 50 mammalian keratins (type I and II) have been identified and characterized which usually co-expressed as a heterodimer between type I (acidic) and type II (basic). The prime role of the keratin intermediate filament cytoskeleton is to offer cells with structural flexibility (Zhang 2018). Thus, according to necessity, expression of type keratins defers according to cell types (normal or cancerous). Keratin has been already established as a prognostic marker in tumor pathology (Karantza 2011). Likewise, prognostic significance of interleukins is also reported. Expression of IL6 has association with prognosis of patients with prostate cancer (Nakashima et al. 2000). Tumor growth and invasion promoted by strong immune response mediated by interleukins (cytokines) either produced by immune or tumor cells. Identification of such keratins and interleukins in presented study provides a platform for the identification of probable prognostic markers which are predictive of variation in horn cancer.

With an aim to explore transcript catalogue in search of biomarkers related to HC, we have employed Illumina MiSeq sequencing technology in sizable number of samples with great sequencing depth. This intricate transcriptome profiling enabled us to explore the nature of HC, associated genes, variants, and pathways which provide new inventory of therapeutic targets for prognosis and/or diagnosis.

Materials and methods

Ethical approval

Research work was approved by College of Veterinary Science and Animal Husbandry, Anand Agricultural University vide approval no. IAEC: 155/2011 to maintain the research ethics. All applicable national, and/or institutional guidelines for the care and use of animals were followed.

Tissue sample collection and histopathology

Tissue collection was planned from the major districts of Gujarat (India) to cover substantial geography. Tissue samples from clinically diagnosed horn cancer affected Kankrej bullock and horn nomal mucosal tissues were collected in RNAlater (Sigma-Aldrich, USA) and stored in liquid nitrogen until further processing. Tissue samples were also collected in 10% formalin and subjected for histopathological analysis for their malignant change confirmation. In total, 26 samples were collected for HC and 5 samples for horn normal (HN) conditions.

RNA extraction, library preparation, and sequencing

Prior to RNA extraction, samples were removed from the − 80 °C freezer and homogenized using tissuelyser. Total RNA was isolated from HC and HN tissue using RNeasy mini kit (Qiagen, Germany) following manufacturer’s instructions. Genomic DNA was eliminated cautiously from RNA preparation using RNase free DNase-I (Qiagen, Germany). Quantity was checked on Qubit 3.0 (Invitrogen, Thermo Fisher Scientific, USA), while integrity was assessed by intensity and shape of 28S and 18S rRNA using RNA 6000 Nano LabChip kit (Agilent technologies, USA) on Bioanalyzer 2100 (Agilent technologies, USA). RNA samples with RIN (RNA integrity number) value more than 6.0, were used for further analysis. Isolated Total RNA from HC and HN samples was put in storage at − 80 °C until further procedure.

The cDNA libraries of HC and HN samples were synthesized by 2–4 µg of starting total RNA following the protocols of the TruSeq® RNA sample preparation v2 kit (Illumina, USA) with low sample (LS) protocol (for multiplexing, appropriate indices have applied for cluster generation and sequencing). The libraries were amplified with 15 cycles of PCR and contained TruSeq indices within the adaptors. The final libraries had an average fragment size of ~ 330 bp and final yields of ~ 36 ng/µl which was monitored using DNA 1000 LabChip kit (Agilent technologies, USA) on Bioanalyzer 2100 (Agilent technologies, USA). After quantitation and dilution, the pooled library of four samples was sequenced in one sequencing run using Illumina MiSeq instrument with 250 bp paired end (PE) reads chemistry. Likewise, a total of eight sequencing runs were performed to accommodate all the samples library.

Gene expression profiling

First line of action in any data analysis is quality check and filtering. Raw data processed at minimum quality value (Q20) using PrinSeq tool followed by contaminant rRNA removal by ShortmeRNA tool. Downstream analysis was performed in CLC Genomics Workbench v 11.0. High-quality HC and HN sample pair end reads were mapped to the annotated Ensemble Bos taurus UMD3.1.1 genome. The total mapped reads number for each transcript was normalized to determine RPKM (reads per kilobase of exon model per million mapped reads) and fold change values. Transcripts with threshold FDR < 0.05 and absolute fold change > 2 were included in analysis as significant differently expressed genes.

Gene ontology, enrichment analysis, and protein network analysis of DEGs

Overrepresented GO (gene ontology) annotations were identified in the differentially expressed genes for GO analysis and enrichment analysis using an online DAVID tool (https://www.david.ncifcrf.gov). Additionally, DEGs were also subjected for Reactome pathway (https://www.reactome.org) analysis and KEGG pathway (https://www.genome.jp/kegg/) analysis. The GO annotation results were categorized with respect to biological process, molecular function, and cellular component. Protein expression network of significant differentially expressed genes was also analyzed on STRING (https://string-db.org/) server and represented using Cytoscape freeware to sneak into protein–protein interaction.

Validation with RT-qPCR

Randomly selected transcripts were validated using RT-qPCR from the RNA obtained from four HC and four HN samples. After through with quality and quantity of total RNA, cDNA was prepared from 1 µg of total RNA using reverse transcription reaction with oligodT primers following manufacturer’s protocol (Thermo Scientific, USA). Expression at mRNA level of respective target transcripts were quantified by real-time PCR analysis on ABI PRISM 7500 fast real-time PCR system (Applied Biosystems, Thermo Scientific, USA) using SYBR Green PCR master mix following manufacturer’s protocol (Kapa Biosystems, USA). For this purpose, Primer3 software (https://frodo.wi.mit.edu/primer3/) was used to design gene-specific primers from the respective transcript sequence spanning exon–exon junction. List of primers used for RT-qPCR are presented in supplementary Table 1. For qPCR, all reactions were run in triplicate and cycle threshold (CT) values for target transcripts were normalized with endogenous reference gene PGK. The fold change between the two conditions was calculated by the following equation:

Foldexpression=2-ΔΔCT,

where ΔCT = average, CT of target − average, CT of endogenous control (gene), and ΔΔCT = ΔCT of target sample (HC) − ΔCT of calibrator sample (HN).

Table 1.

Statistics of reads generated for HC and HN samples

Particulars Horn normal Horn cancer
Total numbers of reads 21,758,594 118,519,923
Total numbers of bases 3,523,246,603 19,446,200,027
Total number of high-quality reads 21,508,091 117,280,400
Total number of high-quality reads after rRNA removal 21,014,764 114,357,237
Mapped pair % 85.5 88.1
Mapped broken pair % 11.3 8.8
Unmapped pair % 3.2 3.0
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HC and HN reads filtered at Q20 as well as rRNA removed. CLC genomics workbench used for mapping of these quality reads to reference genome

Results

Deep sequencing of Bos indicus horn transcriptome

Generating substantial transcriptome data from HC and HN tissue samples was one of the goals, which reflected in total reads generated 21.7 M and 118.5 M for HN and HC samples comprising total 3.52B and 19.4B bases, respectively. The CLC Genomics Workbench v11.0 was used for alignment of sequencing reads against annotated B. taurus reference genome UMD3.1.1 from Ensembl database with default mapping parameters and RPKM normalization as expression value. Table 1 shows summary of sequencing and mapping for HC and HN. Sequencing statistics of HC and HN samples are detailed in provided supplementary Table 2 while the mapping statistics for each sample is provided in supplementary Table 3.

Table 2.

Deregulated keratin genes with significant differential expression in HC

Gene Log2 fold change FDR p value
KRT3 11.57 0.002
KRT6B 11.25 5.33E−08
KRT84 11.06 0.004
KRT81 10.76 0.006
KRT36 10.73 4.27E−05
KRT35 10.56 0.006
KRT1 10.24 0.010
KRT86 10.13 0.012
KRT6C 10.09 2.07E−05
KRT79 9.76 0.012
KRT83 9.66 0.017
KRT31 9.44 0.017
KRT85 9.16 0.003
KRT34 9.15 0.004
KRT33B 9.06 0.003
KRT33A 7.86 0.004
KRT75 7.28 0.040
KRT78 4.91 0.015
KRT14 4.40 0.009
KRT6A 4.33 0.040
KRT10 3.66 0.040
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Keratin genes are the major group in upregulated genes and are listed with their respective Log2 fold change and FDR p value

Table 3.

Deregulated interleukin genes with significant differential expression in HC

Gene Log2 fold change FDR p value
IL17C − 4.63 0.0002
CXCL8 (IL8) − 4.58 2.29E−08
CXCR1 − 4.15 0.002
IL6 − 3.89 6.33E−05
CXCL2 − 3.86 1.66E−05
CXCR2 − 3.71 0.003
IL17A − 3.67 0.015
IL1B − 3.59 0.0004
IL17F − 3.27 0.040
IL11 − 3.02 0.035
CXCL6 − 3.00 0.012
IL1RN − 2.61 0.006
IL2R2 − 2.32 0.004
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Interleukin and receptor genes are the major group in downregulated genes and are listed with their respective Log2 fold change and FDR p value

Cataloguing of differentially expressed genes (DEGs)

The results clearly revealed that in cancerous state, many genes had altered expression. A total of 19,981 transcripts were found differentially expressed in HC condition, where 12,495 genes are positively regulated and 7486 genes negatively regulated, as represented chromosome-wise in Fig. 1. To call significant differentially expressed genes, 5% of false discovery rate (FDR) value which uses negative binomial distribution and Log2 fold change > ± 2 parameters were utilized. Total of 149 significant differentially expressed genes between two conditions were found and presented in volcano plot highlighting with keratin and interleukin genes are shown in Fig. 2. Among these 149 significant DEGs, 18 genes are considered novel as reference annotations were not available. We found 89 up- and 42 downregulated known genes as well as, nine up- and downregulated novel genes. Major group of deregulated significant differentially expressed genes belonged to keratins and interleukins and are detailed in Tables 2 and 3, respectively. A full list of all DEGs is given as supplementary Table 4. Additionally, these genes also intersected with previous reports on horn cancer.

Fig. 1.

Fig. 1

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Chromosome-wise distribution of genes in horn cancer. Differentially expressed genes were plotted against chromosome number to check the distribution of these genes. Both positively and negatively regulated genes were presented in stack bar graph. X-axis denotes chromosome number and Y-axis denotes number of genes

Fig. 2.

Fig. 2

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Differential gene expression in horn cancer vs. horn normal in Bos indicus. Volcano plot indicates the expression of differential genes (Log2 fold) versus the statistical significance of the results. Volcano plot represents the total number of genes used in RNA-Seq analysis. Each point represents a gene plotted as a function of Log2 fold change (x-axis) and statistical significance (−Logp-value). Red dots represent 149 genes with Log2 fold change ± 2 and FDR value < 0.05. Significant differentially expressed keratins and interleukins are labelled

Table 4.

Reactome pathways enriched in up and downregulated genes in HC

Pathway name Reactome identifier p value
Upregulated genes
 Formation of the cornified envelope R-CFA-6809371 5.15E−06
 Keratinization R-CFA-6805567 1.93E−04
Downregulated genes
 Interleukin-10 signaling R-HSA-6783783 3.16E−11
 Calcitonin-like ligand receptors R-SSC-419812 5.30E−05
 Chemokine receptors bind chemokines R-HSA-380108 0.00385
 CLEC7A/inflammasome pathway R-HSA-5660668 0.004453
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Reactome pathway analysis of up and downregulated genes sorted with p value < 0.05

Functional analysis of differentially expressed genes in HC

Genes with significant differential expression with 5% FDR and Log2 fold change ± 2 (n = 149) were subjected to DAVID for gene ontology (GO) term enrichment analysis. Several significantly enriched GO terms were found for DEGs in HC enriched at p value < 0.05 are presented in Fig. 3a with their gene count. Structural component and immune response-related pathways are in majority under biological process category, while cellular component category again augmented with structural events and molecular function category has immune receptor-driven pathways. KEGG pathway also reflected these events categorized under disease response and signaling pathways as presented in Fig. 3b. Reactome pathway analysis revealed several pathways enriched at 0.01 p value for DEGs in HC with at ± 2 Log2 fold change up (n = 695)- and down (n = 120)- expression as compared to HN were included in Table 4. Important pathways for keratinization and cornified envelop formation-related pathways enriched in upregulated genes, whilst immunology-related pathways such as interleukin signaling, chemokines, and inflammasome pathways were in downregulated genes. These pathways directly or indirectly supporting the involvement of keratin and interleukin genes in horn cancer.

Fig. 3.

Fig. 3

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Pathway enriched in DEGs represented using GO category and KEGG category. a Gene ontology analysis using DAVID tool to categorize pathways associated with DEGs in three different category, namely biological process, cellular component, and molecular function. DEGs count number associated with pathways plotted on Y-axis vs GO category on X-axis. Pathways plotted are most significant pathways having p value < 0.05. b Analysis of KEGG database for annotated signaling pathways associated with genes differentially expressed in horn cancer. Curated most significant pathways and their respective −Logp values were plotted on clustered bar graph

Protein–protein interaction prediction of DEGs by STRING revealed an interaction network of major upregulated and downregulated genes’ product (protein) predicted on the basis of known protein interaction available for Bos taurus. This may confer the fact that all protein respective to DEGs may not appear in protein–protein interaction network due to lesser number of protein network availability. Exported data tabulated with node annotation data and score is imported to Cytoscape for network analysis and visualization. Protein interaction network of DEGs grouped in five distinct clusters as observed in Fig. 4. This complex network revealed how deregulated gene’s protein products interact in horn cancer disease condition. Three networks comprising two protein candidates in each, one network has three protein candidates and largest network has 56 protein nominees.

Fig. 4.

Fig. 4

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Interaction network of proteins of DEGs. Hexagonal box is individual protein of differentially expressed gene and lines between the boxes signify the interaction among them. Upregulated genes and downregulated genes were marked with yellow and turquoise blue colors, respectively

Validation of transcript by RT-qPCR

A total of 12 randomly selected significant differentially expressed genes namely CDSN, CNFN, TNN, LY6D, AMTN, SP7, CSF3, CXCL8, CES4A, IL6, CXCL2, and IL17A considered on the basis of their successful amplification of intended PCR product size, thus, they were validated using RT-qPCR to confirm correlation of next generation sequencing results with qPCR. All of their expression patterns have concordance with the expression profile found in RNA-Seq analysis. Genes namely CDSN, CNFN, TNN, LY6D, AMTN, and SP7 were found upregulated and CSF3, CXCL8, CES4A, IL6, CXCL2, and IL17A were established as downregulated with varied expression compared to in silico RNA-Seq analysis as presented in Fig. 5.

Fig. 5.

Fig. 5

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RT-qPCR analysis of randomly selected DEGs on multiple horn cancer samples. The relative expression of 12 candidate genes was analyzed in four HC samples. Gene names indicated below on X-axis and relative expression values (mean ± SE) on Y-axis considering horn normal as baseline at 1. Relative expression value less than one is considered as increased in relative expression marked as blue color and vice-versa for decreased relative expression marked as ochre yellow color

Discussion

Next generation high-throughput sequencing technology has availed a charter to reconnoiter cancer-inherited transcriptional complexity (Guffanti et al. 2009). Comprehensive RNA-Seq in this study has revealed key molecular portraits (genes) associated with development pathways of HC. For accurate identification of genes and their expression, it is of utmost importance to map reads to their genomic counterparts. High-quality deep sequencing data generated in our analysis has elevated reliability of gene predicted as reflected in RT-qPCR validation studies. Total generated reads in each HC (118.5 M) and HN (21.7 M) group themselves suggestive of sufficient genome coverage to extract meaningful genes and transcripts. Broadly, complete concordance of RT-qPCR and RNA-Seq analysis provides meaningful consideration about suitability of RNA-Seq for transcriptome profiling in HC.

The DEGs identified in our studies are also reported to be deregulated in different types of cancer and their metastasis behavior in human as detailed in Table 5. Keratin gene family is amid apex upregulated genes. They are type I and type II molecules forming epithelial heteropolymeric filamentous proteins found in this study namely KRT1, KRT3, KRT6B, KRT35, KRT36, KET81, KRT84, and KRT86. Integrity and mechanical stability of epithelial cells and tissues are the functional aspects of keratins, while wound healing, protection from stress, and apoptosis are the regulatory aspects. Surge in expression of aforementioned genes was reported in human cancer, specifically KRT6B and KRT14 in SCC of lung (Hawthorn et al. 2006; Chang et al. 2011a; Amatschek et al. 2004). Xue et al. (2010) has reported increased expression of KRT14 in squamous cell carcinoma of esophagus, lung, larynx, cervix, and external genitalia. Thus, consistency in expression of both KRT6B and KRT14 genes plays an important role in proliferative metastasis of squamous cell carcinoma in horn. Characteristic expression patterns of keratins in tumors also have substantial importance in immunohistochemical diagnosis of tumor, subtyping and accurate categorize carcinoma, in particular of conflicted metastases (Moll et al. 2008). KRT35–36, KRT81–84–86 are the major proteins in the horn keratins. Koringa et al. (2016) reported KRT36 and KRT84 as novel candidates possibly involved in cancer pathways. KRT1 is suprabasal epidermal keratins and KRT3 is keratins of the corneal epithelium (Pitz and Moll 2002) and are responsible for recurrence of cancer (Iguchi et al. 2014), while, co-expression of certain simple epithelial keratins in squamous cell carcinomas also reported (Moll 1998). Paramount expression of KRT1, KRT3, and KRT6B in squamous epithelial cell may confer their role to provide structural integrity to cells allowing metastasis progression through hollow space of horn and head skull. Furthermore, overexpression of KRT35, KRT36, KRT81, KRT84, and KRT86 which are majorly involved in hard keratin formation of horn may contribute to change in horn swirl and/or bending of horn because of increased epical mass due to filling of metastatic cancer tissues. Possible role of KRTs in cancer is presented in Fig. 6a. Furthermore, involvement of SPINK6 (Lu et al. 2012), DSG1 (Myklebust et al. 2011), IBSP (Mathews et al. 2011), and ASPN (Ikegawa 2008) in cancer development is also reported.

Table 5.

Association of significant differentially expressed keratins, interleukins, and other cytokines in cancer of other mammals

Gene name Human-cancer association References
Upregulated keratin genes
 KRT3 Oral and cervical Ganguly and Ganguly (2015)
 KRT6B Breast, gastric, and bladder Reinert et al. (2011)
 KRT84 Colorectal Kosa et al. (2012)
 KRT81 Squamous cell lung carcinoma Campayo et al. (2011)
 KRT36 Tongue Boldrup et al. (2017)
 KRT35 Breast Vadakekolathu et al. (2018)
 KRT1 Breast Blanckaert et al. (2015)
 KRT31 Squamous cell lung carcinoma Zhang et al. (2017)
 KRT34 Prostate Dozmorov et al. (2009)
 KRT14 Breast, gastric, and bladder Papafotiou et al. (2016), Hanker et al. (2010), Haraguchi et al. (2006)
 KRT6A Breast and squamous cell lung carcinoma Johnson et al. (2015), Chang et al. (2011b)
 KRT10 Ovarian Wu et al. (2014)
Downregulated interleukins and other cytokine genes
 IL17C Pancreas Johnson and Mejia (2013)
 CXCL8 (IL8) Gastric, colorectal, ovarian, breast, and pancreatic Matsuo et al. (2009), Freund et al. (2003), Lu et al. (2005)
 CXCR1 Colon Yuan et al. (2000)
 IL6 Colorectal Landi et al. (2003)
 CXCL2 Prostate Hardaway et al. (2015)
 CXCR2 Lung Keane et al. (2004)
 IL17A Breast Wang et al. (2012)
 IL1B Gastric Garza‐González et al. (2005)
 IL17F Breast Wang et al. (2012)
 IL11 Breast Singh et al. (2006)
 CXCL6 Small cell lung Zhu et al. (2006)
 IL1RN Gastric Garza‐González et al. (2005)
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Fig. 6.

Fig. 6

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Role and pathway acquired by keratin, cytokines, and interleukins in horn SCC. a Role of keratin type I and II in cancer. Deregulation of keratins in different layer of epithelial cell displays their action in different aspects of carcinoma i.e. cell proliferation, migration metastasis, and skipping apoptosis. b Molecular network of cytokines and interleukins in cancer. Pathways presented for cytokines in general and interleukins (IL1b, 6, 8, 17) are acquired from established human cancer pathways. All of them believed to contribute in SCC of horn via intermediate pathways like JAKs, STAT (1, 3, 5), TAK1, TAB1, TRAF6, PI3K, Akt, NF-kB, and FAK

On the flip side, downregulated genes in HC have their own significance. Major downregulated gene are from cytokines which are the factors known to have both tumor promoting as well as inhibitory effects, presumably depending on their relative concentration and other modulating factors. Interleukin 17C (IL17C) is known as proinflammatory cytokines and Johnson et al. (Johnson and Mejia 2013) reported its anticancer property in human cancer cells. Moreover, IL6 gene contributing in inflammation-related risk (Landi et al. 2003) and governs the growth of multiple myeloma (Gadó et al. 2001). The IL6 exerts a powerful pro-survival function through induction of one or more protein ultimately playing an important role in resistance toward apoptosis (Knüpfer et al. 2007). Ásgeirsson et al. (1998) supported IL6-mediated promotion of breast cancer cell mobility indicating its role in metastasis. The chemokine CXCL8, also known as interleukin-8 (IL8), is a proinflammatory molecule that has functions within the tumor microenvironment. CXCL8 and its receptors are now measured as striking targets for cancer therapy due to the activity of the chemokine and its receptors as well as its potent angiogenic effects in the promotion of invasion and metastasis (Gales et al. 2013). Evenly, CXCR1 which is a receptor for IL-8 also have aberrant expression in colon cancer cells as well as in head and neck squamous cell carcinoma, breast cancer, and non-small cell lung cancer cells. The expression of CXCR1 along with CXCR2 plays a vital role in overall patient’s cancer prognosis (Yuan et al. 2000). CXCL2 is classified under angiogenic chemokines which involves in blood vessel formation by inducing endothelial cell migration, proliferation as well as stimulating tube formation (Palacios-Arreola et al. 2014). In most cancer metastasis cases, CXCL2 is shown to be upregulated which contradicting our finding. Similar downregulation of IL1B also challenging the report of Kashyap et al. (2009). Wu et al. (2013) has revealed association of IL11 with cancer grade and stage in bladder cancer and also suggested reduced level of IL11 may play an important role in progression of transitional cell carcinoma indicating that IL11 could be a promising predictor of cancer prognosis. Promotion of tumorigenesis by IL11 triggering JAK-STAT3 pathway and its role in tumor metastasis through PI3K-AKT-mTORC1 pathway also reported (Xu et al. 2016). CXCL6 is angiogenic chemokines and involved in cancer metastasis and immune response (Li et al. 2018) also downregulated in breast cancer (Boneberg et al. 2009). Furthermore, IL17A (proinflammatory cytokines) is involved in pathology of tumor microenvironment and inflammatory diseases by stimulation of angiogenesis and invasive capacity of tumor cells (Wang et al. 2014). Estimated molecular pathways acquired by different cytokines and interleukins in horn SCC adapted from established cancer pathways in human are presented in Fig. 6b. Other than chemokines, RBL12 is reported as one of the marker gene for diagnosing lung cancer using gene expression profile in peripheral blood mononuclear cells (Showe et al. 2013) and also differentially expressed in other cancer type (Wen et al. 2014). Downregulation of CSF3 gene in HC that controls the production, differentiation, and function of granulocytes also reported in small cell lung cancer (Crawford et al. 1991). In horn cancer, suppressed genes are cell-mediated immunity which are considered as a defense mechanism against uncontrolled dividing cells, thereby dominance advanced by cancer growth on cell-mediated immunity can be proposed.

GO analysis is another avenue to elucidate HC in bovine with enrichment of different categories. GO analysis revealed enrichment of significant differentially upregulated genes involved in structure manifestation during cancer development suggesting role of cell–cell adhesion molecules, differentiation of keratinocytes and its regulation as well as extracellular matrix organization. These events in positively regulated gene are enough to establish metastasis nature of horn cancer by formulating keratin and intermediate filaments, desmosomes and exosomes, basement membrane lined with proteinaceous extracellular matrix which account activity of structural molecule as well as metalloendopeptidase with calcium ion binding, hairping binding, carbohydrate binding, and extracellular matrix binding at molecular level. On the other hand, downregulation of IL and CXC gene families deprive immunological response including inflammatory response to horn cancer affected area. Series of events linked to negative regulated genes are cell chemotaxis, regulation of hormonal secretion, cell surface receptor signaling pathway, regulation of cytokines production, cytokines-mediated signaling pathways, and regulation of interleukin secretion. Regulation of cell proliferation accounts activity of cytokines, growth factors, chemokines, and interleukin as well as their receptors activity at molecular platform. Deleterious effect of cell-mediated immunity suggesting defective immunity involvement in cancer development passing clean cheat to uncontrolled proliferating cancerous cells. KEGG pathway analysis presented PI3K-Akt signaling pathway among upregulated genes, which regulates cell survival and proliferation. Recent reports (Spoerke et al. 2012; Sarris et al. 2012; Morgan et al. 2009; Liu et al. 2017) have observed aberrant activation of this pathway in breast, lung, prostate and endometrial human cancers. ECM (extra cellular matrix)-receptor interaction is also one to the step in journey of malignancy cascade in tumor progression brings biochemical and biophysical changes (Venning et al. 2015) as well as modulates cancer hallmarks (Pickup et al. 2014). Focal adhesion is nothing but the adhesion to ECM which is key strategy for cancer cells to migrate through tissues (Nagano et al. 2012). These events govern by IBSP, TNN, COL2A1, COL17A1, THBS4, and SPP1 genes. On the other hand, pathways catalogue regulated by downregulated gene includes cytokine–cytokine receptor interaction, chemokine, JAK-STAT, TNF, NOD-like receptor, toll-like receptor signaling pathways, and disease responses. JAK-STAT is the example of cytokine–cytokine receptor interaction which surveillance tumor (Lee and Rhee 2017). Chemokine signaling pathway regulate recruitment of immune cells in response to inflammatory response (Hembruff and Cheng 2009) and has prognostic significance in several types of human cancers (Rubin 2009). Based on the stimuli, TNF decides which cell to survive or die makes TNF-signaling pathway a potential target in cancer therapy (Wang and Lin 2008). Likewise, disease response events have important integrated network with immunity. Reactome pathway analysis also supported with propionate gene enrichment category to those of GO pathways and KEGG pathways.

We sought to explore transcriptome at protein level presented in STRING network, represented in Cytoscape uncovered few interesting interactions between upregulated and downregulated proteins. Upregulated THBS4 gene controls cell proliferation, migration, adhesion and attachment interact with CXCR2 which is downregulated controlling activation of neutrophils when binds to IL8. Interaction of CSF3 and its receptor with variety of interleukins explains the recession in immune response during horn cancer. Similarly, interaction of COL17A with KRT or via DSG1 describes their role in physical stability in metastasis of horn cancer also, supported by network of KRT35, KRT78 with keratin-associated protein. Positively expressed PENK is preproprotein and associated with AKT signaling pathway connecting IL and CXC gene family as established in network. THBS4 (thrombospondin 4) with elevated expression as reported by Susann et al. (Förster et al. 2011) in gastric adenocarcinoma of human involves in focal adhesion by modulating adhesive glycoprotein for cell–cell and cell–matrix interaction to establish network with CXC receptors. Finally, KRT protein family constitutes in one interaction cluster, likewise IL family and their receptors CXC family. Presented protein–protein interaction has substantial correlation with GO, KEGG and Reactome pathways.

Conclusion

All in all, high number of sample replicates with sufficient sequencing depth in bovine, made a clear passage to evaluate and validate genes, their complex network, and enrichment alongside with available information about bovine transcriptome. Genes expressed differentially in cancerous samples have association with previous studies. In addition to that, keratins as significant upregulated genes contributing to horn cancer via physical stability and allied process for cell migration while interleukins as significant downregulated genes lowering the immune response allowing horn tumor to flourish. Investigation of signaling pathways of deregulated genes in horn cancer with reference to previous scientific reports contribute to SCC of horn due to pathway dysfunctionality. Protein network analysis provided insight into interaction of proteins of DEGs. Noteworthy resemblance was found in gene expression pattern and pathways with human cancer. This study has established that keratins and interleukins can be used as markers for prognosis of horn caner which are filling the gap by providing the assistance for preparing landscape of bovine transcriptome at finer level.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 428 kb) (428.5KB, pdf)

Acknowledgements

We thank the Department of Biotechnology (DBT), Government of India, New Delhi, India for providing financial support (Grant Letter No. BT/PR13649/AAQ/1/627/2015) for this project.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

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