Distinct diagnostic and prognostic values of γ‑aminobutyric acid type A receptor family genes in patients with colon adenocarcinoma
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- Published online on: April 24, 2020 https://doi.org/10.3892/ol.2020.11573
- Pages: 275-291
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Copyright: © Yan et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Colorectal cancer (CRC) is a type of malignant tumor originated from colon and rectum epithelium (1). Most cases of CRC develop slowly through normal mucosal adenoma-cancer sequence for several years and it is one of the most common malignant tumors in the clinic worldwide (2,3). In 2018, the global incidence of colorectal cancer was third from the top among the 36 types of cancer and the mortality rate ranked second and 1.8 million individuals were diagnosed with colorectal cancer in the world (4), the number of deaths due to colorectal cancer was approximately 881,000. Colon cancer is a type of colorectal cancer and accounts for a large proportion of colorectal cancer cases approximately 60.9% in the world in 2018 (4,5). The primary risk factors associated with the disease are elderly, male sex, increased levels of fat consumption, high level of red meat and processed food consumption, lack of exercise, smoking, high alcohol intake (>1 drink/day) (6), obesity and being tall (4,7). The treatment methods of COAD included radiotherapy, surgery, targeted therapy and chemotherapy. Although a great deal of effort has been made to understand the underlying molecular mechanisms of the occurrence and development of COAD, the prevention and treatment of early-onset COAD is still a challenge for researchers (8). Therefore, sensitive and specific biomarkers are needed to improve early diagnosis, aid the management of individualized therapy and predict the prognosis of patients at different stages of the COAD.
γ-Aminobutyric acid (GABA) is the principal inhibitory neurotransmitter in the mammalian brain. γ-Aminobutyric acid type A (GABAA) receptors are the primary mediators of inhibitory neurotransmission in the mature brain, which also functions as an agonist-gated ion channel that mediates rapid synaptic inhibition in the mammalian central nervous system (9). The GABAA receptor subunit is mainly expressed in the cerebellum and its receptor is located in cerebellum, but GABAA is also expressed in testis and CD4-T-cells (10,11). The GABAA receptor (GABR) subunits are a superfamily consisting of 19 subunits: α1-α 6 (GABRA1, GABRA2, GABRA3, GABRA4, GABRA5 and GABRA6); β 1-β 3 (GABRB1, GABRB2 and GABRB3); γ 1-γ 3 (GABRG1, GABRG2 and GABRG3); δ (GABRD); ε (GABRE); π (GABRP); θ (GABRQ); and ρ 1-ρ 3 (GABRR1, GABRR2, GABRR3) (9,12,13). However, the data regarding the mRNA expression levels of five GABAA family genes, including GABRA1, GABRA5, GABRG1, GABRA6 and GABRR3, were not available in The Cancer Genome Atlas (TCGA) database. Thus, only 14 genes were analyzed in the present study. Previous study showed that overexpressed GABRD was observed in 89% of cases and had a weak negative correlation with tumor proliferation, proliferative-independent genes are upregulated in tumors and GABAA receptors might play a role in the differentiation of tumor cells (14). However, the diagnostic and prognostic value of GABRD and its family members had not been thoroughly and systematically described. In the present study, the role of the GABA family in colon cancer was studied using the TCGA database to obtain survival-associated and GABAA family expression in patients with COAD patients and the diagnostic and prognosis value of the mRNA expression levels of GABAA family genes were investigated. A few online data portals were applied to analyze functions and signaling pathways to predict the function of these genes.
Materials and methods
Data preparation
The mRNA expression levels and clinical information associated with COAD, including sex, age and tumor-non-metastasis (TNM) stage (8), were obtained from TCGA (cancer.gov/tcga). Overall, 456 patients were performed by mRNA sequencing. The expression data included 480 tumor tissues and 41 adjacent normal tissues. The Bioconductor package (edgeR, version 3.24.3; R, version 3.6.0 software; rstudio, version 1.2.5019) was used to standardize and correct the original data (15). Genes with P-value<0.05 and |log2 fold-change (FC)|>2 were deemed to be significantly different. These genes were regarded as differentially expressed genes (DEGs) (16). First, tumor tissues and adjacent normal tissues data were isolated and then the gene expression data were integrated with clinical information. Finally, patients who had repetition of the data, a survival time of 0 days or no follow-up data were excluded. In the end, 438 tumor tissues and 41 adjacent normal tissues were analyzed in the final research.
mRNA co-expression and functional analysis
In order to analyze the biological pathways and significance of the GABAA family genes, a set of functional enrichment analyses were carried out using Database for Annotation, Visualization and Integrated Discovery (DAVID 6.8, david.ncifcrf.gov/home.jsp) (17,18). Enriched P-values <0.05 had statistical significance. These included the terms Gene Ontology (GO) functional examination and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The functional detection of Molecular functional (MF), cell component (CC) and Biological process (BP) were based on the analysis of GO terminology.
Biological Networks Gene Ontology (BiNGO) (19) was chosen as a tool for GO functional analysis
BiNGO predicted gene function through the consequences of correlation analysis. Gene Multiple Association Network Integration Algorithm (GeneMANIA) was applied for the calculation of the 14 genes of GABAA family (20,21). The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used to evaluate protein-protein interactions (22) and was applied to evaluate the function and physiological relationships between the GABAA family genes. A total score >0.15 was considered to be statistically significant.
Co-representation matrix of GABAA families
The correlation between GABAA family genes in COAD was determined using Pearson correlation coefficient analysis. An absolute value of correlation coefficient >0.4 was considered strong correlation.
Gene expression level characteristics
Metabolic Gene Rapid Visualizer (MERAV) was performed to create boxplots of the differentially expressed genes of the GABAA family in primary colon cancer tissue and normal colon tissue (23). GABAA gene expression levels in tumor and adjacent normal tissues were used to construct vertical scatterplots. In addition, the differential expressed genes of the GABAA family were screened with the median cut-off values of all genes. Patients who possessed higher value than the median values of GABAA genes expression were classified as the high expression group and the other patients were classified into the low expression group.
Diagnostic forecast
GraphPad Prism version 7 (GraphPad Software) was used to construct receiver operating characteristics (ROC) curves to investigate the prognostic value of the GABAA genes in patients with COAD in TCGA database. Then the correlation between diagnosis associated genes and tumor stage was investigated using a Spearman's test and Gene Expression Profiling Interactive Analysis (24). The normalized diagnostic value of P<0.05 was considered to indicate a statistically significant difference.
Survival analysis
According to the median cut-off value of each GABAA genes, the patients were categorized into low and high expression groups. P-value and overall survival (OS) of the GABAA gene family and clinical data were calculated using Kaplan-Meier analysis and a log-rank test.
To assess the prognostic model thoroughly, a Cox proportional risk regression model for univariate and multivariate survival tests was performed. After adjusting the clinical characteristics, 95% confidence intervals (CIs) and hazard ratios (HRs) were calculated by conducting Cox proportional risk regression model.
Joint-effects analysis
Based on previous survival analysis, joint-effects analysis (25,26) of the prognostic associated genes (GABRB1, GABRD, GABRP and GABRQ) was performed to analyze the effect of polygenes on the survival of patients. Use the following combinations for joint analysis:1) GABRB1 and GABRD; 2) GABRB1 and GABRP; 3) GABRB1 and GABRQ; 4) GABRD and GABRP; 5) GABRD and GABRQ; 6) GABRP and GABRQ; 7) GABRB1, GABRD and GABRP; 8) GABRB1, GABRD and GABRQ; 9) GABRB1, GABRP and GABRQ; 10) GABRD, GABRP and GABRQ. Each combination was divided groups based on the median gene expression mentioned earlier (e.g. combination A and B: Group 1=low A+ low B, group 2=low A+ high B or high A+ low B, group 3=high A +high B; combination A, B and C: Group 1=low A+ low B+ low C, group 2=low A+ low B+ high C or low A+ high B+ low C or high A+ low B+ low C, group 3=high A+ high B+ low C or high A+ low B + high C or low A+ high B + high C; group 4=high A+ high B+ high C). According to the above combination, the Cox proportional risk regression model was adjusted for statistical significance factors (i.e., TNM stage). Kaplan-Meier method and log-rank test were used to evaluate the prognostic value of GABAA genes combination expression in each group.
Nomogram
A nomogram was used to assess the association between GABRB1, GABRD, GABRP, GABRQ and medical rank (gender, age, stage) in terms of OS for patients with COAD. In addition, the potential of these four genes in predicting clinical grade was evaluated.
In terms of clinical data and survival analysis, only tumor stage and GABRB1, GABRD, GABRP and GABRQ expression level entered the risk model after being adjusted by cox proportional hazard regression model. The risk score for all factors were calculated as well as the 1-, 2-, 3-, 4- and 5-year survival rates (27).
Gene set enrichment analysis (GSEA)
In order to explore the differences in pathway and biological functions between low- and high-expression groups of the prognostic GABAA genes, the expression profile of the full-genome dataset in TCGA group was divided into two groups according to the median prognostic GABAA gene value. GSEA version 3.0 (software.broadinstitute.org/gsea/index.jsp) was applied to explore potential KEGG pathway and GO analysis within the Molecular Signatures Database of c2 curated gene set and c5 GO gene set (28). Criteria for significant enrichment gene sets in GSEA were: P<0.05, False discovery rate <0.25.
Statistical analysis
Statistical analyses were performed using SPSS 20.0 (IBM Corp.) and R version 3.6.0 software. P<0.05 was considered to indicate a statistically significant difference. DAVID was applied to analyze GO and KEGG pathways. The interactive network of the target genes was constructed using Cytoscape version 3.6.1. An unpaired t-test was used to compare data between COAD tumors and adjacent normal tissues. A Spearman's test was performed for the correlation analyses between TNM stages and GABRD expression levels.
Results
Gene expression dataset
Detailed baseline characteristics of 438 patients with COAD patients from TCGA database are summarized in Table I. Sex and age were not associated with OS (all P>0.05), whereas TNM stage was significantly associated with OS (adjusted log-rank test P<0.001).
Bioinformatics analysis of GABAA family genes
The biological functional of the GABAA genes was investigated using DAVID to evaluate GO functions and KEGG pathways (Fig. 1), BiNGO was applied to examine the enrichment outcomes (Fig. 2A) and the co-expression of the protein level was examined as shown in Fig. 2B. The interaction between GABAA gene expression levels was presented in Fig. 3. The above results indicate that GABAA genes were involved in the transport of substances and the formation of plasma membrane. In addition, the genes are strongly co-expressed and have complex networks of gene-gene and protein-protein interactions.
Through Pearson correlation coefficient analysis, it was found that there was a correlation between the expression levels of a single GABAA gene. The expression level of GABRB1 was correlated with GABRA2 and GABRA4; GABRA4 were correlated with GABRB1; GABRA3 was correlated with GABRG3; GABRG3 were correlated with GABRA3, GABRQ and GABRG2; GABRQ were correlated with GABRG3 and GABRG2 (correlation coefficient >0.4; Fig. 4A).
Gene expression and diagnostic value of the GABAA gene family
The vertical scattering map of GABAA gene expression levels was shown in Fig. 4B, it showed that the results showed that GABRA2, GABRB2, GABRB3 and GABRG2 had low expression in tumor tissues; GABRB1, GABRD, GABRE and GABRP had high expression in tumor tissues. The correlation between gene expression and TNM stage showed that the expression levels of GABRD was significantly different in the four tumor stages (I, II, III and IV) from GEPIA (Fig. 4C). In our TCGA database, GABRD expression levels were associated with TNM stage also showed significantly weak positive correlation (Correlation Coefficient=0.174, Table II). The results of MERAV showed that the expression levels of GABRA2, GABRA3, GABRB2, GABRB3, GABRG3 and GABRR1 in primary colon tumor tissues were lower compared with normal tissue (Fig. 5A, B, E, F, H and M), whereas the expression levels of GABRA4, GABRB1, GABRG2, GEBRD, GABRE, GABRP and GABRR2 in primary colon tumor tissues was higher compared with normal colon tissue (Fig. 5C, D, G, I-L and N). In addition, ROC curves of the predicted expression levels of the GABAA family genes in tumors and paired colon tissues was constructed (Fig. 6). The expression levels of GABRA2 (Fig. 6A), GABRA3 (Fig. 6B), GABRB2 (Fig. 6E), GABRB3 (Fig. 6F), GABRG2 (Fig. 6G), GABRG3 (Fig. 6H), GABRD (Fig. 6I) and GABRE (Fig. 6J) were significantly associated with the carcinogenesis of colon tumors (AUC >0.7).
 | Table II.Spearman's correlations test between GABRD expression and Tumor-Node-Metastasis stage in patients with colon adenocarcinoma in The Cancer Genome Atlas dataset. |
Survival analysis
Univariate survival analysis demonstrated that tumor staging was the only factor associated with OS (P<0.001, Table I). The Kaplan-Meier curve of the GABAA family genes were presented in Fig. 7A-N. Tumor staging was investigated using Cox proportional hazards regression model for multivariate survival tests, wherein the lower expression levels of GABRB1, GABRD, GABRP and GABRQ were significantly correlated with favorable OS results (adjusted P=0.049, HR=1.517, 95% CI=1.001–2.297; adjusted P=0.006, HR=1.807, 95% CI 1.180–2.765; adjusted P=0.005, HR=1.833, 95% CI 1.196–2.810 and adjusted P=0.034, HR=1.578, 95% CI 1.036–2.405, respectively; Table III).
| Table III.Prognostic survival analysis according to high or low expression of γ-aminobutyric acid type A receptor family genes in 438 patients with colon adenocarcinoma. |
The nomogram of scoring risk included the expression levels of GABRB1, GABRD, GABRP and GABRQ and predictive TNM stage, sex, age and 1-, 2-, 3, 4- and 5-year survival rates (Fig. 8), it showed that the above risk factors contribute to the risk points, among which the age contribution is the smallest one and the stage contribution is the largest one, The higher the risk points, the lower the survival rates.
Effect of GABAA genes expression combination on OS
Based on the survival analysis of GABAA genes, GABRB1, GABRD, GABRP and GABRQ were selected as prognostic genes by multivariate survival analysis. The joint-effects of these four GABAA genes on OS in patients with COAD were determined by the joint-effects model. According to the expression levels of GABRB1, GABRD, GABRP and GABRQ, different combinations for this analysis were generated (Tables IV–V). Log-rank tests were performed using Kaplan-Meier analysis to evaluate the effect of gene expression combinations on the prognosis of patients with COAD (Fig. 9). In the analysis of high expression levels of GABRB1, GABRD, GABRP and GABRQ, the combinations in groups 3, 9, 12, 15, 18, H and P were highly correlated with poor OS (all P<0.05; Table VI). Within the evaluation of low GABRB1, GABRD, GABRP and GABRQ expression levels, the combination of groups 1, 7, 10, 13, 16, A, E, I and M were highly correlated with favorable OS (all P<0.05; Table VII).
| Table VI.Joint analysis of the prognostic value of 2-gene combinations in GABRB1, GABRD, GABRP and GABRQ expression of patients with colon adenocarcinoma. |
| Table VII.Joint analysis of the prognostic value of 3 genes combination in GABRB1, GABRD, GABRP and GABRQ expression of patients with colon adenocarcinoma. |
GSEA
GSEA of the prognostic genes GABRB1, GABRD, GABRP and GABRQ were performed in the TCGA cohorts (Fig. 10). In the GSEA of KEGG pathways, the expression levels of the GABRD were associated with the chondroitin sulfate pathway (Fig. 10H) and GABRP was associated with the intestinal immune network for Immunoglobulin A (IGA) production, hematopoietic cell lineage, the natural killer cell mediated cytotoxicity pathway, sphingolipid metabolism (Fig. 10I-L). GO function enriched examination demonstrated that that GABRD expression levels were associated with the cell matrix adhesion, integrin, angiogenesis, endothelial growth factor, endothelial migration regulation, and so on (Fig. 10A-G); whereas GABRB1 and GABRQ had no significant outcomes.
Discussion
In the present study, the diagnostic and prognosis value of the GABAA family genes based on TCGA database were investigated. The results of ROC curves showed that expression levels of GABRB3, GABRG2, GABRD and GABRE had high values to predict the occurrence of colon cancer, among them, GABRD was associated with COAD stage and may have value as an early diagnostic index of COAD. The results were roughly the same as verified in MERAV and Vertical scatterplots. Low expression levels of GABRB1, GABRD, GABRP and GABRQ were associated with favorable COAD OS and the nomogram indicated these four genes had different degrees of influence on the prognosis of the patients, high expression of GABRB1, GABRD, GABRP have high contribution to the risk score than high expression of GABRQ. In the functional evaluation of GO and KEGG, it was found that the functions of the GABAA gene family were significantly enriched in cell junction, integral component of membrane, signal transduction, integral component of plasma membrane.
GABAA receptors have the same structure with nicotinic acetylcholine receptors, the 5-hydroxytryptamine type 3 receptor and zinc-activated channel, all with pentameric structures and belonging to the agonist-gated ion channel superfamily (29). STRING results showed that obvious gene fusions, gene co-occurrence and co-expression between GABAA genes. Pearson correlation coefficient analysis showed that there was a correlation between the expression levels of some genes in the GABAA family, especially between GABRB1 and GABA4, and GABRQ and GABRG2.
The GABAA family genes also serve a role in several types of cancer, Gumireddy et al (30) found that the high expression levels of GABRA3 were inversely proportional to the survival rate of patients with breast cancer and that GABRA3 activated the AKT pathway which promoted the migration, invasion and metastasis of breast cancer cells. Therefore, GABRA4 might serve a role in COAD, which requires further study. Bautista et al (31) observed that the expression levels of GABRA6 in tumor initiating stem cells (TISCS) and hepatocellular carcinoma (HCC) were reduced, whereas the expression levels of GABRG3 were abundant in TISCS and limited in HCC. A previous study showed that the specific activation of GABAA receptor decreased cell activity, induced apoptosis and inhibited the growth and survival signal pathway of neuroblastoma cells (32). Chen et al (33) found that GABAA receptor could inhibit the migration and invasion of human hepatocellular carcinoma cells and Minuk et al (34) reported downregulated expression of the GABRB3 receptor in liver tissue of human hepatocellular carcinoma, which was consistent with COAD in the present study. Takehara et al (35)found that GABA promoted the growth of pancreatic cancer by expressing GABAA receptor GABRP subunit. Zhang et al showed that RNA binding protein nova 1 and GABRG2 interacted in the central nervous system and in liver cancer. Nova 1, as a potential mechanism of oncogene, might interact with GABRG2 (36). To sum up, the GABAA family plays an important role in many cancer types, Nevertheless, the correlation between GABAA family and COAD is unclear. Here, we use the TCGA database to study the correlation of GABAA gene family expression with diagnosis and prognosis.
GSEA analysis showed that GABRD was associated with cell matrix adhesion and integrin binding. Cell adhesion is an important cellular process that could lead to cancer (37,38). As the main receptor of cell matrix adhesion, integrin exists on the surface of tumor and stroma cells, which had a profound impact on cancer cell's ability to survive in a specific location, cell adhesion and integrin can worked together to lead to apoptosis (39). In addition, integrin also serves a role in promoting the phenotype of tumor cells (40). The present study also suggested that GABRD was significantly associated with angiogenesis and endothelial migration regulation in GSEA. These factors serve a role in tumor invasion and migration (41–43). In addition, tumor angiogenesis is also one of the markers of tumor progression and the increase of tumor microvessel density is an index of poor prognosis (44). Park et al reported that human γ-aminobutyrate type A receptor-binding protein (GABARBP) could inhibit angiogenesis by directly binding to vascular endothelial growth factor receptor 2 (VEGFR-2) to inhibit the phosphorylation of PI3K/AKT pathway related proteins (45). GABARBP served a role in regulating the activity of GABAA receptor, a key participant in intracellular trafficking in all the GABAA receptors (46–48). Therefore, the GABAA family genes may affect angiogenesis through regulating GABRBP, which needs to be verified in future experiments. In the present study, KEGG pathway analysis showed that GABRD was associated with chondroitin sulfate synthesis. Chondroitin sulfate serves a role in cancer metastasis and chondroitin sulfate-E negatively adjusted breast cancer cell motility through the Wnt/β-catenin-Collagen I axis (49,50).
In the present study, it was observed that the expression of GABRD mRNA in adjacent tissues was significantly lower compared with COAD tumor tissues, which was consistent with the results of a previous study (14). KEGG pathway analysis of the present study showed that GABRP was associated with intestinal immune network for IGA production, hematopoietic cell lineage, natural killer (NK) cell mediated cytotoxicity and sphingolipid metabolism. In previous studies, people with IgA deficiency were found to have a moderately increased risk of cancer, especially gastrointestinal cancer (51). NK cells also play an important role in mediating immune surveillance for human cancer (52). As the structural molecules of cell membranes, sphingolipids play an important role in maintaining barrier function and fluidity 2)(53), Besim considered that signaling nodes in sphingolipid metabolism, such as sphingolipids, metabolic enzymes, and/or receptors, are new therapeutic targets for the development of new anticancer intervention strategies (54).
At present, few reports have been reported on GABRB1 in tumor field, our present study showed that GABRB1 was differentially expressed in tumor and adjacent normal tissues and that high expression levels of GABRB1 in patients with COAD was associated with a less favorable OS. Hence, GABRB1 may also have potential as a prognosis biomarker of COAD.
In a previous study, GABRD was upregulated in patients with COAD and was not associated with proliferation (14), which is consistent with the results of the present study. Sarathi et al found GABRD was significantly monotonically upregulated across stages in hepatocellular carcinoma (55). In the present study, it was demonstrated that the expression of GABRD in COAD was significantly upregulated compared with normal tissues. Low expression levels of GABRD were associated with a more favorable prognosis and could be used as a biomarker for prognosis.
At present, it is known that GABRP serves a role in cancer development and progression. Menelaos et al found that GABRP gradually downregulated as tumors progressed, and it may serve as a prognostic marker for breast cancer (56). In contrast, Symmans et al (57) found increased expression of GABRP gene in undifferentiated cell type breast cancer and is significantly associated with shorter lifetime history of breastfeeding and with high-grade breast cancer in Hispanic women. Sung et al found that GABRP enhances aggressive phenotype of ovarian cancer cells (58). Jiang et al found that the expression of GABRP in pancreatic cancer tissues was significantly increased and associated with poor prognosis, contributing to tumor growth and metastasis (59). In our study, we found that the expression of GABRP in cancer tissues was higher than in adjacent normal tissues and high expression of GABRP are associated with poor prognosis of patients with COAD, which were consistent with the previous studies. It was also shown that OS was less favorable in patients with COAD with high expression levels of GABRP compared with patients with low expression levels of COAD.
Li et al (60) demonstrated that the overexpression of GABRQ was associated with the occurrence and development of HCC and might to become a molecular target for new diagnosis and treatment strategies for HCC. The multivariate COX proportional hazards model in the present study divided patients with COAD into groups based on high and low expression levels of GABRQ and showed that patients with high expression levels had a less favorable OS.
There were some limitations in the present study. First, the sample size was relatively small. Second, the clinical data were slightly inadequate, such as Event-free Survival (EFS) information, smoking, drinking history, tumor size and lymph node metastasis were not available from TCGA database. Therefore, it was not possible to perform a far-reaching survival analysis of GABAA genes considering each potential prognostic variable of COAD in the multivariate Cox proportional hazards regression model. Third, although the association between the GABAA gene family mRNA levels and COAD prognosis was investigated, the association between GABAA family protein levels and COAD, GABAA genes and GSEA still require further experimental research. Experiments like cell migration assays, detection of sulfuric acid related pathways at protein level and the functions of these genes in common cancer-related pathways, such as PI3K/AKT signaling pathway (61), JAK/STAT signaling pathway (62), should be conducted in future. However, despite these limitations, the present study further showed that the downregulated expression levels of GABRB1, GABRD, GABRP and GABRQ in COAD was associated with a more favorable prognosis and the potential mechanisms of GSEA associated with to GABRD and GABRP in the prognosis of COAD were studied. These results need to be verified with a larger sample size to confirm the role of the GABAA family genes in the diagnosis and prognosis of COAD in the future.
Overall, the present study showed that the upregulated expression levels of GABRA2, GABRA3, GABRB2, GABRB3, GABRG2, GABRG3, GABRD and GABRE in COAD may have potential diagnostic value in COAD. In addition, the low expression levels of GABRB1, GABRD, GABRP and GABRQ were associated with a more favorable prognosis of patients with COAD and could be used as a prognostic biomarker. Multivariate survival analysis, nomograms and joint survival analysis showed that the high expression of GABRB1, GABRD, GABRP and GABRQ were associated with poor prognosis of COAD. GSEA suggested that GABRD may impact cell adhesion, integrin binding, angiogenesis and so on; GABRP was associated with intestinal immune network for IGA production, hematopoietic cell lineage, and so on. However, the results of the present need to be confirmed by further research.
Acknowledgements
The authors thank the contributors of The Cancer Genome Atlas (portal.gdc.cancer.gov/) and proteinatlas.org for their contribution to share the colon adenocarcinoma dataset on open access.
Funding
The present study was supported by the Innovation Project of Guangxi Graduate Education (grant no. JGY2019052) and Self-financing Scientific Research Project of Guangxi Zhuang Autonomous Region Health Commission, China (grant no. Z20180959).
Availability of data and materials
The analyzed datasets generated during the study are available in The Cancer Genome Atlas repository (cancer.gov/tcga).
Authors' contributions
LY, MS and JG conceived and designed the study. XL, XW, QH, YG, HX and GR processed the data and performed the statistical analysis and they also generated and modified the figures. LY, LZ, XZ and FG wrote and revised the manuscript and helped to perform the analysis and interpretation of data. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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