Whole Transcriptome and Functional Genomic Landscape of A South Indian Cohort of Gastric Tumors

Whole Transcriptome and Functional Genomic Landscape of A South Indian Cohort of Gastric Tumors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Whole Transcriptome and Functional Genomic Landscape of A South Indian Cohort of Gastric Tumors Jaishree Pandian, Madhusudhanan Gnanasekaran, Vikash Vittal, Balaji T Sekar, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1476204/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Gastric cancer is the third most common cause of cancer-related mortality. Genome-wide studies pave way for the delineation of heterogeneities and the possible genomics-guided stratification of tumors. Numerous whole-genome profiles have been established from different parts of globe and are largely lacking from South India. We performed genome-wide expression profiling of 50 gastric tumors and 15 adjacent normal tissues from Madurai, India. The genes differentially expressed in gastric tumors were found comparable with genes from other cohorts across globe. Integrative genomic analysis revealed the transcription factors LEF1, SP1, NFAT, AP1, PAX4, E2F and ATF2 to be upregulated and p53, HNF3, ESR, BMP2, PRC2/SUZ12/EED and NRF2 to be downregulated in gastric tumors. Pathway enrichment analysis showed the higher enrichment of focal adhesion, TGF-β, VEGF, EGFR, MAPK and EMT among the genes upregulated. The gastric acid secretion, digestion and absorption of minerals were identified to be diminished in gastric tumors. In a gene set based comparison of molecular subtypes, the frequency was found to differ between TCGA and Madurai cohorts indicating the disparity in molecular genomic patterns across populations. Thus the identification of the transcription factors, signaling pathways and molecular processes dysregulated in gastric tumors is resourceful and reveals the potential therapeutic targets for the eventual development of targeted gastric cancer therapeutics. gastric cancer genome-wide expression profiling differentially expressed genes functional genomics gastric acid secretion digestion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Gastric cancer is one of the major malignancies with higher morbidity and mortality around the world, especially in Asian countries [ 1 , 2 ]. According to the global scenario of gastric cancer, India falls under the low incidence category. However, in India it is the fifth most common cancer [ 3 ] and the second most common cause of cancer related deaths [ 4 ]. Within India, there is a wide regional variation in the occurrence of gastric cancer. The data from National Cancer Registries programme (NCR), India reveals north east state of India, Mizoram to have the highest incidence followed by Tamil Nadu (for men) and Bangalore (for women). However, diagnosis of early gastric cancer continues to be a problem since routine screening is not feasible considering the population [ 5 ]. The factors contribute to the progression of gastric cancer include lifestyle, environmental factors, Helicobacter pylori infection, genetic and epigenetic alterations. Helicobacter pylori infections are estimated to occur in more than 60% of Indians leading to peptic ulcer, particularly duodenal ulcer [ 6 ]. Consumption of salted, smoked or poorly preserved foods, low consumption of fruits and vegetables are also being major risk factors. Other disease factors associated with an increased risk of gastric cancer include chronic atrophic gastritis, hypertrophic gastropathy and gastric polyps [ 7 ]. However, the underlying molecular biological mechanisms are still elusive. As the pathogenesis of gastric cancer involves the dysregulated molecular signaling pathways and molecular cellular processes, it is necessary to understand the dysregulation by unbiased global methods [ 8 ]. Considering the disparities in the genetic determinants in various diseases, it is necessary to characterize the genomic and transcriptomic landscape across different geographical regions and populations. In this study, we performed whole genome mRNA profiling of gastric adenocarcinoma tumors along with adjacent normal tissues from Madurai, India using Affymetrix HTA2.0 arrays. The genes differentially expressed in gastric tumors compared to the gastric normal tissues were identified and the transcription factors, signaling pathways and molecular-cellular-physiological processes were identified by integrative functional genomic investigation. The differentially expressed genes were compared with genes from other cohorts. The molecular stratification of subtypes of gastric cancer defined in ACRG and TCGA studies were also analyzed across Madurai cohort gastric tumors. The identified dysregulations and the stratification avenues would pave way for the development of potential diagnostic and therapeutic leads to enable the eventual development of genomic medicine for gastric cancer. Materials And Methods Clinical samples and RNA extraction Surgically resected gastric tumor samples were collected from 50 gastric cancer patients those underwent gastrectomy at Madurai Meenakshi Mission Hospital & Research Centre (MMHRC), Madurai. The study was carried out with the approval of institutional ethics committee of MMHRC. Based on the histopathological observation, tumor tissue with ≥70% tumor cells were collected and the adjacent normal tissues were harvested at least a few centimetres away from the core tumor region of the surgically resected biopsies [9, 10]. The samples were collected in RNA later ® (Ambion Inc., Austin, TX) and stored at -80°C until the extraction of RNA. Total RNA was extracted from 50 mg of collected cancerous and adjacent noncancerous gastric tissues using the RNAeasy ® Kit (Qiagen). The tissue was minced in the RLT lysis buffer (Qiagen) using the TissueRuptor ® (Qiagen) and total RNA was isolated as per the protocol of the manufacturer. The concentration and quality of RNA was analyzed by Nanodrop 2000 spectrophotometer ® (Thermo Scientific, USA). The quality of RNA was assessed using the Bioanalyzer 2100 ® (Agilent, Palo Alto, CA) and RIN value (RNA Integrity Number) was recorded to understand the intactness of 18S and 28S rRNA. The samples with RIN value ≥ 5 and RNA concentration ≥ 150ng/µl were taken into consideration and processed further for expression profiling. Whole-genome mRNA profiling of gastric tumor and adjacent gastric normal tissues Total RNA (500 ng) from 50 gastric tumor and 15 adjacent gastric normal samples with the criteria of RIN ≥ 5 were subjected to mRNA profiling using GeneChipTM Human Transcriptome Array (HTA) 2.0 microarray chips (Affymetrix, Santa Clara, CA). RNA processing, hybridization, washing and staining were performed in GeneChip Fluidics Station 450 (Affymetrix), as per the manufacturer’s protocol. The chips were scanned using GeneChip Scanner 3000 7G (Affymetrix). Microarray data analysis The scanned CEL files were Gene Level-SST-RMA (Robust Multi-array Average) normalized in the Expression Console (EC) Software of Affymetrix. The fold change and P -value of the genes were calculated by ANOVA using the Transcriptome Analysis Console version3.0 (TAC) software (Affymetrix). The raw data and the processed data of whole transcriptome profile of 50 gastric tumor and 15 adjacent gastric normal tissues of Madurai, India cohort has been submitted to the Gene Expression Omnibus (GEO) repository database with the accession ID GSE146996. The genes differentially expressed in gastric tumors were filtered with the fold-change ≥ 2.0 for upregulation and ≤ 2.0 for downregulation. The genes were further filtered to be statistically significant at P ≤ 0.05. Pathway and gene set enrichment analysis The genes differentially expressed in gastric tumors were analyzed by gene set enrichment analysis using the available hallmark gene sets of molecular signatures database (MSigDB) with the default parameters [11, 12]. Enriched gene sets with significant P -value were selected for further consideration. Signaling pathway based enrichment analysis was performed using Transcriptome Analysis Console V4.0 (TAC) for the genes differentially expressed in gastric tumors. KEGG orthology based pathway enrichment analysis was performed using the tool GeneCodis [13]. To derive the gene sets for the selected oncogenic signaling pathways, the available corresponding gene signatures were collected from MSigDB. The available gene sets for ERK, OCT4, E2F, ECM, ESR, TGF-β, HNF4 and PPARγ were collected from MSigDB. Independently, for each of the gene sets, the gene set/ pathway based activation scores were calculated as described earlier using the Kolmogorov–Smirnov metrics [14]. Further, the correlation between the gene set activation score and all the genes in the expression profiles of gastric cancer datasets (GSE15459, GSE62254, GSE35809, GSE22377, and GSE15456) was performed. With the specific cut off correlation value (≥0.4) and also based on their presence across multiple datasets, the genes were filtered and shortlisted. These candidate genes were designated as the pathway specific gene sets more relevant to gastric tumors. The gene signatures used for different molecular subtypes of gastric tumors in ACRG [15] and TCGA [16] studies were used to explore the corresponding molecular subtypes. The expression pattern of the genes and the gene sets were analyzed and represented as heatmap using dChip software [17]. Results Genome-wide mRNA profiling of gastric tumors from Madurai, an Indian cohort Genome-wide expression profiles of several cohorts of gastric tumors from different countries have been established. Towards establishing a whole transcriptome profile of a South Indian cohort, whole-genome expression analysis was performed from a cohort comprising 50 gastric tumor and 15 adjacent gastric normal tissues. The samples were collected from gastric cancer patients those underwent gastrectomy at Meenakshi Mission Hospital & Research Centre (MMHRC), Madurai, Tamil Nadu, India. The study was conducted with the approval of institutional ethics committee of MMHRC, Madurai. Based on the histopathological investigation of the tumor samples, the portion of tumor samples with ≥70% of tumor content were selected for RNA extraction. The RNA samples with the criteria of RNA integrity number ≥5 were analyzed by genome-wide mRNA profiling using HTA2.0 arrays (Affymetrix) (Fig. 1A). The microarray data files were subjected to principal component analysis (PCA) to understand the overall distribution and variance of the gene expression among the adjacent normal and gastric tumor samples. The PCA plot revealed the gastric normal and tumor samples to be grouped independently without any outliers (Fig. 1B). Further the genes differentially expressed in gastric tumors compared to adjacent gastric normal samples were investigated. With the fold change cut-off of ≥ 2 and ≤ -2, 1005 genes were found upregulated and 388 genes got downregulated in gastric tumors with the significant P -value of ≤0.05 (Supplementary Table 1 & 2). Further, the unsupervised hierarchical clustering of adjacent normal and gastric tumor samples, based on the expression pattern of differentially expressed genes, revealed the normal and gastric tumor samples to get clustered separately (Fig. 2A). Comparison of the genes dysregulated in Madurai cohort with other established gastric tumor profiles To understand the comparability of the genes dysregulated in Madurai cohort of gastric tumors with the other established cohorts of gastric tumors, we compared the genes differentially expressed across multiple cohorts of gastric tumors. The differentially expressed genes from 23 cohorts of gastric tumors from China, Japan, South Korea, USA, Germany, Spain, India and Argentina were collected (Supplementary Table 3). The comparative analysis of the differentially expressed genes affirms that differentially expressed genes of gastric tumors from Madurai cohort are largely overlapping with the differentially expressed genes of the cohorts of China, India, South Korea and Argentina (Fig. 2B). Of the analyzed 23 cohorts and among the 1005 upregulated genes from Madurai cohort, i) 29 genes were found to show elevated expression in at least 10 other cohorts, ii) 274 genes in 4 cohorts across the globe, iii) 566 genes at least 2 other cohorts, and iv) to the maximum, the gene COL1A1 shows occurrence across 15 cohorts of gastric tumors (Supplementary Table 4). Among the genes downregulated in gastric tumors from Madurai cohort, across 23 investigated cohorts, i) 289 genes were found downregulated in 2 other cohorts, ii) 201 genes were found downregulated in 4 other cohorts, iii) 32 genes in 10 cohorts, and iv) in the higher side, the gene ATP4B, GIF and PGC were found downregulated in 17 and 16 other cohorts (Supplementary Table 5 & Fig. 2B). This shows the comparability of the genes differentially expressed in Madurai cohort with other cohorts across countries. While it is worth and tempting to investigate the differing pattern of pathogenesis or disparity across populations, the current analysis shows that in large, the difference is due to sample size, differing platforms, and also possibly the differing heterogeneities in the subtypes, which all deserve an extensive investigation. Dysregulated expression of major cancer genes in Madurai cohort of gastric tumors Among the genes upregulated in the gastric tumors of Madurai cohort, we could note several well reported oncogenes known to involve in carcinogenesis. SPP1 (Osteopontin) a secreted N-linked glycoprotein gene is known for the elevated expression in gastric cancer [18], to promote metastasis [19] and serve as a prognostic factor [20]. In the current cohort, SPP1 was found to be extremely upregulated with 80 folds of higher expression in gastric tumors compared to non-tumor gastric tissues. Several Extra Cellular Matrix (ECM) genes including BGN (biglycan), a small leucine-rich proteoglycan, known to enhance gastric cancer cell wound healing, migration, invasion, as well as the endothelial tube formation [21], was found upregulated with 65 folds of higher expression in gastric tumors. Several collagen genes such as COL1A1, COL1A2, COL5A2, COL12A1 , and COL14A1 were found upregulated . The collagen genes COL1A2, COL3A1, COL6A3, COL1A1 and COL12A1 showed 20 – 50 folds high expression in gastric tumors. Other significantly upregulated markers include sulfatase 1 (SULF1), thrombospondin 2 (THBS2), carcinoembryonic antigen related cell adhesion molecule 6 (CEACAM6), inhibin beta A (INHBA) and claudin 7 (CLDN7) which got upregulated from 6 – 67 folds. The dysregulation of these genes in gastric tumors of Madurai cohort is in the line of the established whole-genome profiles of gastric tumors (Supplementary Table 6). Gene set enrichment analysis reveals a comprehensive array of molecular processes dysregulated in gastric tumors Further we set out to perform gene set enrichment analysis for the genes upregulated in gastric tumors in order to understand the dysregulated processes. The analysis in Molecular Signatures Database (MSigDB) revealed the enrichment of i) transcription regulators LEF1, SP1, NFAT, AP1, PAX4, E2F, ATF2, ii) oncogenic pathways EGFR, KRAS, VEGF, ECM and loss of p53 related molecular processes among the genes upregulated in gastric tumors. In addition, the oncogenic signatures such as epithelial to mesenchymal transition (EMT), invasion, advanced gastric cancer and cellular proliferation also were found to be enriched among the genes upregulated in gastric tumors (Fig. 3A and Supplementary Table 7). The similar analysis carried out in Transcriptome Analysis Console V4.0 (TAC) tool further revealed the enrichment of the gene-sets corresponding to focal adhesion, EMT, cell cycle, TGF-β, RAS, MAPK, JAK/STAT, NOTCH, EGFR and nuclear receptors including vitamin-D receptor pathways to be significantly upregulated in gastric tumors (Fig. 3B and Supplementary Table 8). KEGG pathway enrichment analysis using GeneCodis tool also revealed significant enrichment of above said pathways (Fig. 3C). In addition, phagosome and cytoskeletal genes were noted. Most of the above said pathways are known for their association in the progression of gastric cancer. For example, the aberrant activation of EMT and the resultant tumorigenic processes could be triggered by various transcription factors and signaling pathways including TGF-β and Notch in gastric cancers [22]. Ras/Raf/MAPK signaling pathway involved in the transmission of extracellular signals, proliferation and differentiation has been reported in gastric carcinogenesis with high rate of mutation [23]. Similarly, all the identified pathways and dysregulations have been reported to have an association with gastric and other cancers. However, the current observation reveals a comprehensive understanding of the processes highly activated in gastric tumors. Loss of stomach associated molecular physiology is the prime dysregulation inferred from the transcriptome of gastric tumors In our cohort, 388 genes got downregulated in gastric tumors compared to normal gastric tissues with more than 2 folds. A number of genes found to be downregulated in the current cohort have already been reported. GIF encoding for gastric intrinsic factor is secreted by the parietal cells of the stomach and is necessary for the absorption of vitamin B12. GIF was found to be the promising predictive maker for gastric cancer [24]. In the current cohort, GIF was found to be extremely downregulated to 26939 folds. Gastrokine family genes GKN1 and GKN2, which are expressed in the normal gastric epithelium plays a significant role in maintaining the integrity and homeostasis of gastric mucosa. Inactivation and downregulation of both GKN1 and GKN2 have been reported to involve in the development and progression of gastric cancer [25, 26]. In Madurai cohort, both GKN1 and GKN2 got downregulated to 11413 and 4326 folds respectively. Gastric lipase (LIPF) of the family of lipases involved in the digestion of triacylglycerides is secreted by gastric mucosal cells [27]. LIPF has been reported for downregulation in gastric cancer and also is a part of the 8 gene signature known to predict gastric cancer [28]. In Madurai cohort, LIPFis downregulated to 9137 folds. KCNE2(Potassium voltage-gated channel) gene was reported to involve in neurotransmitter release, neuronal excitability and electrolyte transport [29]. We found 159 fold downregulation of KCNE2in gastric tumors. Deletion of KCNE2has been reported to cause gastritis and is also a predisposing factor for gastric cancer [30]. Similarly, several genes with the physiological role in gastric cells and also previously reported to have tumor suppressor features have been found to be highly downregulated in gastric tumor samples. For example, ATPase genes (ATP4A, ATP4B) [31], pepsinogen genes (PGA3 – 5, PGC) [32], mucin genes (MUC1, MUC5AC and MUC6) [33] and trefoil factors (TFF1 & TFF2) [34] were found to be highly downregulated in the current cohort of gastric tumors. A list of the previously reported genes to be downregulated in gastric tumors and also downregulated in Madurai cohort are provided in Supplementary Table 9. Gene set enrichment analysis performed with the genes downregulated in gastric tumors revealed the enrichment of the transcription regulators such as Estrogen Receptor (ESR), Bone Morphogenic Protein (BMP2), Hepatocyte Nuclear Factor (HNF3), polycomb repressive complex factors PRC2/SUZ12/EED and TP53. Several metabolism related signatures including xenobiotic, fatty acid, retinol and bile acid metabolism were also found downregulated in gastric tumors (Fig. 4A and Supplementary Table 10). The pathways nuclear erythroid 2-related factor (NRF2), amino acid/tryptophan metabolism, glycosis and gluconeogenesis were also found enriched among the downregulated genes (Fig. 4B and Supplementary Table 11). In an independent gene-set enrichment analysis performed with the GeneCodis tool, the downregulated genes were found enriched with the processes such as metabolism of xenobiotics by cytochrome P450, gastric acid secretion, drug metabolism by cytochrome P450, pancreatic secretion, retinol metabolism and glycosis/gluconeogenesis (Fig. 4C). Notably, loss of the physiology and functions of stomach such as digestion, gastric acid secretion and absorption seems downregulated in gastric tumors. Whole transcriptome analysis reveals the physiological changes in gastric tumors compared to non-cancerous gastric tissues. This is a comprehensive genome-wide expression landscape covering whole transcriptome from Madurai, South India. This study also reveals the multiple markers, pathways and molecular processes which could be clinically useful for their diagnostic, prognostic and therapeutic features upon further evaluation. Pattern of the dysregulation of major oncogenic pathways in gastric tumors Gene set based pathway activation analysis was performed for a few oncogenic pathways across the gastric tumor samples, to understand the pattern of their activation. The coexpressed genes were derived for a panel of oncogenic pathways (Supplementary Table 12). Upon analysis in Madurai cohort of gastric tumors, TGF-β, OCT4, ECM, E2F, ERK, HNF4, PPARγ and YY1 were found to be extremely activated in gastric tumors compared to the adjacent normal gastric tissues (Fig. 5A, B). In contrary, the estrogen receptor (ESR) pathway was found activated in normal samples with a very clear downregulation in gastric tumors (Fig. 5B, C). These results corroborate with the observation from the functional enrichment analysis among the genes up and downregulated in gastric tumors. This analysis shows the promising candidacy of TGF-β, OCT4, ECM, E2F, ERK, YY1, HNF4, PPARγ and ESR pathways for the differential targeting. These leads would pave a way for the development of appropriate diagnostics and targeted therapeutic strategies. Molecular disparity between Madurai and TCGA cohorts of gastric tumors The Cancer Genome Atlas (TCGA) and Asian Cancer Research Group (ACRG) studies have reported four major molecular subtypes of gastric tumors [35, 15]. The molecular subtypes defined by TCGA study include Epstein Barr Virus (EBV), microsatellite-instable (MSI), Genomically Stable (GS) and Chromosomal Instabiltiy (CIN). The ACRG study has defined the independent subtypes as microsatellite-instable (MSI), microsatellite-stable MSS/TP53−, MSS/TP53+, and epithelial-to-mesenchymal transition (EMT). The occurrence of these subtypes was investigated in Madurai cohort gastric tumors with the above listed subtype specific gene sets from TCGA and ACRG studies. Gene set based Z -score computing was performed to analyze the expression pattern of these gene sets across Madurai cohort gastric tumors as well as in the TCGA and ACRG samples. The number of samples activated under each subtype was calculated and a threshold of two-fold cut off was considered [14, 36, 37]. Analysis of the TCGA molecular subtypes in Madurai cohort revealed GS subtype to be predominantly activated in ~40% of samples. EBV and MSI subtypes were found activated in ~30% of samples. However in TCGA, cohort CIN subtype was observed to have high frequent activation of ~38% followed by GS subtype which occurs in ~25% of samples (Fig. 6A, B). From this analysis of the expression pattern of TCGA subtype specific gene sets across TCGA and Madurai cohort samples, we could observe differing prevalence of TCGA gastric cancer subtypes between different cohorts, which are from Europe a major fraction of non-Asian (TCGA) and South India (Madurai). Among ACRG subtypes,EMT subtype was found to be predominant one with ~50% of activation in Madurai and ~40% in ACRG cohort. MSI subtype occurs in ~28% of Madurai and ~22% of ACRG cohorts (Fig. 6C, D). Notably MSI and proliferation gene signatures were also found co-activated in a subset of gastric tumors in Madurai cohort as well as in ACRG samples. While the differing frequency of occurrence of subtypes is predominant between Madurai and TCGA cohort, this was not the case with ACRG cohort (Fig. 6E, F). This disparity warrants a detailed investigation. It is worth mentioning that the current analysis was merely based on the activation pattern of gene sets. While this approach has advantages, the subtyping in TCGA and ACRG studies were based on subtype-related molecular/clinical assays. However the current comparison was made in a uniform manner for all the datasets. Discussion Carcinogenesis and progression are due to the cumulative impact of genomic and epigenetic aberrations [ 38 ]. Understanding the molecular dysregulations involved in gastric cancer would pave a way for the development of improved diagnostic and targeted therapeutic strategies. While a few molecular and genomic subtypes have been established in gastric cancer, it is being necessary to investigate in multiple cohorts and to identify the therapeutic targets for different subtypes [ 39 – 41 ]. Earlier, the intrinsic subtypes of gastric tumors were found to have association with therapeutic response with differential sensitivity to 5-fluorouracil and oxaliplatin [ 42 ]. Based on the gene expression profiles from Singapore and Australian cohorts, gastric tumors have been classified into proliferative, metabolic and mesenchymal subtypes. Proliferative subtype of gastric tumors was characterized with high genomic instability, elevated expression of cell cycle genes, activated RAS, E2F, MYC pathways, TP53 mutations, and the feature of intestinal subtype. Mesenchymal subtype exhibited the features of cancer stem cell, low CDH1, enriched focal adhesion and ECM receptor genes, activated TGF-β, NF-𝜅B, mTOR, VEGF, sonic hedgehog pathways, association with diffuse subtypes, low copy number aberrations and sensitivity to PI3K-AKT-mTOR inhibitors. Metabolic subtype showed the enrichment of metabolic pathways, digestion related genes and sensitivity to 5-Fluorouracil [ 43 ]. The Cancer Genome Atlas (TCGA) cohort comprises gastric tumors from 25% Asian ethnic samples and 75% of samples from non-Asian populations. The study has proposed four molecular subtypes of gastric adenocarcinoma: i) Epstein-Barr virus positive tumors, ii) Microsatellite unstable, iii) Genomically stable, and iv) Tumors with chromosomal instability [ 35 ]. Another study from Asian Cancer Research Group (ACRG) has established four different molecular subtypes of gastric tumors: i) Mesenchymal, ii) Microsatellite instable, iii) TP53-active, and iv) TP53-inactive subtype [ 15 ]. Whole genome/exome sequencing of gastric tumor samples has identified i) subtype specific genetic perturbations with RHOA mutations in 14.3% of diffuse subtype tumors [ 44 ], ii) PIK3CA mutations with the frequency of 25% in tumors [ 45 ], and iii) frequent alteration of many components of RAS/RAF/MAPK/ERK cascades with high frequent mutation in KRAS, MAPK3 and MAP2K4 genes [ 46 ]. In the light of these studies and with the knowledge of functional genomic alterations, the potential therapeutic targets for gastric tumors have been explored. A comprehensive whole genome expression landscape of Indian cohort is yet to be established and was addressed in the present study. A global view of the mRNA expression landscape of Madurai, India cohort comprising 50 gastric tumor and 15 adjacent gastric normal samples has been established. So far, there are three whole genome expression profiles established for gastric tumors from India. A profile from Chennai, India has explored 24 gastric tumors, 5 apparently normal and 5 paired gastric normal samples [ 47 ]. Another profile from New Delhi, India was with 2 gastric normal and 5 gastric tumor samples [GSE20143]. Another profile from Bangalore, India has 14 pairs of gastric normal and tumor samples [ 48 ]. The dysregulated genes from Madurai cohort show larger overlap with the genes from Bangalore cohort of gastric tumors. However such a comparison is cumbersome due to the different platforms, sample subtypes and other conditions. The differentially expressed genes in gastric tumor samples and the deregulated molecular signaling pathways were explored. The highly expressed genes in the current study such as SPP1, COL1A2, and THBS2 have been reported as predictors of gastric cancer prognosis [ 49 ]. Similarly, the other identified genes BGN, SULF1 and INHBA were all reported to be upregulated and associated with gastric cancer progression in other cohorts of gastric tumors [ 21 , 50 , and 51 ]. Similarly the downregulated genes of current cohort, ATP4A, ATP4B, GKN1, GIF, LIPF and PGA4 were also reported in multiple cohorts and the functional deficiency of these genes leads to the gastric cancer progression [ 52 ]. Further, we set out to analyze the overlap in the differentially expressed genes among various cohorts of gastric tumors across globe. A collection of 23 gene sets from different cohorts of gastric tumors across the globe were analyzed. The differentially expressed genes found to overlap between our cohort and other cohorts across the globe were identified. The differentially expressed genes from Madurai cohort were found to overlap mostly with China, South Korea, Germany, Argentina, USA, Japan, and India. It is worth mentioning that the different gastric tumor cohorts were found to show 0.2% − 24% of gene overlap among them in general. Of these, Madurai cohort exhibits 21% overlap with the differentially expressed genes from other cohorts. However, such comparisons are difficult due to differing study designs, platforms, normalization methods, gene selection criteria, etc., [ 53 ]. The ECM-receptor interaction pathway has been identified in multiple cancers and its essential role in cancer progression is well known [ 54 ]. The inflammatory response associated signaling such as the chemokine signaling, Jak/Stat and NF-𝜅B pathways have been reported in gastric cancer. The upregulated genes involved in the chemokine signaling such as CXCL5 and CXCL8 are known to have association with chemo-attracting pro-tumor neutrophils, which increase the risk of chronic inflammation leading to cancer, and metastasis of gastric cancer cells [ 55 ]. The transcription factors and the signaling pathways regulated by SP1, LEF, EGFR, RAS/MAPK, TGF-β and VEGF were found enriched among the genes upregulated in the gastric tumors in the current cohort. SP1 also has been identified for the association with the depth of invasion and TNM stage of gastric cancer. SP1 has been indicated as a potential marker for the prognosis of gastric cancer patients [ 56 ]. Among the genes downregulated in gastric tumors, we could observe the enrichment of the binding sites for a NFAT, HNF3, and TP53. The downregulated genes were found to involve in the digestion process and the metabolisms related to amino acid, fatty acid, bile acid, xenobiotics by cytochrome p450, glycolysis and gluconeogenesis. The other enriched pathways include gastric acid secretion, pancreatic secretion, protein digestion, absorption and retinol metabolism. Gastric acid secretion has been reported to lead to various diseases in the stomach, such as gastroesophageal reflux disease, chronic atrophic gastritis and gastric cancer [ 57 , 58 ]. It has been reported that the majority of patients with advanced gastric cancer to experience nutritional deficiency, which, in combination with surgical trauma, also to cause post-operative immune dysfunction and malnutrition with the impact on recovery [ 59 – 61 ]. This makes sense with the current observation of the diminished expression of the genes involved in absorption and digestion. Transcription programs and signaling pathways are the most prominent therapeutic targets [ 40 , 62 ]. In the current study, the gene sets representing different oncogenic signaling pathways were derived by integrative genomic approach and their activation patterns were analyzed. TGF-β, OCT4, ECM, E2F, ERK and YY1 gene sets showed extreme activation in gastric tumors than normal samples with the contrasting pattern for ESR gene set. The predominant occurrence of gastric cancer in men compared to women is known [ 63 , 64 ]. ER-β has been reported to suppress the progression of gastric cancer [ 65 ]. Patients with higher expression of ER-β were reported to show better survival with the clinical features of lower tumor differentiation, Lauren's intestinal type, and negative association with lymph node metastasis [ 63 – 64 , 66 – 68 ]. This makes sense with the current observation of the suppression of ER signaling in gastric tumors. The anti-estrogen drug tamoxifen has also been identified to increase the risk of gastric adenocarcinoma [ 69 ]. The identified transcription factors and pathways might work as potential targets for the development of targeted therapeutics for gastric cancer. Due to the lack of comparable subtype specific clinical results, the occurrence of subtypes across cohorts were compared based on the activation pattern of subtypes specific gene sets. The current analysis also shows molecular disparity between cohorts specifically the differing prevalence of GS subtype between TCGA and Madurai cohort. This warrants a detailed investigation to tailor the therapeutic regimens in the context of disparity [ 70 ]. The current cohort adds to the array of gastric tumor expression profiles and provides a comprehensive overview of all the dysregulated transcription factors and signaling pathways. The current identification of transcription factors, signaling pathways and physiological processes would pave a way for the development of new therapies for gastric cancer. Declarations Acknowledgements The grant support from Department of Biotechnology (DBT), Government of India, through the Unit of Excellence (UOE) in Cancer Genetics research grant BT/MED/30/SP11290/2015 is acknowledged. The support of Histopathology Division, Dept. of Laboratory Services and Dept. of Surgery & Surgical Gastroenterology, Meenakshi Mission Hospital & Research Centre, Madurai, India is acknowledged. We acknowledge the core facility support from UGC-CEGS, MKU-RUSA and DST-FIST programs of School of Biological Sciences, Madurai Kamaraj University, Madurai. Funding This work was funded by the Department of Biotechnology (DBT), Government of India, through the Unit of Excellence (UOE) in Cancer Genetics research grant BT/MED/30/SP11290/2015 to Dr. Kumaresan Ganesan, Madurai Kamaraj University. Author contributions Kumaresan Ganesan and Jaishree Pandian conceived the study and designed the experiments. Ramesh Ardhanari, Vikash Vittal, Satyajit Patwardhan and Mohan Narasimhan were involved in collecting the samples. Madhusudhanan Gnanasekaran and Indu Kannan performed the histopathological investigations. Vikash Vittal collected and comprehended the clinical information. Jaishree Pandian, Balaji T Sekar, Helen D Jemimah, Ponmathi Paneerpandian, Prem Suresh, and Karthikeyan Selvarasu performed the experiments. Jaishree Pandian and Kumaresan Ganesan analyzed the data and wrote the paper. Conflict of Interest The authors disclose no potential conflicts of interest. Data availability statement The data that supports the findings of this study will be available in Gene Expression Omnibus (GEO) repository database with the accession ID GSE146996. References Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61:69–90. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66:115–32. Dikshit R, Gupta PC, Ramasundarahettige C, Gajalakshmi V, Aleksandrowicz L, Badwe R, et al. Cancer mortality in India: a nationally representative survey. Lancet. 2012;379:1807–16. Rao DN, Ganesh B. Estimate of cancer incidence in India in 1991. Indian J Cancer. 1998;35:10–8. Servarayan Murugesan C, Manickavasagam K, Chandramohan A, Jebaraj A, Jameel ARA, Jain MS, et al. Gastric cancer in India: epidemiology and standard of treatment. Updates Surg. 2018;70:233–9. Thirumurthi S, Graham DY. Helicobacter pylori infection in India from a western perspective. Indian J Med Sci. 2010;64:423–40. Fuchs CS, Mayer RJ. Gastric carcinoma. N Engl J Med. 1995;333:32–41. Riquelme I, Saavedra K, Espinoza JA, Weber H, García P, Nervi B, et al. Molecular classification of gastric cancer: Towards a pathway-driven targeted therapy. Oncotarget. 2015;6:24750–79. Hu YC, Lam KY, Law S, Wong J, Srivastava G. Identification of differentially expressed genes in esophageal squamous cell carcinoma (ESCC) by cDNA expression array: overexpression of Fra-1, Neogenin, Id-1, and CDC25B genes in ESCC. Clin Cancer Res. 2001;7:2213–21. Chen K-N, Gu Z-D, Ke Y, Li J-Y, Shi X-T, Xu G-W. Expression of 11 HOX genes is deregulated in esophageal squamous cell carcinoma. Clin Cancer Res. 2005;11:1044–9. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene-set collection. Cell Syst. 2015;1:417–25. Carmona-Saez P, Chagoyen M, Tirado F, Carazo JM, Pascual-Montano A. GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome Biol. 2007;8:R3. Levine DM, Haynor DR, Castle JC, Stepaniants SB, Pellegrini M, Mao M, Johnson JM. Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways. Genome Biol. 2006;7(10):R93. doi: 10.1186/gb-2006-7-10-r93. Epub 2006 Oct 17. PMID: 17044931; PMCID: PMC1794557 Cristescu R, Lee J, Nebozhyn M, Kim K-M, Ting JC, Wong SS, et al. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med. 2015;21:449–56. Sohn BH, Hwang JE, Jang HJ, Lee HS, Oh SC, Shim JJ, Lee KW, Kim EH, Yim SY, Lee SH, Cheong JH, Jeong W, Cho JY, Kim J, Chae J, Lee J, Kang WK, Kim S, Noh SH, Ajani JA, Lee JS. Clinical Significance of Four Molecular Subtypes of Gastric Cancer Identified by The Cancer Genome Atlas Project. Clin Cancer Res. 2017 Jul 26:10.1158/1078-0432.CCR-16-2211. doi: 10.1158/1078-0432.CCR-16-2211. Epub ahead of print. PMID: 28747339; PMCID: PMC5785562. Li C, Wong WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA. 2001;98:31–6. Imano M, Satou T, Itoh T, Sakai K, Ishimaru E, Yasuda A, et al. Immunohistochemical expression of osteopontin in gastric cancer. J Gastrointest Surg. 2009;13:1577–82. Song G, Ouyang G, Mao Y, Ming Y, Bao S, Hu T. Osteopontin promotes gastric cancer metastasis by augmenting cell survival and invasion through Akt-mediated HIF-1alpha up-regulation and MMP9 activation. J Cell Mol Med. 2009;13:1706–18. Dai N, Bao Q, Lu A, Li J. Protein expression of osteopontin in tumor tissues is an independent prognostic indicator in gastric cancer. Oncology. 2007;72:89–96. Hu L, Duan Y-T, Li J-F, Su L-P, Yan M, Zhu Z-G, et al. Biglycan enhances gastric cancer invasion by activating FAK signaling pathway. Oncotarget. 2014;5:1885–96. Huang L, Wu R-L, Xu A-M. Epithelial-mesenchymal transition in gastric cancer. Am J Transl Res. 2015;7:2141–58. Shen, B.F., Li, M., Wang, H., Xin, L., & Xie, J. Expression and clinical significance of the RAS / RAF / MAPK cell signaling pathway in gastric cancer. Int J Clin Exp Med 2018;11:11682-11689 Cui J, Chen Y, Chou W-C, Sun L, Chen L, Suo J, et al. An integrated transcriptomic and computational analysis for biomarker identification in gastric cancer. Nucleic Acids Res. 2011;39:1197–207. Yoon JH, Song JH, Zhang C, Jin M, Kang YH, Nam SW, et al. Inactivation of the Gastrokine 1 gene in gastric adenomas and carcinomas. J Pathol. 2011;223:618–25. Dai J, Zhang N, Wang J, Chen M, Chen J. Gastrokine-2 is downregulated in gastric cancer and its restoration suppresses gastric tumorigenesis and cancer metastasis. Tumour Biol. 2014;35:4199–207. Abrams CK, Hamosh M, Lee TC, Ansher AF, Collen MJ, Lewis JH, et al. Gastric lipase: localization in the human stomach. Gastroenterology. 1988;95:1460–4. Zhang X, Yang J-J, Kim YS, Kim K-Y, Ahn WS, Yang S. An 8-gene signature, including methylated and down-regulated glutathione peroxidase 3, of gastric cancer. Int J Oncol. 2010;36:405–14. Miller C. An overview of the potassium channel family. Genome Biol. 2000;1:REVIEWS0004. Roepke TK, Purtell K, King EC, La Perle KMD, Lerner DJ, Abbott GW. Targeted deletion of Kcne2 causes gastritis cystica profunda and gastric neoplasia. PLoS ONE. 2010;5:e11451. Raja UM, Gopal G, Rajkumar T. Intragenic DNA methylation concomitant with repression of ATP4B and ATP4A gene expression in gastric cancer is a potential serum biomarker. Asian Pac J Cancer Prev. 2012;13:5563–8. Shen S, Jiang J, Yuan Y. Pepsinogen C expression, regulation and its relationship with cancer. Cancer Cell Int. 2017;17:57. Boltin D, Niv Y. Mucins in Gastric Cancer - An Update. J Gastrointest Dig Syst. 2013;3:15519. Katoh M. Trefoil factors and human gastric cancer (review). Int J Mol Med. 2003;12:3–9. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513:202–9. Smith AM, Walsh JR, Long J, Davis CB, Henstock P, Hodge MR, Maciejewski M, Mu XJ, Ra S, Zhao S, Ziemek D, Fisher CK. Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data. BMC Bioinformatics. 2020 Mar 20;21(1):119. doi: 10.1186/s12859-020-3427-8. PMID: 32197580; PMCID: PMC7085143. Cheadle C, Vawter MP, Freed WJ, Becker KG. Analysis of microarray data using Z score transformation. J Mol Diagn. 2003 May;5(2):73-81. doi: 10.1016/S1525-1578(10)60455-2. PMID: 12707371; PMCID: PMC1907322. Correa P. A human model of gastric carcinogenesis. Cancer Res. 1988;48:3554–60. Pfeifer SP. From next-generation resequencing reads to a high-quality variant data set. Heredity (Edinb). 2017;118:111–24. Holbrook JD, Parker JS, Gallagher KT, Halsey WS, Hughes AM, Weigman VJ, et al. Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine. J Transl Med. 2011;9:119. Lim B, Kim J-H, Kim M, Kim S-Y. Genomic and epigenomic heterogeneity in molecular subtypes of gastric cancer. World J Gastroenterol. 2016;22:1190–201. Tan IB, Ivanova T, Lim KH, Ong CW, Deng N, Lee J, et al. Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy. Gastroenterology. 2011;141:476–85, 485.e1-11. Lei Z, Tan IB, Das K, Deng N, Zouridis H, Pattison S, et al. Identification of molecular subtypes of gastric cancer with different responses to PI3-kinase inhibitors and 5-fluorouracil. Gastroenterology. 2013;145:554–65. Wang K, Yuen ST, Xu J, Lee SP, Yan HHN, Shi ST, et al. Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer. Nat Genet. 2014;46:573–82. Karakas B, Bachman KE, Park BH. Mutation of the PIK3CA oncogene in human cancers. Br J Cancer. 2006;94:455–9. Zang, Z.J., C.K. Ong, I. Cutcutache, et al. 2011. Genetic and structural variation in the gastric cancer kinome revealed through targeted deep sequencing. Cancer Res. 71: 29–39. Rajkumar T, Vijayalakshmi N, Gopal G, Sabitha K, Shirley S, Raja UM, et al. Identification and validation of genes involved in gastric tumorigenesis. Cancer Cell Int. 2010;10:45. Marimuthu A, Jacob HKC, Jakharia A, Subbannayya Y, Keerthikumar S, Kashyap MK, et al. Gene Expression Profiling of Gastric Cancer. J Proteomics Bioinform. 2011;4:74–82. Zhuo C, Li X, Zhuang H, Tian S, Cui H, Jiang R, et al. Elevated THBS2, COL1A2, and SPP1 Expression Levels as Predictors of Gastric Cancer Prognosis. Cell Physiol Biochem. 2016;40:1316–24. Junnila S, Kokkola A, Mizuguchi T, Hirata K, Karjalainen-Lindsberg M-L, Puolakkainen P, et al. Gene expression analysis identifies over-expression of CXCL1, SPARC, SPP1, and SULF1 in gastric cancer. Genes Chromosomes Cancer. 2010;49:28–39. Katayama Y, Oshima T, Sakamaki K, Aoyama T, Sato T, Masudo K, et al. Clinical Significance of INHBA Gene Expression in Patients with Gastric Cancer who Receive Curative Resection Followed by Adjuvant S-1 Chemotherapy. In Vivo. 2017;31:565–71. Fei H-J, Chen S-C, Zhang J-Y, Li S-Y, Zhang L-L, Chen Y-Y, et al. Identification of significant biomarkers and pathways associated with gastric carcinogenesis by whole genome-wide expression profiling analysis. Int J Oncol. 2018;52:955–66. Walsh CJ, Hu P, Batt J, Santos CCD. Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery. Microarrays (Basel). 2015;4:389–406. Krupp M, Maass T, Marquardt JU, Staib F, Bauer T, König R, et al. The functional cancer map: a systems-level synopsis of genetic deregulation in cancer. BMC Med Genomics. 2011;4:53. Mao Y, Zhao Q, Yin S, Ding X, Wang H. Genome-wide expression profiling and bioinformatics analysis of deregulated genes in human gastric cancer tissue after gastroscopy. Asia Pac J Clin Oncol. 2018;14:e29–36. Shi S, Zhang Z-G. Role of Sp1 expression in gastric cancer: A meta-analysis and bioinformatics analysis. Oncol Lett. 2019;18:4126–35. Di Mario F, Goni E. Gastric acid secretion: changes during a century. Best Pract Res Clin Gastroenterol. 2014;28:953–65. Martinsen TC, Bergh K, Waldum HL. Gastric juice: a barrier against infectious diseases. Basic Clin Pharmacol Toxicol. 2005;96:94–102. Ishizuka M, Oyama Y, Abe A, Tago K, Tanaka G, Kubota K. Prognostic nutritional index is associated with survival after total gastrectomy for patients with gastric cancer. Anticancer Res. 2014;34:4223–9. Tegels JJW, De Maat MFG, Hulsewé KWE, Hoofwijk AGM, Stoot JHMB. Improving the outcomes in gastric cancer surgery. World J Gastroenterol. 2014;20:13692–704. Sun K, Chen S, Xu J, Li G, He Y. The prognostic significance of the prognostic nutritional index in cancer: a systematic review and meta-analysis. J Cancer Res Clin Oncol. 2014;140:1537–49. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–7. Ryu W-S, Kim J-H, Jang Y-J, Park S-S, Um J-W, Park S-H, et al. Expression of estrogen receptors in gastric cancer and their clinical significance. J Surg Oncol. 2012;106:456–61. Kim HW, Kim J-H, Lim BJ, Kim H, Kim H, Park JJ, et al. Sex Disparity in Gastric Cancer: Female Sex is a Poor Prognostic Factor for Advanced Gastric Cancer. Ann Surg Oncol. 2016;23:4344–51. Zhang Y, Wu Y, Zhou X, Yi B, Wang L. Estrogen Receptor Beta Inhibits The Proliferation, Migration, And Angiogenesis Of Gastric Cancer Cells Through Inhibiting Nuclear Factor-Kappa B Signaling. Onco Targets Ther. 2019;12:9153–64. Xu CY, Guo JL, Jiang ZN, Xie SD, Shen JG, Shen JY, et al. Prognostic role of estrogen receptor alpha and estrogen receptor beta in gastric cancer. Ann Surg Oncol. 2010;17:2503–9. Zhang D, Ku J, Yi Y, Zhang J, Liu R, Tang N. The prognostic values of estrogen receptor alpha and beta in patients with gastroesophageal cancer: A meta-analysis. Medicine (Baltimore). 2019;98:e17954. Ge H, Yan Y, Tian F, Wu D, Huang Y. Prognostic value of estrogen receptor α and estrogen receptor β in gastric cancer based on a meta-analysis and The Cancer Genome Atlas (TCGA) datasets. Int J Surg. 2018;53:24–31. Nie X, Xie R, Tuo B. Effects of Estrogen on the Gastrointestinal Tract. Dig Dis Sci. 2018;63:583–96. Li H, Wang C, Wei Z, et al. Differences in the prognosis of gastric cancer patients of different sexes and races and the molecular mechanisms involved. Int J Oncol . 2019;55:1049-1068. Supplementary Files GastrictumorMSsupplementarytable.xlsx Supplementary Table 1: Genes upregulated in Madurai cohort gastric tumor samples compared to adjacent gastric normal samples are shown along with fold change and P -value.Supplementary Table 2: Genes downregulated in Madurai cohort gastric tumor samples compared to adjacent gastric normal samples are shown along with fold change and P -value.Supplementary Table 3: The details of the expression profiles of gastric tumors used in the study along with the microarray platform used, characteristics of samples, number of samples and details of publication. Supplementary Table 4: The lists of upregulated genes in Madurai cohort and the overlap across cohorts.Supplementary Table 5: The lists of downregulated genes in Madurai cohort and the overlap across cohorts..Supplementary Table 6: The list of genes upregulated in gastric tumors which are also reported for their association with gastric cancer progression from previous studies are shown with citation.Supplementary Table 7: The list of gene signatures enriched among upregulated genes of gastric tumors in the analysis with MSigDB. Supplementary Table 8: The list of pathways enriched among upregulated genes of gastric tumors in the analysis with TAC. Supplementary Table 9: The list of genes downregulated in gastric tumors which are also reported for their dysregulation in gastric cancer from previous studies are shown with citation.Supplementary Table 10: The list of gene signatures enriched among downregulated genes of gastric tumors in the analysis with MSigDB. Supplementary Table 11: The list of pathways enriched among downregulated genes of gastric tumors in the analysis with TAC.Supplementary Table 12: The list of the derived gene sets for major oncogenic signaling pathways. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1476204","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":93461289,"identity":"0f242c8d-bc6d-4e95-9dd6-cf92dc9f3c69","order_by":0,"name":"Jaishree Pandian","email":"","orcid":"","institution":"Madurai Kamaraj University School of Biological Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jaishree","middleName":"","lastName":"Pandian","suffix":""},{"id":93461290,"identity":"1ca25fbf-e31b-461f-b4a0-4d81467d0ad3","order_by":1,"name":"Madhusudhanan Gnanasekaran","email":"","orcid":"","institution":"Meenakshi Mission Hospital and Research Centre","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Madhusudhanan","middleName":"","lastName":"Gnanasekaran","suffix":""},{"id":93461291,"identity":"1d8ce5c7-c14f-4487-b72d-fdfc60f9211b","order_by":2,"name":"Vikash Vittal","email":"","orcid":"","institution":"Meenakshi Mission Hospital and Research Centre","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vikash","middleName":"","lastName":"Vittal","suffix":""},{"id":93461292,"identity":"8ddda30e-ba59-4c7b-aa9d-96f33af25d35","order_by":3,"name":"Balaji T Sekar","email":"","orcid":"","institution":"Madurai Kamaraj University School of Biological Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Balaji","middleName":"T","lastName":"Sekar","suffix":""},{"id":93461293,"identity":"32e0fef1-2d98-4765-a671-210a9e5ef136","order_by":4,"name":"Helen D Jemimah","email":"","orcid":"","institution":"Madurai Kamaraj University School of Biological Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Helen","middleName":"D","lastName":"Jemimah","suffix":""},{"id":93461294,"identity":"8c4be747-6acf-4854-ad9a-310115ace6a7","order_by":5,"name":"Ponmathi Panneerpandian","email":"","orcid":"","institution":"Madurai Kamaraj University School of Biological Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ponmathi","middleName":"","lastName":"Panneerpandian","suffix":""},{"id":93461295,"identity":"bda29b5e-c88f-4414-8178-61371f834dc6","order_by":6,"name":"Satyajit Patwardhan","email":"","orcid":"","institution":"Meenakshi Mission Hospital and Research Centre","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Satyajit","middleName":"","lastName":"Patwardhan","suffix":""},{"id":93461296,"identity":"08b66410-b88b-4806-aff1-dc2fb2abefde","order_by":7,"name":"Indhu Kannan","email":"","orcid":"","institution":"Meenakshi Mission Hospital and Research Centre","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Indhu","middleName":"","lastName":"Kannan","suffix":""},{"id":93461297,"identity":"e78bb366-24a2-4129-8e63-8f55079f2114","order_by":8,"name":"Prem Suresh","email":"","orcid":"","institution":"Madurai Kamaraj University School of Biological Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Prem","middleName":"","lastName":"Suresh","suffix":""},{"id":93461298,"identity":"3c4099d0-def9-44f8-8460-3fece60281ba","order_by":9,"name":"Karthikeyan Selvarasu","email":"","orcid":"","institution":"Madurai Kamaraj University School of Biological Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Karthikeyan","middleName":"","lastName":"Selvarasu","suffix":""},{"id":93461299,"identity":"36ce4031-4225-47b3-919e-baf7e5a5b8bd","order_by":10,"name":"Mohan Narasimhan","email":"","orcid":"","institution":"Meenakshi Mission Hospital and Research Centre","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohan","middleName":"","lastName":"Narasimhan","suffix":""},{"id":93461300,"identity":"d1dcbdf2-713f-47a4-80b8-7f28b869cf19","order_by":11,"name":"Ramesh Ardhanari","email":"","orcid":"","institution":"Meenakshi Mission Hospital and Research Centre","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ramesh","middleName":"","lastName":"Ardhanari","suffix":""},{"id":93461301,"identity":"fc8cd15b-7139-4a9b-9a1a-8db67b297e02","order_by":12,"name":"Kumaresan Ganesan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACCTBZAeU9KGBgYCNOyxkoL8GAWC2MbUhaCALJGdmJnyvn2eTxtzc/fJBgYBPNx378AcPHPbU4tUhL5G6WPLstrVjizDFjgwSDtNw2nhwDxhnPjuPUIieRu0GycdvhxA0SOWwSCQaHc9skeBiYeQ4cw6dl88/GOWAt7D8gWtgf4NUCdNg2ycYGiC0MEC0MBkAtNbi93/N2m2XDsbTEGUC/SMD8cnDGgQM4tUgcz918s6HGJrEfGGIfPlTY5M5vP/7wwYcDdTi1YAdAKw6TqAUISLVlFIyCUTAKhjEAAPIZVuY5cHq/AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0533-3181","institution":"Madurai Kamaraj University School of Biological Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kumaresan","middleName":"","lastName":"Ganesan","suffix":""}],"badges":[],"createdAt":"2022-03-22 06:13:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1476204/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1476204/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19736532,"identity":"d0b4912a-f223-45a4-975e-3b28f513dc7a","added_by":"auto","created_at":"2022-03-29 14:03:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":306199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome-wide mRNA profiling of gastric tumor and adjacent normal samples collected from Madurai, India\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e Flow-chart depicting the collection, quality analysis and processing of gastric tumor and adjacent gastric normal samples from Madurai, India. \u003cstrong\u003eB\u003c/strong\u003e Principal Component Analysis plot shows the independent grouping of gastric tumor and normal samples. The overall variations covered by PCA are 40% and shows the samples to be highly heterogeneous.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1476204/v1/e53901a75682a24c475e37ba.png"},{"id":19736000,"identity":"dc5752b4-a339-45e9-b88e-e7a177d1630d","added_by":"auto","created_at":"2022-03-29 13:58:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed genes in the gastric tumors from the cohort of Madurai, India and comparison with the genes dysregulated in 23 different cohorts of gastric tumors.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e Gene-level RMA normalization was performed and differentially expressed genes with fold change ≥2 and ≤-2 with p-value ≤0.05 in gastric tumors compared to normal samples were derived. Totally, 1005 genes got upregulated and 388 genes got downregulated in 50 gastric tumors compared to 15 normal gastric tissues.\u0026nbsp;Heatmap shows expression pattern of differentially expressed genes between gastric tumor and gastric normal samples. Each column represents a sample and each row represents a gene. Red colour indicates the upregulated genes and green colour indicates the downregulated genes. \u003cstrong\u003eB\u003c/strong\u003e The genes differentially expressed in Madurai cohort were analyzed for their differential expression across 23 other cohorts of gastric tumors across the globe. Of the analyzed cohorts and among the 1005 upregulated genes from Madurai cohort, 29 genes were found to have elevated expression in at least 10 other cohorts, and 32 genes show a consistent pattern of diminished expression in at least 10 other cohorts.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1476204/v1/df72596409b2135fc1ce4d3c.png"},{"id":19736004,"identity":"423998b9-60ce-4478-a90f-54bb03dbcd0f","added_by":"auto","created_at":"2022-03-29 13:58:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128923,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional genomic investigation of the genes upregulated in gastric tumors\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e Gene set enrichment analysis of the genes upregulated in Madurai cohort using MSigDB shows the enrichment of transcription factors and oncogenes such as SP1, LEF, EGFR, E2F, RAS and VEGF among gastric tumors. \u003cstrong\u003eB\u003c/strong\u003e Molecular functional analysis using TAC shows the involvement of focal adhesion, epithelial to mesenchymal transitions (EMT), cell cycle, Notch, TGF-β, MAPK and EGFR pathways among the genes upregulated in Madurai cohort of gastric tumors. \u003cstrong\u003eC\u003c/strong\u003e KEGG pathway enrichment analysis using GeneCodis also show the involvement of focal adhesion, phagosome, MAPK, cell cycle, ECM and actin cytoskeleton related pathways.\u0026nbsp;\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1476204/v1/a4b7d3a81adf1371a4caaa6c.png"},{"id":19736006,"identity":"929f9c5f-dda8-4a93-98ab-9dc0a8008f10","added_by":"auto","created_at":"2022-03-29 13:58:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":128885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional genomic investigation of the genes downregulated in Madurai cohort\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e The downregulated genes show association with the transcription regulators HNF, ESR, BMP2, TP53 and PRC2/SUZ12/EED. \u003cstrong\u003eB\u003c/strong\u003e Molecular functional analysis reveals the enrichment of NRF2, nuclear receptor pathway, metabolism including amino acid metabolism, glycolysis and gluconeogenesis. \u003cstrong\u003eC\u003c/strong\u003e KEGG pathway analysis shows the metabolic pathways and the physiological processes like digestion, absorption and secretion to be enriched among the genes downregulated in gastric tumors.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-1476204/v1/fb0ef0a8ef31d58b7bae951e.png"},{"id":19736003,"identity":"38a03063-01a9-4df2-b6b2-b6c949ce2631","added_by":"auto","created_at":"2022-03-29 13:58:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78490,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of the gene set based activation pattern of the major oncogenic pathways in Madurai cohort of gastric tumors. A-B\u003c/strong\u003e Gene set based activation pattern of the derived gene sets for major oncogenic signaling pathways in Madurai cohort shows the activation of TGF-β, OCT4, ECM, E2F, ERK/MAPK and YY1 signaling pathways in gastric tumors compared to the non-tumor gastric samples. The gene set activation represented by \u003cem\u003eZ\u003c/em\u003e-score is shown as heatmap (\u003cstrong\u003eA\u003c/strong\u003e) and also represented in a plot (\u003cstrong\u003eB\u003c/strong\u003e). In the X-axis, N represents non-tumor gastric samples and T is gastric tumor samples. \u003cstrong\u003eC\u003c/strong\u003e Compared to most of normal samples, gastric tumors show the diminished expression of Estrogen receptor (ESR) signaling pathway genes.\u0026nbsp;\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1476204/v1/cd0bbe17b302288b1dd74a2a.png"},{"id":19736533,"identity":"259c706a-2d70-4b45-8edb-64fd99c8922b","added_by":"auto","created_at":"2022-03-29 14:03:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":86933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of TCGA and ACRG molecular subtypes across Madurai cohort gastric tumor samples\u003c/strong\u003e. \u003cstrong\u003eA-B\u003c/strong\u003e Activation pattern of the subtype specific gene signatures of TCGA across TCGA samples (\u003cstrong\u003eA\u003c/strong\u003e) and Madurai cohort samples (\u003cstrong\u003eB\u003c/strong\u003e) reveals variation in the activation percentage of subtypes among samples. \u003cstrong\u003eC-D\u003c/strong\u003e Activation pattern of the gene signatures used in the subtype classification scheme of ACRG study shows the predominant activation pattern of EMT and MSI gene signatures in ACRG samples (\u003cstrong\u003eC\u003c/strong\u003e) and Madurai cohort gastric tumor samples (\u003cstrong\u003eD\u003c/strong\u003e). \u003cstrong\u003eE-F\u003c/strong\u003e The percentage of samples activated under each subtypes of TCGA and ACRG studies across Madurai cohort as well as in the TCGA (\u003cstrong\u003eE\u003c/strong\u003e) and ACRG (\u003cstrong\u003eF\u003c/strong\u003e) cohorts of gastric tumors.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-1476204/v1/4eb65158212ce258035f2ec0.png"},{"id":19736534,"identity":"b33cb28f-3cce-4d65-b8b9-451651b4fdf2","added_by":"auto","created_at":"2022-03-29 14:03:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1248672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1476204/v1/78947ea4-2459-492d-90e5-5a5a40fc4dbe.pdf"},{"id":19736001,"identity":"900b7492-3a8a-4332-8027-828b75e9e0ae","added_by":"auto","created_at":"2022-03-29 13:58:27","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":105662,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e: Genes upregulated in Madurai cohort gastric tumor samples compared to adjacent gastric normal samples are shown along with fold change and \u003cem\u003eP\u003c/em\u003e-value.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e: Genes downregulated in Madurai cohort gastric tumor samples compared to adjacent gastric normal samples are shown along with fold change and \u003cem\u003eP\u003c/em\u003e-value.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e: The details of the expression profiles of gastric tumors used in the study along with the microarray platform used, characteristics of samples, number of samples and details of publication. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e: The lists of upregulated genes in Madurai cohort and the overlap across cohorts.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Table 5\u003c/strong\u003e: The lists of downregulated genes in Madurai cohort and the overlap across cohorts.\u003c/p\u003e\u003cp\u003e.\u003cstrong\u003eSupplementary Table 6\u003c/strong\u003e: The list of genes upregulated in gastric tumors which are also reported for their association with gastric cancer progression from previous studies are shown with citation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Table 7\u003c/strong\u003e: The list of gene signatures enriched among upregulated genes of gastric tumors in the analysis with MSigDB. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Table 8: \u003c/strong\u003eThe list of pathways enriched among upregulated genes of gastric tumors in the analysis with TAC. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Table 9: \u003c/strong\u003eThe list of genes downregulated in gastric tumors which are also reported for their dysregulation in gastric cancer from previous studies are shown with citation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Table 10: \u003c/strong\u003eThe list of gene signatures enriched among downregulated genes of gastric tumors in the analysis with MSigDB.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Table 11: \u003c/strong\u003eThe list of pathways enriched among downregulated genes of gastric tumors in the analysis with TAC.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Table 12\u003c/strong\u003e: The list of the derived gene sets for major oncogenic signaling pathways.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"GastrictumorMSsupplementarytable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1476204/v1/eeac13ade3a08cf02b7e2cea.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eWhole Transcriptome and Functional Genomic Landscape of A South Indian Cohort of Gastric Tumors\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer is one of the major malignancies with higher morbidity and mortality around the world, especially in Asian countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the global scenario of gastric cancer, India falls under the low incidence category. However, in India it is the fifth most common cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and the second most common cause of cancer related deaths [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Within India, there is a wide regional variation in the occurrence of gastric cancer. The data from National Cancer Registries programme (NCR), India reveals north east state of India, Mizoram to have the highest incidence followed by Tamil Nadu (for men) and Bangalore (for women). However, diagnosis of early gastric cancer continues to be a problem since routine screening is not feasible considering the population [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The factors contribute to the progression of gastric cancer include lifestyle, environmental factors, \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection, genetic and epigenetic alterations. \u003cem\u003eHelicobacter pylori\u003c/em\u003e infections are estimated to occur in more than 60% of Indians leading to peptic ulcer, particularly duodenal ulcer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consumption of salted, smoked or poorly preserved foods, low consumption of fruits and vegetables are also being major risk factors. Other disease factors associated with an increased risk of gastric cancer include chronic atrophic gastritis, hypertrophic gastropathy and gastric polyps [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the underlying molecular biological mechanisms are still elusive. As the pathogenesis of gastric cancer involves the dysregulated molecular signaling pathways and molecular cellular processes, it is necessary to understand the dysregulation by unbiased global methods [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Considering the disparities in the genetic determinants in various diseases, it is necessary to characterize the genomic and transcriptomic landscape across different geographical regions and populations.\u003c/p\u003e \u003cp\u003eIn this study, we performed whole genome mRNA profiling of gastric adenocarcinoma tumors along with adjacent normal tissues from Madurai, India using Affymetrix HTA2.0 arrays. The genes differentially expressed in gastric tumors compared to the gastric normal tissues were identified and the transcription factors, signaling pathways and molecular-cellular-physiological processes were identified by integrative functional genomic investigation. The differentially expressed genes were compared with genes from other cohorts. The molecular stratification of subtypes of gastric cancer defined in ACRG and TCGA studies were also analyzed across Madurai cohort gastric tumors. The identified dysregulations and the stratification avenues would pave way for the development of potential diagnostic and therapeutic leads to enable the eventual development of genomic medicine for gastric cancer.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eClinical samples and RNA extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurgically resected gastric tumor samples were collected from 50 gastric cancer patients those underwent gastrectomy at Madurai Meenakshi Mission Hospital \u0026amp; Research Centre (MMHRC), Madurai. The study was carried out with the approval of institutional ethics committee of MMHRC. Based on the histopathological observation, tumor tissue with \u0026ge;70% tumor cells were collected and the adjacent normal tissues were harvested at least a few centimetres away from the core tumor region of the surgically resected biopsies [9, 10]. The samples were collected in RNA\u003cem\u003elater\u003c/em\u003e\u003csup\u003e\u0026reg;\u003c/sup\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Ambion Inc., Austin, TX) and stored at -80\u0026deg;C until the extraction of RNA. Total RNA was extracted from 50 mg of collected cancerous and adjacent noncancerous gastric tissues using the RNAeasy\u003csup\u003e\u0026reg;\u003c/sup\u003e Kit (Qiagen). \u0026nbsp;The tissue was minced in the RLT lysis buffer (Qiagen) using the TissueRuptor\u003csup\u003e\u0026reg;\u003c/sup\u003e (Qiagen) and total RNA was isolated as per the protocol of the manufacturer. The concentration and quality of RNA was analyzed by Nanodrop 2000 spectrophotometer\u003csup\u003e\u0026reg;\u003c/sup\u003e (Thermo Scientific, USA). The quality of RNA was assessed using the Bioanalyzer 2100\u003csup\u003e\u0026reg;\u003c/sup\u003e (Agilent, Palo Alto, CA) and RIN value (RNA Integrity Number) was recorded to understand the intactness of 18S and 28S rRNA. The samples with RIN value \u0026ge; 5 and RNA concentration \u0026ge; 150ng/\u0026micro;l were taken into consideration and processed further for expression profiling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhole-genome mRNA profiling of gastric tumor and adjacent gastric normal tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA (500 ng) from 50 gastric tumor and 15 adjacent gastric normal samples with the criteria of RIN \u0026ge; 5 were subjected to\u0026nbsp;mRNA profiling using GeneChipTM Human Transcriptome Array (HTA) 2.0 microarray chips (Affymetrix, Santa Clara, CA). RNA processing, hybridization, washing and staining were performed in GeneChip Fluidics Station 450 (Affymetrix), as per the manufacturer\u0026rsquo;s protocol. The chips were scanned using GeneChip Scanner 3000 7G (Affymetrix).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicroarray data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scanned CEL files were Gene Level-SST-RMA (Robust Multi-array Average) normalized in the Expression Console (EC) Software of Affymetrix. The fold change and \u003cem\u003eP\u003c/em\u003e-value of the genes were calculated by ANOVA using the Transcriptome Analysis Console version3.0 (TAC) software (Affymetrix). The raw data and the processed data of whole transcriptome profile of 50 gastric tumor and 15 adjacent gastric normal tissues of Madurai, India cohort has been submitted to the Gene Expression Omnibus (GEO) repository database with the accession ID\u0026nbsp;GSE146996. The\u0026nbsp;genes\u0026nbsp;differentially expressed in gastric tumors were filtered with the fold-change \u0026ge; 2.0 for upregulation and \u0026le; 2.0 for downregulation. The genes were further filtered to be statistically significant at \u003cem\u003eP \u0026le;\u0026nbsp;\u003c/em\u003e0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway and gene set enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genes differentially expressed in gastric tumors were analyzed by gene set\u0026nbsp;enrichment analysis\u0026nbsp;using the available hallmark gene sets\u0026nbsp;of molecular signatures database (MSigDB)\u0026nbsp;with the default parameters [11, 12]. Enriched gene sets with significant \u003cem\u003eP\u003c/em\u003e-value were selected for further consideration. Signaling pathway based enrichment analysis was performed using Transcriptome Analysis Console V4.0 (TAC) for the genes differentially expressed in gastric tumors. KEGG orthology based pathway enrichment analysis was performed using the tool GeneCodis [13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo derive the gene sets for the selected oncogenic signaling pathways, the available corresponding gene signatures were collected from MSigDB. The available gene sets for ERK, OCT4, E2F, ECM, ESR, TGF-\u0026beta;, HNF4 and PPAR\u0026gamma; were collected from MSigDB. Independently, for each of the gene sets, the gene set/ pathway based activation scores were calculated as described earlier\u0026nbsp;using the Kolmogorov\u0026ndash;Smirnov metrics [14]. Further, the correlation\u0026nbsp;between the gene set activation score and all the genes in the expression profiles of gastric cancer datasets (GSE15459, GSE62254, GSE35809, GSE22377, and GSE15456) was performed. \u0026nbsp;\u003cem\u003eWith the specific cut off correlation value (\u0026ge;0.4) and also based on their presence across multiple datasets, the genes were filtered and shortlisted. These candidate genes were designated as the pathway specific gene sets more relevant to gastric tumors. The gene signatures used for different molecular subtypes of gastric tumors in ACRG [15] and TCGA [16] studies were used to explore the corresponding molecular subtypes.\u0026nbsp;\u003c/em\u003eThe expression pattern of the genes and the gene sets were analyzed and represented as heatmap using dChip software [17].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGenome-wide mRNA profiling of gastric tumors from Madurai, an Indian cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenome-wide expression profiles of several cohorts of gastric tumors from different countries have been established. Towards establishing a whole transcriptome profile of a South Indian cohort, whole-genome expression analysis was performed from a cohort comprising 50 gastric tumor and 15 adjacent gastric normal tissues. The samples were collected from gastric cancer patients those underwent gastrectomy at Meenakshi Mission Hospital \u0026amp; Research Centre (MMHRC), Madurai, Tamil Nadu, India. The study was conducted with the approval of institutional ethics committee of MMHRC, Madurai. \u0026nbsp;Based on the histopathological investigation of the tumor samples, the portion of tumor samples with \u0026ge;70% of tumor content were selected for RNA extraction. The RNA samples with the criteria of RNA integrity number \u0026ge;5 were analyzed by genome-wide mRNA profiling using HTA2.0 arrays (Affymetrix) (Fig. 1A). The microarray data files were subjected to principal component analysis (PCA) to understand the overall distribution and variance of the gene expression among the adjacent normal and gastric tumor samples. The PCA plot revealed the gastric normal and tumor samples to be grouped independently without any outliers (Fig. 1B). Further the genes\u0026nbsp;differentially expressed in gastric tumors compared to adjacent gastric normal samples were investigated. With the fold change cut-off of \u0026ge; 2 and \u0026le; -2, 1005 genes were found upregulated and 388 genes got downregulated in gastric tumors with the significant\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value of \u0026le;0.05\u0026nbsp;(Supplementary\u0026nbsp;Table 1 \u0026amp; 2). Further, the unsupervised hierarchical clustering of adjacent normal and gastric tumor samples, based on the expression pattern of differentially expressed genes, revealed the normal and gastric tumor samples to get clustered separately (Fig. 2A). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of the genes dysregulated in Madurai cohort with other established gastric tumor profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the comparability of the genes dysregulated in Madurai cohort of gastric tumors with the other established cohorts of gastric tumors, we compared the genes differentially expressed across multiple cohorts of gastric tumors. The\u0026nbsp;differentially expressed genes from 23 cohorts of gastric tumors from China, Japan, South Korea, USA, Germany, Spain, India and Argentina \u0026nbsp;were collected (Supplementary\u0026nbsp;Table 3). The comparative analysis of the differentially expressed genes affirms that\u0026nbsp;differentially expressed genes of gastric tumors from Madurai cohort are largely overlapping with the\u0026nbsp;differentially expressed genes of the\u0026nbsp;cohorts of China, India, South Korea and Argentina (Fig. 2B). Of the analyzed 23 cohorts and among the 1005 upregulated genes from Madurai cohort, i) 29 genes were found to show elevated expression in at least 10 other cohorts, ii) 274 genes in 4 cohorts across the globe, iii) 566 genes at least 2 other cohorts, and iv) to the maximum, the gene COL1A1 shows occurrence across 15 cohorts of gastric tumors\u0026nbsp;(Supplementary\u0026nbsp;Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the genes downregulated in gastric tumors from Madurai cohort, across 23 investigated cohorts, i) 289 genes were found downregulated in 2 other cohorts, ii) 201 genes were found downregulated in 4 other cohorts, iii) 32 genes in 10 cohorts, and iv) in the higher side, the gene ATP4B, GIF and PGC were found downregulated in 17 and 16 other cohorts\u0026nbsp;(Supplementary\u0026nbsp;Table 5 \u0026amp;\u0026nbsp;Fig. 2B). This shows the comparability of the genes differentially expressed in Madurai cohort with other cohorts across countries. While it is worth and tempting to investigate the differing\u0026nbsp;pattern of pathogenesis or disparity across populations, the current analysis shows that in large, the difference is due to sample size, differing platforms, and also possibly the differing heterogeneities\u0026nbsp;in the subtypes, which all deserve an extensive investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDysregulated expression of major cancer genes in Madurai cohort of gastric tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the genes upregulated in the gastric tumors of Madurai cohort, we could note several well reported oncogenes known to involve in carcinogenesis. \u003cem\u003eSPP1\u003c/em\u003e (Osteopontin) a secreted N-linked glycoprotein gene is known for the elevated expression in gastric cancer [18], to promote metastasis [19] and serve as a prognostic factor [20]. In the current cohort, SPP1 was found to be extremely upregulated with 80 folds of higher expression in gastric tumors compared to non-tumor gastric tissues. Several Extra Cellular Matrix (ECM) genes including BGN (biglycan), a small leucine-rich proteoglycan, known to enhance gastric cancer cell wound healing, migration, invasion, as well as the endothelial tube formation \u003cem\u003e[21],\u003c/em\u003e\u003cem\u003ewas found upregulated with 65 folds of higher expression in gastric tumors.\u0026nbsp;\u003c/em\u003eSeveral collagen genes such as \u003cem\u003eCOL1A1, COL1A2, COL5A2, COL12A1\u003c/em\u003e, and \u003cem\u003eCOL14A1 were found upregulated\u003c/em\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003cem\u003eThe collagen genes\u003c/em\u003eCOL1A2, COL3A1, COL6A3, COL1A1 and COL12A1 showed 20 \u0026ndash; 50 folds high expression in gastric tumors.\u0026nbsp;Other significantly upregulated markers include sulfatase 1 (SULF1), thrombospondin 2 (THBS2), carcinoembryonic antigen related cell adhesion molecule 6 (CEACAM6),\u0026nbsp;inhibin beta A (INHBA) and\u0026nbsp;claudin 7 (CLDN7) which got upregulated from 6 \u0026ndash; 67 folds.\u0026nbsp;The dysregulation of these genes in gastric tumors of Madurai cohort is in the line of the established whole-genome profiles of gastric tumors (Supplementary\u0026nbsp;Table 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis reveals a comprehensive array of molecular processes dysregulated in gastric tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther we set out to perform gene set enrichment analysis for the genes upregulated in gastric tumors in order to understand the dysregulated processes. The analysis in Molecular Signatures Database (MSigDB) revealed the enrichment of i) transcription regulators LEF1, SP1, NFAT, AP1, PAX4, E2F, ATF2, ii) oncogenic pathways EGFR, KRAS, VEGF, ECM and loss of p53 related molecular processes among the genes upregulated in gastric tumors. In addition, the oncogenic signatures such as epithelial to mesenchymal transition (EMT), invasion, advanced gastric cancer and cellular proliferation also were found to be enriched among the genes upregulated in gastric tumors (Fig. 3A and Supplementary Table 7). The similar analysis carried out in\u0026nbsp;Transcriptome Analysis Console V4.0 (TAC) tool further revealed the enrichment of the gene-sets corresponding to focal adhesion, EMT, cell cycle, TGF-\u0026beta;, RAS, MAPK, JAK/STAT, NOTCH, EGFR and nuclear receptors including vitamin-D receptor pathways to be significantly upregulated in gastric tumors (Fig. 3B and\u0026nbsp;Supplementary Table 8). KEGG pathway enrichment analysis using GeneCodis tool also revealed significant enrichment of above said pathways (Fig. 3C). In addition, phagosome and cytoskeletal genes were noted. Most of the above said pathways are known for their association in the progression of gastric cancer. For example, the aberrant activation of EMT and the resultant tumorigenic processes could be triggered by various transcription factors and signaling pathways including TGF-\u0026beta; and Notch in gastric cancers [22].\u0026nbsp;Ras/Raf/MAPK signaling pathway involved in the transmission of extracellular signals, proliferation and differentiation has been reported in gastric carcinogenesis with high rate of mutation [23]. Similarly, all the identified pathways and dysregulations have been reported to have an association with gastric and other cancers. However, the current observation reveals a comprehensive understanding of the processes highly activated in gastric tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLoss of stomach associated molecular physiology is the prime dysregulation inferred from the transcriptome of gastric tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our cohort, 388 genes got downregulated in gastric tumors compared to normal gastric tissues with more than 2 folds. \u0026nbsp; A\u0026nbsp;number of genes found to be downregulated in the current cohort have already been reported. GIF\u0026nbsp;encoding for gastric intrinsic factor is secreted by the parietal cells of the stomach and is necessary for the absorption of vitamin B12. GIF was found to be the promising predictive maker for gastric cancer [24]. In the current cohort, GIF was found to be extremely downregulated to 26939 folds. Gastrokine family genes GKN1 and GKN2, which are expressed in the normal gastric epithelium plays a significant role in maintaining the integrity and homeostasis of gastric mucosa. Inactivation and downregulation of both GKN1 and GKN2 have been reported to involve in the development and progression of gastric cancer [25, 26]. In Madurai cohort, both GKN1 and GKN2 got downregulated to 11413 and 4326 folds respectively.\u0026nbsp;Gastric lipase (LIPF) of the family of lipases involved in the digestion of triacylglycerides is secreted by gastric mucosal cells [27]. LIPF has been reported for downregulation in gastric cancer and also is a part of the 8 gene signature known to predict gastric cancer [28].\u0026nbsp;In Madurai cohort, LIPFis downregulated to 9137 folds. KCNE2(Potassium voltage-gated channel) gene was reported to involve in neurotransmitter release, neuronal excitability and electrolyte transport [29]. We found 159 fold downregulation of KCNE2in gastric tumors. Deletion of KCNE2has been reported to cause gastritis and is also a predisposing factor for gastric cancer [30]. Similarly, several genes with the physiological role in gastric cells and also previously reported to have tumor suppressor features have been found to be highly downregulated in gastric tumor samples. For example, ATPase genes (ATP4A, ATP4B) [31], pepsinogen genes (PGA3 \u0026ndash; 5, PGC)\u0026nbsp;[32], mucin genes (MUC1, MUC5AC and MUC6)\u0026nbsp;[33]\u0026nbsp;and trefoil factors (TFF1 \u0026amp; TFF2)\u0026nbsp;[34]\u0026nbsp;were found to be highly downregulated in the current cohort of gastric tumors.\u0026nbsp;A list of the previously reported genes to be downregulated in gastric tumors and also downregulated in Madurai cohort are provided in\u0026nbsp;Supplementary\u0026nbsp;Table 9.\u003c/p\u003e\n\u003cp\u003eGene set enrichment analysis performed with the\u0026nbsp;genes downregulated in gastric tumors revealed the enrichment of the transcription regulators such as Estrogen Receptor (ESR), Bone Morphogenic Protein (BMP2), Hepatocyte Nuclear Factor (HNF3), polycomb repressive complex factors PRC2/SUZ12/EED and TP53. Several metabolism related signatures including xenobiotic, fatty acid, retinol and bile acid metabolism were also found downregulated in gastric tumors (Fig. 4A and\u0026nbsp;Supplementary Table 10). The pathways\u0026nbsp;nuclear erythroid 2-related factor (NRF2), amino acid/tryptophan metabolism, glycosis and gluconeogenesis were also found enriched among the downregulated genes (Fig. 4B and\u0026nbsp;Supplementary Table 11). In an independent gene-set enrichment analysis performed with the\u0026nbsp;GeneCodis\u0026nbsp;tool, the downregulated genes were found enriched with the processes such as metabolism of xenobiotics by cytochrome P450, gastric acid secretion, drug metabolism by cytochrome P450, pancreatic secretion, retinol metabolism and glycosis/gluconeogenesis (Fig. 4C). Notably, loss of the physiology and functions of stomach such as digestion, gastric acid secretion and absorption seems downregulated in gastric tumors. Whole transcriptome analysis reveals the physiological changes in gastric tumors compared to non-cancerous gastric tissues.\u0026nbsp;This is a comprehensive genome-wide expression landscape covering whole transcriptome from Madurai, South India. This study also reveals the multiple markers, pathways and molecular processes which could be clinically useful for their diagnostic, prognostic and therapeutic features upon further evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePattern of the dysregulation of major oncogenic pathways in gastric tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene set based pathway activation analysis was performed for a few oncogenic pathways across the gastric tumor samples, to understand the pattern of their activation. The coexpressed genes were derived for a panel of oncogenic pathways (Supplementary Table 12). Upon analysis in Madurai cohort of gastric tumors,\u0026nbsp;TGF-\u0026beta;,\u0026nbsp;OCT4, ECM, E2F, ERK, HNF4, PPAR\u0026gamma; and YY1 were found to be extremely activated in gastric tumors compared to the adjacent normal gastric tissues (Fig. 5A, B). In contrary, the estrogen receptor (ESR) pathway was found activated in normal samples with a very clear downregulation in gastric tumors (Fig. 5B, C). These results corroborate with the observation from the functional enrichment analysis among the genes up and downregulated in gastric tumors. This analysis shows the promising candidacy of\u0026nbsp;TGF-\u0026beta;,\u0026nbsp;OCT4, ECM, E2F, ERK, YY1, HNF4, PPAR\u0026gamma; and ESR pathways for the differential targeting. These leads would pave a way for the development of appropriate diagnostics and targeted therapeutic strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular disparity between Madurai and TCGA cohorts of gastric tumors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Cancer Genome Atlas (TCGA) and\u0026nbsp;Asian Cancer Research Group (ACRG) studies have reported four major molecular subtypes of gastric tumors [35, 15]. The molecular subtypes defined by TCGA study include Epstein Barr Virus (EBV), microsatellite-instable (MSI), Genomically Stable (GS) and Chromosomal Instabiltiy (CIN). The ACRG study has defined the independent subtypes as microsatellite-instable (MSI), microsatellite-stable MSS/TP53\u0026minus;, MSS/TP53+, and epithelial-to-mesenchymal transition (EMT). The occurrence of these subtypes was investigated in Madurai cohort gastric tumors with the above listed subtype specific gene sets from TCGA and ACRG studies.\u0026nbsp;Gene set based \u003cem\u003eZ\u003c/em\u003e-score computing was performed to analyze the expression pattern of these gene sets across Madurai cohort gastric tumors as well as in the TCGA and ACRG samples. The number of samples activated under each subtype was calculated\u0026nbsp;and a threshold of two-fold cut off was considered [14, 36, 37]. \u0026nbsp;Analysis of the TCGA molecular subtypes in Madurai cohort revealed GS subtype to be predominantly activated in\u0026nbsp;~40% of samples. EBV and MSI subtypes were found activated in ~30% of samples. However in TCGA, cohort CIN subtype was observed to have high frequent activation of ~38% followed by GS subtype which occurs in ~25% of samples (Fig. 6A, B). From this analysis of the expression pattern of TCGA subtype specific gene sets across TCGA and Madurai cohort samples, we could observe differing prevalence of TCGA gastric cancer subtypes between different cohorts, which are from Europe a major fraction of non-Asian (TCGA) and South India (Madurai). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong ACRG subtypes,EMT subtype was found to be predominant one with ~50% of activation in Madurai and ~40% in ACRG cohort. MSI subtype occurs in ~28% of Madurai and ~22% of ACRG cohorts (Fig. 6C, D). Notably MSI and proliferation gene signatures were also found co-activated in a subset of gastric tumors in Madurai cohort as well as in ACRG samples. While the differing frequency of occurrence of subtypes is predominant between Madurai and TCGA cohort, this was not the case with ACRG cohort (Fig. 6E, F). This disparity warrants a detailed investigation. It is worth mentioning that the current analysis was merely based on the activation pattern of gene sets. While this approach has advantages, the subtyping in TCGA and ACRG studies were based on subtype-related molecular/clinical assays. However the current comparison was made in a uniform manner for all the datasets.\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eCarcinogenesis and progression are due to the cumulative impact of genomic and epigenetic aberrations [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Understanding the molecular dysregulations involved in gastric cancer would pave a way for the development of improved diagnostic and targeted therapeutic strategies. While a few molecular and genomic subtypes have been established in gastric cancer, it is being necessary to investigate in multiple cohorts and to identify the therapeutic targets for different subtypes [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Earlier, the intrinsic subtypes of gastric tumors were found to have association with therapeutic response with differential sensitivity to 5-fluorouracil and oxaliplatin [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Based on the gene expression profiles from Singapore and Australian cohorts, gastric tumors have been classified into proliferative, metabolic and mesenchymal subtypes. Proliferative subtype of gastric tumors was characterized with high genomic instability, elevated expression of cell cycle genes, activated RAS, E2F, MYC pathways, TP53 mutations, and the feature of intestinal subtype. Mesenchymal subtype exhibited the features of cancer stem cell, low CDH1, enriched focal adhesion and ECM receptor genes, activated TGF-β, NF-\u0026#120581;B, mTOR, VEGF, sonic hedgehog pathways, association with diffuse subtypes, low copy number aberrations and sensitivity to PI3K-AKT-mTOR inhibitors. Metabolic subtype showed the enrichment of metabolic pathways, digestion related genes and sensitivity to 5-Fluorouracil [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Cancer Genome Atlas (TCGA) cohort comprises gastric tumors from 25% Asian ethnic samples and 75% of samples from non-Asian populations. The study has proposed four molecular subtypes of gastric adenocarcinoma: i) Epstein-Barr virus positive tumors, ii) Microsatellite unstable, iii) Genomically stable, and iv) Tumors with chromosomal instability [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Another study from Asian Cancer Research Group (ACRG) has established four different molecular subtypes of gastric tumors: i) Mesenchymal, ii) Microsatellite instable, iii) TP53-active, and iv) TP53-inactive subtype [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Whole genome/exome sequencing of gastric tumor samples has identified i) subtype specific genetic perturbations with RHOA mutations in 14.3% of diffuse subtype tumors [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], ii) PIK3CA mutations with the frequency of 25% in tumors [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and iii) frequent alteration of many components of RAS/RAF/MAPK/ERK cascades with high frequent mutation in KRAS, MAPK3 and MAP2K4 genes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In the light of these studies and with the knowledge of functional genomic alterations, the potential therapeutic targets for gastric tumors have been explored.\u003c/p\u003e \u003cp\u003eA comprehensive whole genome expression landscape of Indian cohort is yet to be established and was addressed in the present study. A global view of the mRNA expression landscape of Madurai, India cohort comprising 50 gastric tumor and 15 adjacent gastric normal samples has been established. So far, there are three whole genome expression profiles established for gastric tumors from India. A profile from Chennai, India has explored 24 gastric tumors, 5 apparently normal and 5 paired gastric normal samples [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Another profile from New Delhi, India was with 2 gastric normal and 5 gastric tumor samples [GSE20143]. Another profile from Bangalore, India has 14 pairs of gastric normal and tumor samples [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The dysregulated genes from Madurai cohort show larger overlap with the genes from Bangalore cohort of gastric tumors. However such a comparison is cumbersome due to the different platforms, sample subtypes and other conditions. The differentially expressed genes in gastric tumor samples and the deregulated molecular signaling pathways were explored. The highly expressed genes in the current study such as SPP1, COL1A2, and THBS2 have been reported as predictors of gastric cancer prognosis [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Similarly, the other identified genes BGN, SULF1 and INHBA were all reported to be upregulated and associated with gastric cancer progression in other cohorts of gastric tumors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, and \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Similarly the downregulated genes of current cohort, ATP4A, ATP4B, GKN1, GIF, LIPF and PGA4 were also reported in multiple cohorts and the functional deficiency of these genes leads to the gastric cancer progression [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Further, we set out to analyze the overlap in the differentially expressed genes among various cohorts of gastric tumors across globe. A collection of 23 gene sets from different cohorts of gastric tumors across the globe were analyzed. The differentially expressed genes found to overlap between our cohort and other cohorts across the globe were identified. The differentially expressed genes from Madurai cohort were found to overlap mostly with China, South Korea, Germany, Argentina, USA, Japan, and India. It is worth mentioning that the different gastric tumor cohorts were found to show 0.2% \u0026minus;\u0026thinsp;24% of gene overlap among them in general. Of these, Madurai cohort exhibits 21% overlap with the differentially expressed genes from other cohorts. However, such comparisons are difficult due to differing study designs, platforms, normalization methods, gene selection criteria, etc., [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe ECM-receptor interaction pathway has been identified in multiple cancers and its essential role in cancer progression is well known [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The inflammatory response associated signaling such as the chemokine signaling, Jak/Stat and NF-\u0026#120581;B pathways have been reported in gastric cancer. The upregulated genes involved in the chemokine signaling such as CXCL5 and CXCL8 are known to have association with chemo-attracting pro-tumor neutrophils, which increase the risk of chronic inflammation leading to cancer, and metastasis of gastric cancer cells [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The transcription factors and the signaling pathways regulated by SP1, LEF, EGFR, RAS/MAPK, TGF-β and VEGF were found enriched among the genes upregulated in the gastric tumors in the current cohort. SP1 also has been identified for the association with the depth of invasion and TNM stage of gastric cancer. SP1 has been indicated as a potential marker for the prognosis of gastric cancer patients [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Among the genes downregulated in gastric tumors, we could observe the enrichment of the binding sites for a NFAT, HNF3, and TP53.\u003c/p\u003e \u003cp\u003eThe downregulated genes were found to involve in the digestion process and the metabolisms related to amino acid, fatty acid, bile acid, xenobiotics by cytochrome p450, glycolysis and gluconeogenesis. The other enriched pathways include gastric acid secretion, pancreatic secretion, protein digestion, absorption and retinol metabolism. Gastric acid secretion has been reported to lead to various diseases in the stomach, such as gastroesophageal reflux disease, chronic atrophic gastritis and gastric cancer [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. It has been reported that the majority of patients with advanced gastric cancer to experience nutritional deficiency, which, in combination with surgical trauma, also to cause post-operative immune dysfunction and malnutrition with the impact on recovery [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This makes sense with the current observation of the diminished expression of the genes involved in absorption and digestion.\u003c/p\u003e \u003cp\u003eTranscription programs and signaling pathways are the most prominent therapeutic targets [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In the current study, the gene sets representing different oncogenic signaling pathways were derived by integrative genomic approach and their activation patterns were analyzed. TGF-β, OCT4, ECM, E2F, ERK and YY1 gene sets showed extreme activation in gastric tumors than normal samples with the contrasting pattern for ESR gene set. The predominant occurrence of gastric cancer in men compared to women is known [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. ER-β has been reported to suppress the progression of gastric cancer [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Patients with higher expression of ER-β were reported to show better survival with the clinical features of lower tumor differentiation, Lauren's intestinal type, and negative association with lymph node metastasis [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. This makes sense with the current observation of the suppression of ER signaling in gastric tumors. The anti-estrogen drug tamoxifen has also been identified to increase the risk of gastric adenocarcinoma [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The identified transcription factors and pathways might work as potential targets for the development of targeted therapeutics for gastric cancer.\u003c/p\u003e \u003cp\u003eDue to the lack of comparable subtype specific clinical results, the occurrence of subtypes across cohorts were compared based on the activation pattern of subtypes specific gene sets. The current analysis also shows molecular disparity between cohorts specifically the differing prevalence of GS subtype between TCGA and Madurai cohort. This warrants a detailed investigation to tailor the therapeutic regimens in the context of disparity [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The current cohort adds to the array of gastric tumor expression profiles and provides a comprehensive overview of all the dysregulated transcription factors and signaling pathways. The current identification of transcription factors, signaling pathways and physiological processes would pave a way for the development of new therapies for gastric cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe grant support from\u0026nbsp;Department of Biotechnology (DBT), Government of India, through the Unit of Excellence (UOE) in Cancer Genetics research grant BT/MED/30/SP11290/2015 is acknowledged.\u0026nbsp;The support\u0026nbsp;of\u0026nbsp;Histopathology Division,\u0026nbsp;Dept. of Laboratory Services and\u0026nbsp;Dept. of\u0026nbsp;Surgery \u0026amp; Surgical Gastroenterology,\u0026nbsp;Meenakshi Mission Hospital \u0026amp; Research Centre, Madurai, India is acknowledged.\u0026nbsp;We acknowledge the core facility support from UGC-CEGS, MKU-RUSA and DST-FIST programs of School of Biological Sciences, Madurai Kamaraj University, Madurai.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Department of Biotechnology (DBT), Government of India, through the Unit of Excellence (UOE) in Cancer Genetics research grant BT/MED/30/SP11290/2015 to Dr. Kumaresan Ganesan, Madurai Kamaraj University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKumaresan Ganesan and Jaishree Pandian conceived the study and designed the experiments.\u0026nbsp;Ramesh Ardhanari, Vikash Vittal, Satyajit Patwardhan and\u0026nbsp;Mohan Narasimhan were involved\u0026nbsp;in collecting the samples. Madhusudhanan Gnanasekaran and Indu Kannan performed the histopathological investigations. Vikash Vittal collected and comprehended the clinical information. \u0026nbsp;Jaishree Pandian, Balaji T Sekar, Helen D Jemimah,\u0026nbsp;Ponmathi Paneerpandian, Prem Suresh, and Karthikeyan Selvarasu performed the experiments.\u0026nbsp;Jaishree Pandian and Kumaresan Ganesan analyzed the data and wrote the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors disclose no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study will be available in Gene Expression Omnibus (GEO) repository database with the accession ID\u0026nbsp;GSE146996.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61:69\u0026ndash;90.\u003c/li\u003e\n \u003cli\u003eChen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66:115\u0026ndash;32.\u003c/li\u003e\n \u003cli\u003eDikshit R, Gupta PC, Ramasundarahettige C, Gajalakshmi V, Aleksandrowicz L, Badwe R, et al. Cancer mortality in India: a nationally representative survey. Lancet. 2012;379:1807\u0026ndash;16.\u003c/li\u003e\n \u003cli\u003eRao DN, Ganesh B. Estimate of cancer incidence in India in 1991. Indian J Cancer. 1998;35:10\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eServarayan Murugesan C, Manickavasagam K, Chandramohan A, Jebaraj A, Jameel ARA, Jain MS, et al. Gastric cancer in India: epidemiology and standard of treatment. Updates Surg. 2018;70:233\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eThirumurthi S, Graham DY. Helicobacter pylori infection in India from a western perspective. Indian J Med Sci. 2010;64:423\u0026ndash;40.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFuchs CS, Mayer RJ. Gastric carcinoma. N Engl J Med. 1995;333:32\u0026ndash;41.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRiquelme I, Saavedra K, Espinoza JA, Weber H, Garc\u0026iacute;a P, Nervi B, et al. Molecular classification of gastric cancer: Towards a pathway-driven targeted therapy. Oncotarget. 2015;6:24750\u0026ndash;79.\u003c/li\u003e\n \u003cli\u003eHu YC, Lam KY, Law S, Wong J, Srivastava G. Identification of differentially expressed genes in esophageal squamous cell carcinoma (ESCC) by cDNA expression array: overexpression of Fra-1, Neogenin, Id-1, and CDC25B genes in ESCC. Clin Cancer Res. 2001;7:2213\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eChen K-N, Gu Z-D, Ke Y, Li J-Y, Shi X-T, Xu G-W. Expression of 11 HOX genes is deregulated in esophageal squamous cell carcinoma. Clin Cancer Res. 2005;11:1044\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene-set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545\u0026ndash;50.\u003c/li\u003e\n \u003cli\u003eLiberzon A, Birger C, Thorvaldsd\u0026oacute;ttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene-set collection. Cell Syst. 2015;1:417\u0026ndash;25.\u003c/li\u003e\n \u003cli\u003eCarmona-Saez P, Chagoyen M, Tirado F, Carazo JM, Pascual-Montano A. GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome Biol. 2007;8:R3.\u003c/li\u003e\n \u003cli\u003eLevine DM, Haynor DR, Castle JC, Stepaniants SB, Pellegrini M, Mao M, Johnson JM. Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways. Genome Biol. 2006;7(10):R93. doi: 10.1186/gb-2006-7-10-r93. Epub 2006 Oct 17. PMID: 17044931; PMCID: PMC1794557\u003c/li\u003e\n \u003cli\u003eCristescu R, Lee J, Nebozhyn M, Kim K-M, Ting JC, Wong SS, et al. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med. 2015;21:449\u0026ndash;56.\u003c/li\u003e\n \u003cli\u003eSohn BH, Hwang JE, Jang HJ, Lee HS, Oh SC, Shim JJ, Lee KW, Kim EH, Yim SY, Lee SH, Cheong JH, Jeong W, Cho JY, Kim J, Chae J, Lee J, Kang WK, Kim S, Noh SH, Ajani JA, Lee JS. Clinical Significance of Four Molecular Subtypes of Gastric Cancer Identified by The Cancer Genome Atlas Project. Clin Cancer Res. 2017 Jul 26:10.1158/1078-0432.CCR-16-2211. doi: 10.1158/1078-0432.CCR-16-2211. Epub ahead of print. PMID: 28747339; PMCID: PMC5785562.\u003c/li\u003e\n \u003cli\u003eLi C, Wong WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA. 2001;98:31\u0026ndash;6.\u003c/li\u003e\n \u003cli\u003eImano M, Satou T, Itoh T, Sakai K, Ishimaru E, Yasuda A, et al. Immunohistochemical expression of osteopontin in gastric cancer. J Gastrointest Surg. 2009;13:1577\u0026ndash;82.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSong G, Ouyang G, Mao Y, Ming Y, Bao S, Hu T. Osteopontin promotes gastric cancer metastasis by augmenting \u0026nbsp;cell survival and invasion through Akt-mediated HIF-1alpha up-regulation and MMP9 activation. J Cell Mol Med. 2009;13:1706\u0026ndash;18.\u003c/li\u003e\n \u003cli\u003eDai N, Bao Q, Lu A, Li J. Protein expression of osteopontin in tumor tissues is an independent prognostic indicator in gastric cancer. Oncology. 2007;72:89\u0026ndash;96.\u003c/li\u003e\n \u003cli\u003eHu L, Duan Y-T, Li J-F, Su L-P, Yan M, Zhu Z-G, et al. Biglycan enhances gastric cancer invasion by activating FAK signaling pathway. Oncotarget. 2014;5:1885\u0026ndash;96.\u003c/li\u003e\n \u003cli\u003eHuang L, Wu R-L, Xu A-M. Epithelial-mesenchymal transition in gastric cancer. Am J Transl Res. 2015;7:2141\u0026ndash;58.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShen, B.F., Li, M., Wang, H., Xin, L., \u0026amp; Xie, J. Expression and clinical significance of the RAS / RAF / MAPK cell signaling pathway in gastric cancer.\u0026nbsp;Int J Clin Exp Med 2018;11:11682-11689\u003c/li\u003e\n \u003cli\u003eCui J, Chen Y, Chou W-C, Sun L, Chen L, Suo J, et al. An integrated transcriptomic and computational analysis for biomarker identification in gastric cancer. Nucleic Acids Res. 2011;39:1197\u0026ndash;207.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYoon JH, Song JH, Zhang C, Jin M, Kang YH, Nam SW, et al. Inactivation of the Gastrokine 1 gene in gastric adenomas and carcinomas. J Pathol. 2011;223:618\u0026ndash;25.\u003c/li\u003e\n \u003cli\u003eDai J, Zhang N, Wang J, Chen M, Chen J. Gastrokine-2 is downregulated in gastric cancer and its restoration suppresses gastric tumorigenesis and cancer metastasis. Tumour Biol. 2014;35:4199\u0026ndash;207.\u003c/li\u003e\n \u003cli\u003eAbrams CK, Hamosh M, Lee TC, Ansher AF, Collen MJ, Lewis JH, et al. Gastric lipase: localization in the human stomach. Gastroenterology. 1988;95:1460\u0026ndash;4.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang X, Yang J-J, Kim YS, Kim K-Y, Ahn WS, Yang S. An 8-gene signature, including methylated and down-regulated glutathione peroxidase 3, of gastric cancer. Int J Oncol. 2010;36:405\u0026ndash;14.\u003c/li\u003e\n \u003cli\u003eMiller C. An overview of the potassium channel family. Genome Biol. 2000;1:REVIEWS0004.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eRoepke TK, Purtell K, King EC, La Perle KMD, Lerner DJ, Abbott GW. Targeted deletion of Kcne2 causes gastritis cystica profunda and gastric neoplasia. PLoS ONE. 2010;5:e11451.\u003c/li\u003e\n \u003cli\u003eRaja UM, Gopal G, Rajkumar T. Intragenic DNA methylation concomitant with repression of ATP4B and ATP4A gene expression in gastric cancer is a potential serum biomarker. Asian Pac J Cancer Prev. 2012;13:5563\u0026ndash;8.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShen S, Jiang J, Yuan Y. Pepsinogen C expression, regulation and its relationship with cancer. Cancer Cell Int. 2017;17:57.\u003c/li\u003e\n \u003cli\u003eBoltin D, Niv Y. Mucins in Gastric Cancer - An Update. J Gastrointest Dig Syst. 2013;3:15519.\u003c/li\u003e\n \u003cli\u003eKatoh M. Trefoil factors and human gastric cancer (review). Int J Mol Med. 2003;12:3\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eCancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513:202\u0026ndash;9.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSmith AM, Walsh JR, Long J, Davis CB, Henstock P, Hodge MR, Maciejewski M, Mu XJ, Ra S, Zhao S, Ziemek D, Fisher CK. Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data. BMC Bioinformatics. 2020 Mar 20;21(1):119. doi: 10.1186/s12859-020-3427-8. PMID: 32197580; PMCID: PMC7085143.\u003c/li\u003e\n \u003cli\u003eCheadle C, Vawter MP, Freed WJ, Becker KG. Analysis of microarray data using Z score transformation. J Mol Diagn. 2003 May;5(2):73-81. doi: 10.1016/S1525-1578(10)60455-2. PMID: 12707371; PMCID: PMC1907322.\u003c/li\u003e\n \u003cli\u003eCorrea P. A human model of gastric carcinogenesis. Cancer Res. 1988;48:3554\u0026ndash;60.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePfeifer SP. From next-generation resequencing reads to a high-quality variant data set. Heredity (Edinb). 2017;118:111\u0026ndash;24.\u003c/li\u003e\n \u003cli\u003eHolbrook JD, Parker JS, Gallagher KT, Halsey WS, Hughes AM, Weigman VJ, et al. Deep sequencing of gastric carcinoma reveals somatic mutations relevant to personalized medicine. J Transl Med. 2011;9:119.\u003c/li\u003e\n \u003cli\u003eLim B, Kim J-H, Kim M, Kim S-Y. Genomic and epigenomic heterogeneity in molecular subtypes of gastric cancer. World J Gastroenterol. 2016;22:1190\u0026ndash;201.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTan IB, Ivanova T, Lim KH, Ong CW, Deng N, Lee J, et al. Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy. Gastroenterology. 2011;141:476\u0026ndash;85, 485.e1-11.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLei Z, Tan IB, Das K, Deng N, Zouridis H, Pattison S, et al. Identification of molecular subtypes of gastric cancer with different responses to PI3-kinase inhibitors and 5-fluorouracil. Gastroenterology. 2013;145:554\u0026ndash;65.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang K, Yuen ST, Xu J, Lee SP, Yan HHN, Shi ST, et al. Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer. Nat Genet. 2014;46:573\u0026ndash;82.\u003c/li\u003e\n \u003cli\u003eKarakas B, Bachman KE, Park BH. Mutation of the PIK3CA oncogene in human cancers. Br J Cancer. 2006;94:455\u0026ndash;9.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZang, Z.J., C.K. Ong, I. Cutcutache,\u0026nbsp;\u003cem\u003eet al.\u0026nbsp;\u003c/em\u003e2011. Genetic and structural variation in the gastric cancer kinome revealed\u0026nbsp;through targeted deep sequencing.\u0026nbsp;\u003cem\u003eCancer Res.\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e71:\u0026nbsp;\u003c/strong\u003e29\u0026ndash;39.\u003c/li\u003e\n \u003cli\u003eRajkumar T, Vijayalakshmi N, Gopal G, Sabitha K, Shirley S, Raja UM, et al. Identification and validation of genes involved in gastric tumorigenesis. Cancer Cell Int. 2010;10:45.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMarimuthu A, Jacob HKC, Jakharia A, Subbannayya Y, Keerthikumar S, Kashyap MK, et al. Gene Expression Profiling of Gastric Cancer. J Proteomics Bioinform. 2011;4:74\u0026ndash;82.\u003c/li\u003e\n \u003cli\u003eZhuo C, Li X, Zhuang H, Tian S, Cui H, Jiang R, et al. Elevated THBS2, COL1A2, and SPP1 Expression Levels as Predictors of Gastric Cancer Prognosis. Cell Physiol Biochem. 2016;40:1316\u0026ndash;24.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJunnila S, Kokkola A, Mizuguchi T, Hirata K, Karjalainen-Lindsberg M-L, Puolakkainen P, et al. Gene expression analysis identifies over-expression of CXCL1, SPARC, SPP1, and SULF1 in gastric cancer. Genes Chromosomes Cancer. 2010;49:28\u0026ndash;39.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Katayama Y, Oshima T, Sakamaki K, Aoyama T, Sato T, Masudo K, et al. Clinical Significance of INHBA Gene Expression in Patients with Gastric Cancer who Receive Curative Resection Followed by Adjuvant S-1 Chemotherapy. In Vivo. 2017;31:565\u0026ndash;71.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFei H-J, Chen S-C, Zhang J-Y, Li S-Y, Zhang L-L, Chen Y-Y, et al. Identification of significant biomarkers and pathways associated with gastric carcinogenesis by whole genome-wide expression profiling analysis. Int J Oncol. 2018;52:955\u0026ndash;66.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWalsh CJ, Hu P, Batt J, Santos CCD. Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery. Microarrays (Basel). 2015;4:389\u0026ndash;406.\u003c/li\u003e\n \u003cli\u003eKrupp M, Maass T, Marquardt JU, Staib F, Bauer T, K\u0026ouml;nig R, et al. The functional cancer map: a systems-level synopsis of genetic deregulation in cancer. BMC Med Genomics. 2011;4:53.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMao Y, Zhao Q, Yin S, Ding X, Wang H. Genome-wide expression profiling and bioinformatics analysis of deregulated genes in human gastric cancer tissue after gastroscopy. Asia Pac J Clin Oncol. 2018;14:e29\u0026ndash;36.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShi S, Zhang Z-G. Role of Sp1 expression in gastric cancer: A meta-analysis and bioinformatics analysis. Oncol Lett. 2019;18:4126\u0026ndash;35.\u003c/li\u003e\n \u003cli\u003eDi Mario F, Goni E. Gastric acid secretion: changes during a century. Best Pract Res Clin Gastroenterol. 2014;28:953\u0026ndash;65.\u003c/li\u003e\n \u003cli\u003eMartinsen TC, Bergh K, Waldum HL. Gastric juice: a barrier against infectious diseases. Basic Clin Pharmacol Toxicol. 2005;96:94\u0026ndash;102.\u003c/li\u003e\n \u003cli\u003eIshizuka M, Oyama Y, Abe A, Tago K, Tanaka G, Kubota K. Prognostic nutritional index is associated with survival after total gastrectomy for patients with gastric cancer. Anticancer Res. 2014;34:4223\u0026ndash;9.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTegels JJW, De Maat MFG, Hulsew\u0026eacute; KWE, Hoofwijk AGM, Stoot JHMB. Improving the outcomes in gastric cancer surgery. World J Gastroenterol. 2014;20:13692\u0026ndash;704.\u003c/li\u003e\n \u003cli\u003eSun K, Chen S, Xu J, Li G, He Y. The prognostic significance of the prognostic nutritional index in cancer: a systematic review and meta-analysis. J Cancer Res Clin Oncol. 2014;140:1537\u0026ndash;49.\u003c/li\u003e\n \u003cli\u003eGolub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eRyu W-S, Kim J-H, Jang Y-J, Park S-S, Um J-W, Park S-H, et al. Expression of estrogen receptors in gastric cancer and their clinical significance. J Surg Oncol. 2012;106:456\u0026ndash;61.\u003c/li\u003e\n \u003cli\u003eKim HW, Kim J-H, Lim BJ, Kim H, Kim H, Park JJ, et al. Sex Disparity in Gastric Cancer: Female Sex is a Poor Prognostic Factor for Advanced Gastric Cancer. Ann Surg Oncol. 2016;23:4344\u0026ndash;51.\u003c/li\u003e\n \u003cli\u003eZhang Y, Wu Y, Zhou X, Yi B, Wang L. Estrogen Receptor Beta Inhibits The Proliferation, Migration, And Angiogenesis Of Gastric Cancer Cells Through Inhibiting Nuclear Factor-Kappa B Signaling. Onco Targets Ther. 2019;12:9153\u0026ndash;64.\u003c/li\u003e\n \u003cli\u003eXu CY, Guo JL, Jiang ZN, Xie SD, Shen JG, Shen JY, et al. Prognostic role of estrogen receptor alpha and estrogen receptor beta in gastric cancer. Ann Surg Oncol. 2010;17:2503\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eZhang D, Ku J, Yi Y, Zhang J, Liu R, Tang N. The prognostic values of estrogen receptor alpha and beta in patients with gastroesophageal cancer: A meta-analysis. Medicine (Baltimore). 2019;98:e17954.\u003c/li\u003e\n \u003cli\u003eGe H, Yan Y, Tian F, Wu D, Huang Y. Prognostic value of estrogen receptor \u0026alpha; and estrogen receptor \u0026beta; in gastric cancer based on a meta-analysis and The Cancer Genome Atlas (TCGA) datasets. Int J Surg. 2018;53:24\u0026ndash;31.\u003c/li\u003e\n \u003cli\u003eNie X, Xie R, Tuo B. Effects of Estrogen on the Gastrointestinal Tract. Dig Dis Sci. 2018;63:583\u0026ndash;96.\u003c/li\u003e\n \u003cli\u003eLi H, Wang C, Wei Z, et al. Differences in the prognosis of gastric cancer patients of different sexes and races and the molecular mechanisms involved. \u003cem\u003eInt J Oncol\u003c/em\u003e. 2019;55:1049-1068.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gastric cancer, genome-wide expression profiling, differentially expressed genes, functional genomics, gastric acid secretion, digestion","lastPublishedDoi":"10.21203/rs.3.rs-1476204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1476204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGastric cancer is the third most common cause of cancer-related mortality. Genome-wide studies pave way for the delineation of heterogeneities and the possible genomics-guided stratification of tumors. Numerous whole-genome profiles have been established from different parts of globe and are largely lacking from South India. We performed genome-wide expression profiling of 50 gastric tumors and 15 adjacent normal tissues from Madurai, India. The genes differentially expressed in gastric tumors were found comparable with genes from other cohorts across globe. Integrative genomic analysis revealed the transcription factors LEF1, SP1, NFAT, AP1, PAX4, E2F and ATF2 to be upregulated and p53, HNF3, ESR, BMP2, PRC2/SUZ12/EED and NRF2 to be downregulated in gastric tumors. Pathway enrichment analysis showed the higher enrichment of focal adhesion, TGF-β, VEGF, EGFR, MAPK and EMT among the genes upregulated. The gastric acid secretion, digestion and absorption of minerals were identified to be diminished in gastric tumors. In a gene set based comparison of molecular subtypes, the frequency was found to differ between TCGA and Madurai cohorts indicating the disparity in molecular genomic patterns across populations. Thus the identification of the transcription factors, signaling pathways and molecular processes dysregulated in gastric tumors is resourceful and reveals the potential therapeutic targets for the eventual development of targeted gastric cancer therapeutics.\u003c/p\u003e","manuscriptTitle":"Whole Transcriptome and Functional Genomic Landscape of A South Indian Cohort of Gastric Tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-29 13:58:25","doi":"10.21203/rs.3.rs-1476204/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"622a7941-dd5a-4e90-a81b-78f006638933","owner":[],"postedDate":"March 29th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-03-29T13:58:26+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-29 13:58:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1476204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1476204","identity":"rs-1476204","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}