# Clinical Data and Biocalculation Methods of GABRD Determine the Clinical Characteristics and Immune Relevance of Colorectal Cancer

Authors: Yuhe Bi; Xinju Liu; Wei Li; JiaCheng Xu; Jie Xi; Shengchao Wei
Journal: Evidence-Based Complementary and Alternative Medicine
 (2022)

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## Abstract

Background. The aim of this study was to clarify the expression of gamma-aminobutyric acid type A receptor delta subunit (GABRD) gene in pan-cancer and its correlation with patient prognosis, and to investigate the function and possible mechanism of GABRD in colorectal cancer (CRC). Methods. The Cancer Genome Atlas (TCGA) data were used to analyze the expression differences of GABRD in pan-cancer, and the correlation between GABRD and clinical prognosis of various tumors was analyzed by Cox regression method. According to the expression level of GABRD, Gene Function Annotation (GO) and Kyoto Encyclopedia of Genomes (KEGG) functional enrichment analysis were performed on the differentially expressed genes. Expression of GABRD gene and 44 marker genes of three types of RNA modification (m1A (10), m5C (13), m6A (21)) genes in different tumors was observed. Pearson correlation of GABRD gene and marker genes of five immune pathways was measured. Results: TCGA data analysis showed that GABRD was significantly upregulated in various tumor tissues, especially COAD and READCOAD. Survival analysis showed that GABRD was a prognostic protective factor in CRC (p<0.001). The results of survival nomogram showed that GABRD, age, and tumor (T) lymph node (N) distant metastasis (M) stage were independent prognostic factors, and the survival model C-index was 0.724 (0.644-1). Gene enrichment and functional analysis showed that GABRD may be related to protein digestion and absorption, ECM-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, and antimicrobial humoral response. The expression of GABRD was positively correlated in m1A-, m5C-, and m6A-related genes. The GABRD gene was found in B cell, T cell CD4, T cell CD8, neutrophil, macrophage in TCGA-COAD (N = 282), and TCGA-COADREAD (N = 373). The infiltration level and DC was significantly positively correlated (p<0.05). Also, the Pearson correlation coefficient is the largest. Conclusion. The involvement of GABRD in the occurrence and development of CRC may be related to protein digestion and absorption, ECM-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, and antimicrobial humoral response. GABRD can be used as a molecular marker for the prognosis of CRC.


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## Body

## 1. Introduction

Colorectal cancer is the most common malignant tumor of the digestive tract. Worldwide, it ranks fourth and third in the number of deaths and incidences among male cancers and third and second among female cancers [1]. Clinically, the treatment of colorectal cancer is mainly based on surgery and comprehensive treatment with the help of radiotherapy and chemotherapy, but due to the bottom location of colorectal cancer, deep into the pelvis, and complex anatomical relationship, it is easy to cause incomplete surgery and relatively high recurrence rate after surgery, and patient efficacy and prognosis assessment are crucial for patients; in the past, the prognosis assessment of colorectal cancer mostly relied on clinical and pathological, and with the development of molecular biology, research on the relationship between molecular biology markers and tumor prognosis has become a trend [2]. The search for effective therapeutic targets and prognostic molecular markers is important for the early diagnosis and treatment of colorectal cancer [3]. The research on targeted molecular markers is still on the way of exploration and development, and the realization of individualized and precise treatment of colorectal cancer still needs continuous exploration and research.It has been suggested that bioinformatics approaches may be fruitful for identifying novel targets for CRC therapy [4]. Identification of genes differentially expressed in CRC patients with different clinical outcomes may prove to be a more favorable approach to identify new targets. Indeed, prognostic CRC genes have been identified in patients by survival [5]. Here, we used this approach to examine the GABRD system genes in CRC patients’ tumors.GABRD (gamma-aminobutyric acid type A receptor delta subunit) is a ligand-gated ion channel-type receptor that is closely associated with a variety of neurological and psychiatric disease-related symptoms and cancer development [6, 7]. It has been found that GABRD is one of the strongest upregulated genes in tumor tissues [8]. The expression of GABRD was significantly upregulated in different tumor stages [9]. In addition, GABRD gene may be a prognostic marker in gliomas with low expression, and it is associated with poor prognosis [10, 11]. In another study, GABRD expression was found to be significantly lower in IDH wild-type diffuse gliomas compared to IDH mutant tumors [12], and patients with high GABRD expression had a better prognosis than those with low GABRD expression [13]. Taken together, it is suggested that the functional mechanism of GABRD in cancer needs to be investigated on specific cancer types.TheGABRD gene has been poorly studied and reported, and its primary function is unknown. However, some studies have found that high expression of GABRD mRNA can promote CRC progression to advanced TNM stage [14]. GABRD expression was analyzed using colorectal cancer patient data onto the University of California Santa (UCSC), The Cancer Genome Atlas (TCGA), and other databases. Potential regulatory mechanisms in colorectal cancer were then explored, leading to a deeper understanding of the relationship between GABRD and colorectal cancer. This also provides support for GABRD as a prognostic marker for colorectal cancer patients and provides clues to further study the related functional mechanisms.


## 2. Materials and Methods

### 2.1. RNA-Seq Data Source

First of all, we download a unified and standardized pan-cancer data set: TCGAPan-Cancer (PANCAN,N = 10535, G = 60499) from the UCSC database (https://xenabrowser.net/). This database collects data from normal human tissues for sequencing and can be used to study differential gene expression between different tissues and between normal and diseased tissues. In addition, because The Cancer Genome Atlas (TCGA) collects data primarily from cancerous tissues, it can be used in conjunction with the TCGA database to ensure more reliable results. The data are then downloaded from the TCGA database (https://cancergenome.nih.gov/). TCGA contains data mainly from cancer tissues, including data from 33 types of tumors. Fragments of million per kilobase (FPKM) values are converted into transcripts of million per kilobase (TPM) values, and further logarithmic conversions are made to better compare the comparisons between samples.


### 2.2. GABRD Differential Expression

The RNA-seq data (level 3) and corresponding clinical information of 620 CRC tumors were obtained from the cancer genome map (TCGA) dataset (https://portal.gdc.com). The differential expression of mRNA was studied by using the Limma package of R software. The adjusted P value was analyzed in TCGA to correct the false positive results. “Adjusted P<0.05and log2 (multiple change) > 1 or log2 (multiple change) <−1” is defined as the screening of threshold mRNA differential expression.In order to further confirm the potential function ofGABRD gene, the data were analyzed by rich functions. Gene Ontology (GO) is a tool widely used to annotate functional genes. KEGG enrichment analysis is practical and can be used to analyze gene function and related advanced genomic function information. In order to better understand the role of GABRD gene, the ClusterProfiler package in R was used to analyze the GO function of potential mRNA and enrich the KEGG pathway.


### 2.3. GABRD Genes and Survival

The RNA-seq data (level 3) and corresponding clinical information of CRC tumors were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.com). Log rank was used to test KM survival analysis to compare the survival differences between the two groups, and timeROC analysis was carried out to compare the predictive accuracy of GABRD gene. For the Kaplan–Meier curve, the P value and the hazard ratio (HR) with 95% confidence interval (CI) were obtained by the logrank test and univariate Cox regression. For the Kaplan–Meier curve, the P value and the hazard ratio (HR) with 95% confidence interval (CI) were obtained by the logrank test and univariate Cox regression.


### 2.4. Analysis of GABRD Gene and Immune Correlation

To investigate the immune expression relationship of GABRD gene in CRC, we screened the samples with sample sources: primary blood-derived cancer: peripheral blood and primary tumor. We also filtered all normal samples. Furthermore, log2 (x + 1) transformation was performed. Next, we calculated the Pearson correlation between ENSG00000187730 (GABRD) and the marker genes of the five types of immune pathways. Also, the correlations were calculated and analyzed using Spearman rank correlation coefficients (∗P<0.05; ∗∗P<0.01; ∗∗∗P<0.001).


### 2.5. Construction and Evaluation of Predictive Line Graphs

Combining all the independent prognostic factors revealed by multivariate analysis, a row chart was obtained to predict the probability of survival of CRC-1 years: 2 years and 3 years. The performance of the line chart is evaluated by using the consistency index (C-index) and the calibration curve.


### 2.6. Correlation Expression Analysis of GABRD Genes

From them, we extracted the correlation expression data of ENSG00000187730 (GABRD) gene and 44 marker genes of three classes of RNA modification (m1A (10), m5C (13), m6A (21)) genes in different tumors. We next calculated the Pearson correlation between ENSG00000187730 (GABRD) and marker genes of five classes of immune pathways.


### 2.7. Statistical Analysis

Univariate regression analysis was used to detect the expression and prognosis ofGABRD gene in CRC tumors and tissues. The correlation between the gene expression of GABRD gene and the expression of immune infiltration level in CRC cancer was assessed using Spearman’s correlation coefficient. P value <0.05 was considered statistically significant.


## 2.1. RNA-Seq Data Source

First of all, we download a unified and standardized pan-cancer data set: TCGAPan-Cancer (PANCAN,N = 10535, G = 60499) from the UCSC database (https://xenabrowser.net/). This database collects data from normal human tissues for sequencing and can be used to study differential gene expression between different tissues and between normal and diseased tissues. In addition, because The Cancer Genome Atlas (TCGA) collects data primarily from cancerous tissues, it can be used in conjunction with the TCGA database to ensure more reliable results. The data are then downloaded from the TCGA database (https://cancergenome.nih.gov/). TCGA contains data mainly from cancer tissues, including data from 33 types of tumors. Fragments of million per kilobase (FPKM) values are converted into transcripts of million per kilobase (TPM) values, and further logarithmic conversions are made to better compare the comparisons between samples.


## 2.2. GABRD Differential Expression

The RNA-seq data (level 3) and corresponding clinical information of 620 CRC tumors were obtained from the cancer genome map (TCGA) dataset (https://portal.gdc.com). The differential expression of mRNA was studied by using the Limma package of R software. The adjusted P value was analyzed in TCGA to correct the false positive results. “Adjusted P<0.05and log2 (multiple change) > 1 or log2 (multiple change) <−1” is defined as the screening of threshold mRNA differential expression.In order to further confirm the potential function ofGABRD gene, the data were analyzed by rich functions. Gene Ontology (GO) is a tool widely used to annotate functional genes. KEGG enrichment analysis is practical and can be used to analyze gene function and related advanced genomic function information. In order to better understand the role of GABRD gene, the ClusterProfiler package in R was used to analyze the GO function of potential mRNA and enrich the KEGG pathway.


## 2.3. GABRD Genes and Survival

The RNA-seq data (level 3) and corresponding clinical information of CRC tumors were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.com). Log rank was used to test KM survival analysis to compare the survival differences between the two groups, and timeROC analysis was carried out to compare the predictive accuracy of GABRD gene. For the Kaplan–Meier curve, the P value and the hazard ratio (HR) with 95% confidence interval (CI) were obtained by the logrank test and univariate Cox regression. For the Kaplan–Meier curve, the P value and the hazard ratio (HR) with 95% confidence interval (CI) were obtained by the logrank test and univariate Cox regression.


## 2.4. Analysis of GABRD Gene and Immune Correlation

To investigate the immune expression relationship of GABRD gene in CRC, we screened the samples with sample sources: primary blood-derived cancer: peripheral blood and primary tumor. We also filtered all normal samples. Furthermore, log2 (x + 1) transformation was performed. Next, we calculated the Pearson correlation between ENSG00000187730 (GABRD) and the marker genes of the five types of immune pathways. Also, the correlations were calculated and analyzed using Spearman rank correlation coefficients (∗P<0.05; ∗∗P<0.01; ∗∗∗P<0.001).


## 2.5. Construction and Evaluation of Predictive Line Graphs

Combining all the independent prognostic factors revealed by multivariate analysis, a row chart was obtained to predict the probability of survival of CRC-1 years: 2 years and 3 years. The performance of the line chart is evaluated by using the consistency index (C-index) and the calibration curve.


## 2.6. Correlation Expression Analysis of GABRD Genes

From them, we extracted the correlation expression data of ENSG00000187730 (GABRD) gene and 44 marker genes of three classes of RNA modification (m1A (10), m5C (13), m6A (21)) genes in different tumors. We next calculated the Pearson correlation between ENSG00000187730 (GABRD) and marker genes of five classes of immune pathways.


## 2.7. Statistical Analysis

Univariate regression analysis was used to detect the expression and prognosis ofGABRD gene in CRC tumors and tissues. The correlation between the gene expression of GABRD gene and the expression of immune infiltration level in CRC cancer was assessed using Spearman’s correlation coefficient. P value <0.05 was considered statistically significant.


## 3. Results

### 3.1. Relationship between Expression of GABRD mRNA and Clinicopathological Features in Patients with CRC

Expression differences between normal and tumor samples in each tumor were calculated using the R software as shown in Figure1(a), and analyzed for significance of differences using unpaired for nonparametric tests and signed rank tests, with significant upregulation observed in 23 tumors and significant downregulation observed in 3 tumors. Cox proportional hazards regression model [15] was developed using the coxph function of R package survival to analyze the prognostic relationship between GABRD gene expression and each tumor, and statistical tests were performed using the logrank test to obtain prognostic significance, Finally, it was observed that high expression in 2 tumor types TCGA-COAD (N = 278, P=1.0e−5, HR = 3.56 (2.04,6.22)), TCGA-COADREAD (N = 368, P=1.3e−4, HR = 2.47 (1.56,3.93)) had poor prognosis, and in 4 tumor types, TCGA-KIPAN (N = 855, P=0.02, HR = 0.92 (0.86, 0.99)), TCGA-KIRC (N = 515, P=2.5e−11, HR = 0.78 (0.72,0.84)), TCGA-LIHC (N = 341, P=1.6e−3, HR = 0.87 (0.79,0.95)), TCGA- ACC (N = 77,P=0.04, HR = 0.81 (0.66,0.99)) with low expression in poor prognosis as shown in Figure 1(b). Kruskal–Wallis test showed the clinical stage expression of GABRD in colorectal cancer tissues, which was significantly higher in colorectal cancer tissues than in normal tissues (P<0.05), as shown in Figure 1(c).Figure 1
GABRD gene expression. (a) GABRD expression levels in different cancer and paraneoplastic tissues. Red is tumor tissue and blue is normal tissue. (b) Survival analysis of GABRD gene in pan-cancer. (c) Clinical stage expression of GABRD gene in CRC tumors.
(a)(b)(c)


### 3.2. GABRD Single Gene Prognostic Model Construction

According to the median ofGABRD gene expression, it was divided into two groups: high and low. Based on the risk values and grouping of patients, heat maps of risk values were drawn. Kaplan–Meier survival analysis showed a significant difference between the high and low-risk groups with P<0.001 as shown in Figure 2(a)–2(c), which indicated that GABRD was a protective factor for prognosis in CRC. With higher prognostic score scores, the number of patients who died increased, i.e., the higher the risk score, the worse the prognosis of patients as shown in Figure 2(d). In addition, the reliability of the model was assessed by the ROC curve calculating the AUC area, which was 0.665 at 1 year, 0.662 at 2 years, and 0.660 at 3 years, which illustrates the reliability of using GABRD for assessing the overall survival of patients as shown in Figure 2(e).Figure 2
GABRD gene prognosis model survival analysis and risk curves. (a–c) Prognosis of colorectal cancer patients in the high-risk and low-risk groups. (d) Risk score distribution of colorectal cancer patients in the high-risk (red) and low-risk (green) colorectal cancer prognosis model; scatter plot shows the survival of CRC patients in the model; colorectal patients in the high-risk (blue) and low-risk (red) groups in the model expression of risk genes. (e) ROC curves demonstrate the predictive efficiency of risk scores.
(a)(b)(c)(d)(e)


### 3.3. GABRD Nomogram Construction

According toGABRD expression, age, gender, and TNM stage, univariate cox analysis was first used to select factors associated with prognosis. The results of univariate cox analysis showed that GABRD, age, and TNM stage were prognostic factors as shown in Figure 3(a). Multivariate results showed that GABRD, age, and TNM stage were independent prognostic factors as shown in Figure 3(b). Since tumor stage and GABRD gene signature were shown to be independent prognostic factors for CRC, nomograms containing the tumor stage and risk groups were constructed to predict OS at 1, 2, and 3 years. The survival model C-index was 0.724 (0.644–1) (Figure 3(c), and the calibration curves at 1, 2, and 3 years were located in the diagonal complex line, indicating that the model was reliable, as shown in Figure 3(d).Figure 3
Construction of column line graphs to assess overall patient survival. (a) Single-factor Cox analysis of prognostic correlates of GABRD. (b) Multifactor Cox analysis of prognostic correlates of GABRD. (c) Survival column line graph prediction model. (d) Calibration curves to predict survival at years 1, 2, and 3 in patients with colorectal cancer.
(a)(b)(c)(d)


### 3.4. Potential Mechanism of Action of GABRD Gene in CRC

Based on the false discovery rate less than 0.05 and the absolute value of logFC greater than 1 (2-fold change), 357 differentially expressed genes were identified fromGABRD high and low expression; 23 differentially expressed genes were identified from low expression (Figure 4). The results of GO functional annotation and KEGG pathway enrichment analysis showed that these differentially expressed genes were enriched in protein digestion and absorption, ECM-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, antimicrobial humoral response, and other known cancer-related biological processes and pathways, as shown in (Figure 5).Figure 4
CRC tissue differentially expressed gene heat map and volcano map. (a) Use Foldchange and correctedP value to draw a volcano map. The red dot in the diagram indicates the upregulated genes with significant difference, the blue dot indicates the downregulated genes with significant difference, and the gray dot indicates the genes that are not significant. (b) Differential gene expression heat map, in which different colors represent the expression trend in different tissues.
(a)(b)Figure 5
CRC differential gene GO and KEGG enrichment analysis. (a) Differential upregulated gene KEGG pathway enrichment results. (b) Differential downregulated gene KEGG pathway enrichment results. (c). Differential upregulated gene GO term enrichment results. (d) Differential downregulated gene GO term enrichment results. The different colors represent the significance of differential enrichment results. The larger value represents the smaller fdr value. The circle size represents the number of enriched genes, and the more the number, the larger the circle.
(a)(b)(c)(d)


### 3.5. Role of GABRD Expression and RNA Chemical Modification in CRC

Since there is a lack of studies on the functions played by GABRD in tumors, this study attempted to investigate the gene functions of GABRD, starting from the functional analysis of the associated genes of this gene. Chemical modification is an efficient way to regulate the structure and function of macromolecules, such as DNA, RNA, proteins, sugars, and lipids, which require postsynthesis and covalent modification to function in living organisms. High-throughput sequencing technology has shown that RNA modification plays a critical role in the selective expression of genes [16]. We found positive correlations between ACC, KIRC, THCA, COAD, COADREAD, OV, PAAD, GBM, and other tumors in m1A-, m5C-, and m6A-related genes by the expression of GABRD genes and 44 marker genes of three types of RNA modifications (m1A (10), m5C (13), m6A (21)) genes in pan-cancer, as shown in Figure 6 with a high correlation, especially higher in COAD and COADREAD tumors (P<0.05).Figure 6
Pan-cancer-related expression of GABRD in m1A-, m5C-, and m6A-related genes.


### 3.6. Correlation of GABRD Gene Expression with Signature Genes of Different Immune Cell Subpopulations

From Figure7(a), we know that the infiltration levels in TCGA-COAD (N = 282) and TCGA-COADREAD (N = 373) in B cell, T cell CD4, T cell CD8, neutrophil, macrophage, and DC were significantly positively correlated (P<0.05). Also, the Pearson correlation coefficient was the largest. The results indicated that GABRD expression was associated with high immune infiltration in TCGA-COAD and TCGA-COADREAD cancer types. These findings strongly suggest that GABRD affects patient survival by interacting with immune infiltration in cancers such as TCGA-COAD and TCGA-COADREAD. As shown in Figure 7(b)–7(d), for colon adenocarcinoma (COAD), GABRD expression levels were highly positively correlated with ImmuneScore (P=4.2e−3, r = 0.15), StromalScore (P=1.4e−7, r = 0.31), and ESTIMATEScore (P=4.5e−5, r = 0.24).Figure 7
Relationship between GABRD expression and immune scores. (a) Correlation analysis of GABRD gene in TCGA-COAD and TCGA-COADREAD immune infiltration. (b–d) Correlation expression of GABRD gene in different immune scores.
(a)(b)(c)(d)


## 3.1. Relationship between Expression of GABRD mRNA and Clinicopathological Features in Patients with CRC

Expression differences between normal and tumor samples in each tumor were calculated using the R software as shown in Figure1(a), and analyzed for significance of differences using unpaired for nonparametric tests and signed rank tests, with significant upregulation observed in 23 tumors and significant downregulation observed in 3 tumors. Cox proportional hazards regression model [15] was developed using the coxph function of R package survival to analyze the prognostic relationship between GABRD gene expression and each tumor, and statistical tests were performed using the logrank test to obtain prognostic significance, Finally, it was observed that high expression in 2 tumor types TCGA-COAD (N = 278, P=1.0e−5, HR = 3.56 (2.04,6.22)), TCGA-COADREAD (N = 368, P=1.3e−4, HR = 2.47 (1.56,3.93)) had poor prognosis, and in 4 tumor types, TCGA-KIPAN (N = 855, P=0.02, HR = 0.92 (0.86, 0.99)), TCGA-KIRC (N = 515, P=2.5e−11, HR = 0.78 (0.72,0.84)), TCGA-LIHC (N = 341, P=1.6e−3, HR = 0.87 (0.79,0.95)), TCGA- ACC (N = 77,P=0.04, HR = 0.81 (0.66,0.99)) with low expression in poor prognosis as shown in Figure 1(b). Kruskal–Wallis test showed the clinical stage expression of GABRD in colorectal cancer tissues, which was significantly higher in colorectal cancer tissues than in normal tissues (P<0.05), as shown in Figure 1(c).Figure 1
GABRD gene expression. (a) GABRD expression levels in different cancer and paraneoplastic tissues. Red is tumor tissue and blue is normal tissue. (b) Survival analysis of GABRD gene in pan-cancer. (c) Clinical stage expression of GABRD gene in CRC tumors.
(a)(b)(c)


## 3.2. GABRD Single Gene Prognostic Model Construction

According to the median ofGABRD gene expression, it was divided into two groups: high and low. Based on the risk values and grouping of patients, heat maps of risk values were drawn. Kaplan–Meier survival analysis showed a significant difference between the high and low-risk groups with P<0.001 as shown in Figure 2(a)–2(c), which indicated that GABRD was a protective factor for prognosis in CRC. With higher prognostic score scores, the number of patients who died increased, i.e., the higher the risk score, the worse the prognosis of patients as shown in Figure 2(d). In addition, the reliability of the model was assessed by the ROC curve calculating the AUC area, which was 0.665 at 1 year, 0.662 at 2 years, and 0.660 at 3 years, which illustrates the reliability of using GABRD for assessing the overall survival of patients as shown in Figure 2(e).Figure 2
GABRD gene prognosis model survival analysis and risk curves. (a–c) Prognosis of colorectal cancer patients in the high-risk and low-risk groups. (d) Risk score distribution of colorectal cancer patients in the high-risk (red) and low-risk (green) colorectal cancer prognosis model; scatter plot shows the survival of CRC patients in the model; colorectal patients in the high-risk (blue) and low-risk (red) groups in the model expression of risk genes. (e) ROC curves demonstrate the predictive efficiency of risk scores.
(a)(b)(c)(d)(e)


## 3.3. GABRD Nomogram Construction

According toGABRD expression, age, gender, and TNM stage, univariate cox analysis was first used to select factors associated with prognosis. The results of univariate cox analysis showed that GABRD, age, and TNM stage were prognostic factors as shown in Figure 3(a). Multivariate results showed that GABRD, age, and TNM stage were independent prognostic factors as shown in Figure 3(b). Since tumor stage and GABRD gene signature were shown to be independent prognostic factors for CRC, nomograms containing the tumor stage and risk groups were constructed to predict OS at 1, 2, and 3 years. The survival model C-index was 0.724 (0.644–1) (Figure 3(c), and the calibration curves at 1, 2, and 3 years were located in the diagonal complex line, indicating that the model was reliable, as shown in Figure 3(d).Figure 3
Construction of column line graphs to assess overall patient survival. (a) Single-factor Cox analysis of prognostic correlates of GABRD. (b) Multifactor Cox analysis of prognostic correlates of GABRD. (c) Survival column line graph prediction model. (d) Calibration curves to predict survival at years 1, 2, and 3 in patients with colorectal cancer.
(a)(b)(c)(d)


## 3.4. Potential Mechanism of Action of GABRD Gene in CRC

Based on the false discovery rate less than 0.05 and the absolute value of logFC greater than 1 (2-fold change), 357 differentially expressed genes were identified fromGABRD high and low expression; 23 differentially expressed genes were identified from low expression (Figure 4). The results of GO functional annotation and KEGG pathway enrichment analysis showed that these differentially expressed genes were enriched in protein digestion and absorption, ECM-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, antimicrobial humoral response, and other known cancer-related biological processes and pathways, as shown in (Figure 5).Figure 4
CRC tissue differentially expressed gene heat map and volcano map. (a) Use Foldchange and correctedP value to draw a volcano map. The red dot in the diagram indicates the upregulated genes with significant difference, the blue dot indicates the downregulated genes with significant difference, and the gray dot indicates the genes that are not significant. (b) Differential gene expression heat map, in which different colors represent the expression trend in different tissues.
(a)(b)Figure 5
CRC differential gene GO and KEGG enrichment analysis. (a) Differential upregulated gene KEGG pathway enrichment results. (b) Differential downregulated gene KEGG pathway enrichment results. (c). Differential upregulated gene GO term enrichment results. (d) Differential downregulated gene GO term enrichment results. The different colors represent the significance of differential enrichment results. The larger value represents the smaller fdr value. The circle size represents the number of enriched genes, and the more the number, the larger the circle.
(a)(b)(c)(d)


## 3.5. Role of GABRD Expression and RNA Chemical Modification in CRC

Since there is a lack of studies on the functions played by GABRD in tumors, this study attempted to investigate the gene functions of GABRD, starting from the functional analysis of the associated genes of this gene. Chemical modification is an efficient way to regulate the structure and function of macromolecules, such as DNA, RNA, proteins, sugars, and lipids, which require postsynthesis and covalent modification to function in living organisms. High-throughput sequencing technology has shown that RNA modification plays a critical role in the selective expression of genes [16]. We found positive correlations between ACC, KIRC, THCA, COAD, COADREAD, OV, PAAD, GBM, and other tumors in m1A-, m5C-, and m6A-related genes by the expression of GABRD genes and 44 marker genes of three types of RNA modifications (m1A (10), m5C (13), m6A (21)) genes in pan-cancer, as shown in Figure 6 with a high correlation, especially higher in COAD and COADREAD tumors (P<0.05).Figure 6
Pan-cancer-related expression of GABRD in m1A-, m5C-, and m6A-related genes.


## 3.6. Correlation of GABRD Gene Expression with Signature Genes of Different Immune Cell Subpopulations

From Figure7(a), we know that the infiltration levels in TCGA-COAD (N = 282) and TCGA-COADREAD (N = 373) in B cell, T cell CD4, T cell CD8, neutrophil, macrophage, and DC were significantly positively correlated (P<0.05). Also, the Pearson correlation coefficient was the largest. The results indicated that GABRD expression was associated with high immune infiltration in TCGA-COAD and TCGA-COADREAD cancer types. These findings strongly suggest that GABRD affects patient survival by interacting with immune infiltration in cancers such as TCGA-COAD and TCGA-COADREAD. As shown in Figure 7(b)–7(d), for colon adenocarcinoma (COAD), GABRD expression levels were highly positively correlated with ImmuneScore (P=4.2e−3, r = 0.15), StromalScore (P=1.4e−7, r = 0.31), and ESTIMATEScore (P=4.5e−5, r = 0.24).Figure 7
Relationship between GABRD expression and immune scores. (a) Correlation analysis of GABRD gene in TCGA-COAD and TCGA-COADREAD immune infiltration. (b–d) Correlation expression of GABRD gene in different immune scores.
(a)(b)(c)(d)


## 4. Discussion

The occurrence and development of cancer involves multiple genes and stages [17]. Cellular malignancy is firstly altered at the molecular level and therefore needs to be corrected and treated at the molecular level of the patient’s organism [18] in order to eradicate the tumor effectively and rapidly. It is currently believed that the development of CRC involves various factors such as lifestyle, dietary habits, intestinal microflora, and genetics [19–21]. Late stage CRC has a low overall survival rate because it is prone to metastatic recurrence.As medical research enters the era of big data and the development of multiomics technologies, bioinformatics analysis based on expression profiling microarrays and transcriptome sequencing is increasingly used in the investigation of cancer development mechanisms, diagnosis, and staging [22]. The GEO database, a comprehensive gene expression database in the United States, and The Cancer Genome Atlas (TCGA) database are both commonly used for bioinformatics analysis, and the latter contains more comprehensive cancer gene expression profiles, mutation gene profiles, and related clinical information. The latter contains more comprehensive cancer gene expression profiles, mutation gene profiles, and related clinical information, and is the largest database of cancer genetic information available [23].In recent years, the rapid development of bioinformatics has become an important research tool in the field of oncology research, and bioinformatics analysis of colorectal cancer has been published in related literature [24, 25]. In this study, we systematically analyzed the clinical information data related to 33 tumors of TCGA and found that GABRD was highly expressed in TCGA-COAD, TCGA-COADREAD with poor prognosis, and low expressed in four tumor types TCGA-KIPAN, TCGA-KIRC, TCGA-LIHC, and TCGA-ACC with poor prognosis. The GABRD single gene prognostic model was constructed, indicating that GABRD is a protective factor for prognosis in CRC. The multifactorial results of the constructed survival line graph showed that GABRD, age, and TNM staging were independent prognostic factors, suggesting that GABRD may play an important role in the development and progression of CRC compared with other tumor types.In addition, colorectal cancer develops from multiple chronic processes from normal epithelium to adenoma and adenocarcinoma and eventually metastasis [26], timely diagnosis and removal of colorectal adenoma before it develops into adenocarcinoma is an important way to prevent colorectal cancer, and this study found that GABRD expression was significantly elevated in colorectal adenoma, suggesting that GABRD can be used as a biomarker for early diagnosis of colorectal cancer. How GABRD is involved in the development and progression of CRC is still poorly understood. To further investigate the biological functions of GABRD in CRC, GO and KEGG functional enrichment analyses were performed on the differentially expressed genes according to the expression of GABRD. The results showed that GABRD was mainly enriched for protein digestion and absorption, ECM-receptor interaction, extracellular structure organization, extracellular matrix organization, pancreatic secretion, antimicrobial humoral response, and so on.Tumors are not only composed of malignant cells but are also embedded in a complex microenvironment in which dynamic interactions are established [27]. Notably, this tumor microenvironment (TME) includes a large number of immune cells. Knowledge of the immune cell content in cancer samples is invaluable for the discovery of cancer immunotherapeutic agents and for clinical decisions on treatment options. Another important part of this study is the discovery of the correlation between GABRD expression and the infiltration of multiple immune cells in colorectal cancer. Our analysis revealed that GABRD expression in CRC was significantly and positively correlated with the infiltration levels of B cell, T cell CD4, T cell CD8, neutrophil, macrophage, and DC. To further investigate the correlation between GABRD expression and immune scores, we found that GABRD expression was positively correlated with ImmuneScore, StromalScore, and ESTIMATEScore, indicating that GABRD genes are involved in expression in the tumor immune microenvironment.This study is based on the bioinformatics analysis of domestic and international public databases of tumors, with relatively large sample size and high credibility, and has certain reference value. However, this study also has some limitations. First, we only used the data from TCGA-LGG and did not use the validation dataset. Second, the findings of this study are limited toGABRD mRNA expression and further validation is needed to combine GABRD protein expression with experiments. Finally, the mechanism by which GABRD mediates tumor immunity has not been fully evaluated. More clarification and underlying data are needed to better assess the potential relationship between GABRD and CRC.To sum up, this study confirmed thatGABRD is an independent prognostic indicator for patients with colorectal cancer, while the high expression of GABRD indicates a poor prognosis; the expression of GABRD is closely related to the immune cell infiltration of colorectal cancer. Although it has not been verified by in vivo experiments, the results of this study provide a reference basis for further study of the biological function and mechanism of GABRD in colorectal cancer.



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*Source: 6198448-2022-06-21.xml*