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Objective:To screen the differentially expressed genes (DEG) related to inflammatory response associated with the prognosis of colon cancer based on the bioinformatics approach, and to construct and validate a prognostic model for colon cancer.Methods:RNA sequencing and clinical data of 472 colon cancer patients and normal colon tissues of 41 healthy people were retrieved from the Cancer Genome Atlas (TCGA) database. Gene expression related to prognosis of colon cancer and clinical data were retrieved from the International Cancer Genome Consortium (ICGC) database. The retrieval time was all from the establishment of library to November 2022. A total of 200 genes associated with inflammatory response obtained from the Gene Set Enrichment Analysis (GSEA) database were compared with the RNA sequencing gene dataset of colon cancer and normal colon tissues obtained from the TCGA database, and then DEG associated with inflammatory response were obtained. The prognosis-related DEG in the TCGA database were analyzed by using Cox proportional risk model, and the inflammatory response-related DEG were intersected with the prognosis-related DEG to obtain the prognosis-related inflammatory response-related DEG. The prognostic model of colon cancer was constructed by using LASSO Cox regression. Risk scores were calculated, and colon cancer patients in the TCGA database were divided into two groups of low risk (< the median value) and high risk (≥the median value) according to the median value of risk scores. Principal component analysis (PCA) was performed on patients in both groups, and survival analysis was performed by using Kaplan-Meier method. The efficacy of risk score in predicting the overall survival (OS) of colon cancer patients in the TCGA database was analyzed based on the R software timeROC program package. Clinical data from the ICGC database were applied to externally validate the constructed prognostic model, and patients with colon cancer in the ICGC database were classified into high and low risk groups based on the median risk score of patients with colon cancer in the TCGA database. By using R software, single-sample gene set enrichment analysis (ssGESA), immunophenotyping difference analysis, immune microenvironment correlation analysis, and immune checkpoint gene difference analysis of immune cells and immune function were performed for prognosis-related inflammation response-related DEG in the TCGA database.Results:A total of 60 inflammatory response-related DEG and 12 prognosis-related DEG were obtained; and 6 prognosis-related inflammatory response-related DEG (CCL24, GP1BA, SLC4A4, SRI, SPHK1, TIMP1) were obtained by taking the intersection set. LASSO Cox regression analysis showed that a prognostic model for colon cancer was constructed based on 6 prognosis-related inflammatory response-related DEG, and the risk score was calculated as = -0.113×CCL24+0.568×GP1BA+ (-0.375)×SLC4A4+(-0.051)×SRI+0.287×SPHK1+0.345×TIMP1. PCA results showed that patients with colon cancer could be better classified into 2 clusters. The OS in the high-risk group was worse than that in the low-risk group in the TCGA database ( P < 0.001); the area of the curve (AUC) of the prognostic risk score for predicting the OS rates of 1-year, 3-year, 5-year was 0.701, 0.685, and 0.675, respectively. The OS of the low-risk group was better than that of the high-risk group in the ICGC database; AUC of the prognostic risk score for predicting the OS rates of 1-year, 2-year, 3-year was 0.760, 0.788, and 0.743, respectively. ssGSEA analysis showed that the level of immune cell infiltration in the high-risk group in the TCGA database was high, especially the scores of activated dendritic cells, macrophages, neutrophils, plasmacytoid dendritic cells, T helper cells, and follicular helper T cells in the high-risk group were higher than those in the low-risk group, while the score of helper T cells 2 (Th2) in the high-risk group was lower compared with that in the low-risk group (all P < 0.05); in terms of immune function, the high-risk group had higher scores of antigen-presenting cell (APC) co-inhibition, APC co-stimulation, immune checkpoint, human leukocyte antigen (HLA), promotion of inflammation, parainflammation, T-cell stimulation, type Ⅰ interferon (IFN) response, and type ⅡIFN response scores compared with those in the low-risk group (all P < 0.05). The results of immunophenotyping analysis showed that IFN-γ-dominant type (C2) had the highest inflammatory response score, and the differences were statistically significant when compared with trauma healing type (C1) and inflammatory response type (C3), respectively (all P < 0.05). Immune microenvironment stromal cells and immune cells were all positively correlated with prognostic risk scores ( r values were 0.35 and 0.21, respectively, both P < 0.01). The results of immune checkpoint difference analysis showed there was a statistically significant difference in programmed-death receptor ligand 1 (PD-L1) expression level between high-risk group and low-risk group ( P = 0.002), and PD-L1 expression level was positively correlated with prognostic risk score ( r = 0.23, P < 0.01). Conclusions:Inflammatory response-related genes may play an important role in tumor immunity of colon cancer and can be used in the prognostic analysis and immunotherapy of colon cancer patients.
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Objective:To explore the value of prognostic model based on ferroptosis-related long non-coding RNA (lncRNA) in predicting the prognosis of patients with colon cancer.Methods:Ferroptosis-related genes were downloaded from FerrDb database, and the RNA sequencing gene data and clinical data of colon cancer patients from the establishment of the database to November 2021 were downloaded from the Cancer Genome Atlas (TCGA) database. Through R3.6.3 software, the colon cancer gene expression data obtained from TCGA database and ferroptosis-related genes obtained from FerreDb database were analyzed to obtain differentially expressed ferroptosis-related genes in colon cancer and normal tissues. The expression correlation between ferroptosis-related genes and lncRNA in colon cancer was calculated by using R3.6.3 software to determine ferroptosis-related lncRNA in colon cancer. The survival-related differentially expressed ferroptosis-related lncRNA was screened and included in the multivariate Cox proportional hazards model to construct a colon cancer prognosis model; and the risk score of colon cancer patients was calculated by the prognostic model according to the lncRNA expression. According to the median risk score, the clinical cases collected from TCGA database were divided into high-risk group and low-risk group with 223 cases in each group. Kaplan-Meier survival analysis was performed for the two groups. The receiver operating characteristic (ROC) curve was used to analyze the effect of prognostic model risk score and clinical characteristics on predicting the survival of all patients. GSEA 4.1.0 software was used for gene set enrichment analysis (GSEA) of lncRNA in high-risk and low-risk groups, and ggpubr package of R3.6.3 software was used for single sample GSEA (ssGSEA) of immune cells and immune function of differentially expressed lncRNA between high-risk and low-risk groups.Results:According to the intersection of ferroptosis-related genes and differentially expressed genes obtained from databases, 65 differentially expressed ferroptosis-related genes were obtained, and 24 lncRNA related to the prognosis of colon cancer were analyzed, and then prognostic model was constructed based on lncRNA. Kaplan-Meier survival analysis showed that the survival of low-risk group was better than that of high-risk group ( P < 0.001); ROC curve analysis showed that the area under the curve (AUC) of 1-, 2-, 3-year survival predicted by the prognostic model risk score was more than 0.75, and the AUC of 1-year survival predicted by the risk score for all patients was greater than age, gender, the National Comprehensive Cancer Network (NCCN), T staging, N staging and M staging. GSEA showed that differentially expressed lncRNA in high-risk and low-risk groups concentrated in tumor and immune-related pathways; ssGSEA showed that there were differences in T cells, macrophages, mast cells, neutrophils, immune stimulation, human leukocyte antigen, type Ⅰ and type Ⅱ interferon response between high-risk group and low-risk group (all P < 0.05), and the expression levels of CD200 and TNFRSF14 at the immune checkpoint were significantly different (both P < 0.01). Conclusions:Ferroptosis-related lncRNA may play an important role in tumor immunity of colon cancer, and it can be used for the prognosis analysis of patients with colon cancer.
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Objective:To explore the characteristics of pyroptosis-related genes in colon cancer cells screened by bioinformatics, and to verify the constructed prognostic model of colon cancer based on differentially expressed pyroptosis-related genes.Methods:Genetic data of RNA sequencing and clinical data of colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. Fifty-two genes associated with pyroptosis were identified by searching the literature and compared with the RNA sequencing gene dataset of colon cancer and normal colon tissues obtained from TCGA database to obtain differentially expressed pyroptosis-related genes in clinical samples. The protein interaction network of differentially expressed pyroptosis-related genes was analyzed by using STRING website and R software. Based on the differential expression of pyroptosis-related genes in clinical samples of TCGA database, colon cancer patients in TCGA database were divided into pyroptosis and non-pyroptosis groups, and genes with significant differential expression between the two groups were screened at P < 0.05 according to gene expression; based on these differentially expressed genes, LASSO Cox regression was used to construct a prognostic model of colon cancer associated with pyroptosis. Patients collected from TCGA database were divided into high risk (≥ median value) and low risk (< median value) groups according to the median value of risk scores calculated by the model, and the overall survival of the two groups was analyzed by Kaplan-Meier survival function. The time ROC package of R software was used to analyze the efficacy of applying risk scores to predict the different survival time of colon cancer patients in TCGA database. Multivariate Cox regression was used to analyze the effects of clinicopathological factors and risk scores calculated by the model on the survival of patients in TCGA database. R software was used to analyze and obtain the differential genes between high and low risk groups of colon cancer patients in TCGA database. R software was used to conduct Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and single sample gene set enrichment analysis of immune cells and immune function (ssGESA) for differentially expressed pyroptosis-related genes. Results:Thirty-eight differentially expressed pyroptosis-related genes between colon cancer tissues and normal tissues of clinical samples were obtained based on data of TCGA database. A prognostic model consisting of 13 pyroptosis-related genes was established by applying LASSO Cox regression, the risk score = 0.118×MID2+0.354×IL20RB+0.083×HOXC11+0.011×TMEM88+0.021×SYNGR3+0.246×UPK3B+0.030×EGFL7+0.109×TMPRSS11E+0.138×IFITM10+0.161×RNF207+0.097×LINGO1+0.202×HEYL+0.025×ROBO3. Survival analysis showed that TCGA database had worse overall survival in the high-risk group than in the low-risk group ( P < 0.001). Receiver operating characteristic (ROC) curve analysis showed that the area under the curve of the prognostic model risk score in predicting the survival of colon cancer patients in TCGA database at 1, 3 and 5 years was all > 0.7. Multivariate Cox regression analysis showed that risk score was an independent influencing factor for survival of colon cancer patients in TCGA database (high risk vs. low risk HR = 3.988, 95% CI 2.865-5.551, P < 0.001). GO and KEGG enrichment analysis showed that the differentially expressed genes between high and low risk groups (SULF1, FBLN2, COL1A1, DES, SFRP2, FNDC1, MYH11, APOE, C3, SPP1, COL1A2, COL10A1, THBS2, AEBP1, CNN1, IGHG1, and SFRP4) were upregulated in the high risk group, which were mainly associated with cellular matrix structural components and extracellular matrix (ECM) receptor interactions. ssGSEA analysis showed that the level of immune cell infiltration was higher in high risk group, especially B cells, macrophages, mast cells, helper T cells, and tumor-infiltrating lymphocytes were higher than those in low risk group; for immune function, chemokine receptors, immune checkpoints, human leukocyte antigens, parainflammation, T cell suppression, T-cell stimulation, and type Ⅱ interferon response in high risk group were higher than those in low risk group. Conclusions:The constructed prognostic model of colon cancer based on pyroptosis-related genes is valuable for predicting the prognosis of colon cancer patients. Pyroptosis-related genes may play an important role in tumor immunity of colon cancer and can be used for prognostic analysis of colon cancer patients.