Prediction and identification of immune genes related to the prognosis of patients with colon adenocarcinoma and its mechanisms.
ABSTRACT: BACKGROUND:Colon adenocarcinoma (COAD) is a gastrointestinal tumor with a high degree of malignancy. Its deterioration process is closely related to the tumor microenvironment, and transcription factors (TF) play a regulatory role in this process. Currently, there is a lack of exploration between the genes related to the COAD tumor microenvironment and the survival prognosis of patients. Models composed of multiple genes usually predict the survival prognosis of patients more accurately than single genes. We can analyze the multigene models that can predict the prognosis of COAD from the current database. METHODS:The limma package of the R programming language is used for gene differential expression analysis. Kaplan-Meier curve is used to analyze the relationship between the patient risk score model and survival data. The hazard model is used to analyze the relationship between the risk score and the clinical data of COAD patients. The information of immune genes and immune cells is obtained from IMMPORT database and TIMER database. Receiver operating characteristic (ROC) curve is used to judge the stability of the model. RESULTS:We found 7 immune genes, which can built a risk score model to predict the survival prognosis of COAD. According to univariate and multivariate analysis, the risk score can be used as an independent predictor. The content of some immune microenvironment cells will also increase as the risk score increases. CONCLUSIONS:We found 7 immune genes, such as SLC10A2 (solute carrier family 10 member 2), CXCL3 (C-X-C motif chemokine ligand 3), IGHV5-51 (immunoglobulin heavy variable 5-51), INHBA (inhibin subunit beta A), STC1 (stanniocalcin 1), UCN (urocortin), and OXTR (oxytocin receptor), can constitute a model for predicting the prognosis of COAD. They may provide potential therapeutic targets for clinical treatment of COAD.
Project description:BACKGROUND:Colon cancer is the most common type of gastrointestinal cancer and has high morbidity and mortality. Colon adenocarcinoma (COAD) is the main pathological type of colon cancer, and much evidence has supported the correlation between the prognosis of COAD and the immune system. The current study aimed to develop a robust prognostic immune-related gene pair (IRGP) model to estimate the overall survival of patients with COAD. METHODS:The gene expression profiles and clinical information of patients with colon adenocarcinoma were obtained from the TCGA and GEO databases and were divided into training and validation cohorts. Immune genes were selected that showed a significant association with prognosis. RESULTS:Among 1647 immune genes, a model with 17 IRGPs was built that was significantly associated with OS in the training cohort. In the training and validation datasets, the IRGP model divided patients into the high-risk group and low-risk group, and the prognosis of the high-risk group was significantly worse (P<0.001). Univariate and multivariate Cox proportional hazard analyses confirmed the feasibility of this model. Functional analysis confirmed that multiple tumor progression and stem cell growth-related pathways were upregulated in the high-risk groups. Regulatory T cells and macrophages M0 were significantly highly expressed in the high-risk group. CONCLUSION:We successfully constructed an IRGP model that can predict the prognosis of COAD, providing new insights into the treatment strategy of COAD.
Project description:Background:Colon adenocarcinoma (COAD) is one of the most commonly diagnosed cancers, and it is closely related to the immune microenvironment. Considering that immunotherapy is not effective for all COAD patients, it is necessary to identify the effective population before administering treatment. In this study, we established an independent prognostic index based on immune-related genes (IRGs), in order to evaluate the clinical outcome of COAD. Methods:The gene expression profiles and IRGs taken from The Cancer Genome Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort), respectively, were integrated in order to identify the differentially expressed IRGs. Functional enrichment analysis was conducted and the prognostic value of survival-related IRGs was determined. Based on Cox regression analysis, the IRG-based prognostic index (IRGPI) was established, and the model was evaluated and applied. Results:A total of 51 differentially expressed survival-related IRGs were identified. The most significant signaling pathway was "cytokine-cytokine receptor interaction". The index established herein was based on 12 survival-related IRGs, and it was highly accurate in monitoring prognosis. Moreover, the IRGPI was significantly correlated with multiple clinicopathologic factors, as well as with the infiltration of immune cells. Conclusions:An independent IRGPI was established in order to assess the immune status and tumor prognosis in COAD patients. This index can serve as a robust biomarker in clinical prognosis applications, including cancer immunotherapy.
Project description:Colon adenocarcinoma (COAD) is a common type of colon cancer, and post-operative recurrence and metastasis may occur in COAD patients. This study is designed to build a risk score system for COAD patients. The Cancer Genome Atlas (TCGA) dataset of COAD (the training set) was downloaded, and GSE17538 and GSE39582 (the validation sets) from Gene Expression Omnibus database were obtained. The differentially expressed RNAs (DERs) were analyzed by limma package. Using survival package, the independent prognosis-associated long non-coding RNAs (lncRNAs) were selected for constructing risk score system. After the independent clinical prognostic factors were screened out using survival package, a nomogram survival model was constructed using rms package. Furthermore, competitive endogenous RNA (ceRNA) regulatory network and enrichment analyses separately were performed using Cytoscape software and DAVID tool. Totally 404 DERs between recurrence and non-recurrence groups were identified. Based on the six independent prognosis-associated lncRNAs (including H19, KCNJ2-AS1, LINC00899, LINC01503, PRKAG2-AS1, and SRRM2-AS1), the risk score system was constructed. After the independent clinical prognostic factors (Pathologic M, pathologic T, and RS model status) were identified, the nomogram survival model was built. In the ceRNA regulatory network, there were three lncRNAs, four miRNAs, and 77 mRNAs. Additionally, PPAR signaling pathway and hedgehog signaling pathway were enriched for the mRNAs in the ceRNA regulatory network. The risk score system and the nomogram survival model might be used for predicting COAD recurrence. Besides, PPAR signaling pathway and hedgehog signaling pathway might affect the recurrence of COAD patients.
Project description:Background: Immune-related genes (IRGs) are critically involved in the tumor microenvironment (TME) of colon adenocarcinoma (COAD). Here, the study was mainly designed to establish a prognostic model of IRGs to predict the survival of COAD patients. Methods: The Cancer Genome Atlas (TCGA), Immunology Database and Analysis Portal (ImmPort) database, and Cistrome database were utilized for extracting data regarding the expression of immune gene- and tumor-related transcription factors (TFs), aimed at the identification of differentially expressed genes (DEGs), differentially expressed IRGs (DEIRGs), and differentially expressed TFs (DETFs). Univariate Cox regression analysis was subsequently performed for the acquisition of prognosis-related IRGs, followed by establishment of TF regulatory network for uncovering the possible molecular regulatory association in COAD. Subsequently, multivariate Cox regression analysis was conducted to further determine the role of prognosis-related IRGs for prognostic prediction in COAD. Finally, the feasibility of a prognostic model with immunocytes was explored by immunocyte infiltration analysis. Results: A total of 2450 DEGs, 8 DETFs, and 79 DEIRGs were extracted from the corresponding databases. Univariate Cox regression analysis revealed 11 prognosis-related IRGs, followed by establishment of a regulatory network on prognosis-related IRGs at transcriptional levels. Functionally, IRG GLP2R was negatively modulated by TF MYH11, whereas IRG TDGF1 was positively modulated by TF TFAP2A. Multivariate Cox regression analysis was subsequently performed to establish a prognostic model on the basis of seven prognosis-related IRGs (GLP2R, ESM1, TDGF1, SLC10A2, INHBA, STC2, and CXCL1). Moreover, correlation analysis of immunocyte infiltration also revealed that the seven-IRG prognostic model was positively associated with five types of immunocytes (dendritic cell, macrophage, CD4 T cell, CD8 T cell, and neutrophil), which may directly reflect tumor immune state in COAD. Conclusions: Our present findings indicate that the prognostic model based on prognosis-related IRGs plays a crucial role in the clinical supervision and prognostic prediction of COAD patients at both molecular and cellular levels.
Project description:Objective:We aimed at identifying the key genes of prognostic value in clear cell renal cell carcinoma (ccRCC) microenvironment and construct a risk score prognostic model. Materials and Methods:Immune and stromal scores were calculated using the ESTIMATE algorithm. A total of 539 ccRCC cases were divided into high- and low-score groups. The differentially expressed genes in immune and stromal cells for the prognosis of ccRCC were screened. The relationship between survival outcome and gene expression was evaluated using univariate and multivariate Cox proportional hazard regression analyses. A risk score prognostic model was constructed based on the immune/stromal scores. Results:The median survival time of the low immune score group was longer than that of the high immune score group (p = 0.044). Ten tumor microenvironment-related genes were selected by screening, and a predictive model was established, based on which patients were divided into high- and low-risk groups with markedly different overall survival (p < 0.0001). Multivariate Cox analyses showed that the risk score prognostic model was independently associated with overall survival, with a hazard ratio of 1.0437 (confidence interval: 1.0237-1.0641, p < 0.0001). Conclusions:Low immune scores were associated with extended survival time compared to high immune scores. The novel risk predictive model based on tumor microenvironment-related genes may be an independent prognostic biomarker in ccRCC.
Project description:Background:Colon adenocarcinoma (COAD) is a malignant and lethal tumor in digestive system and distance metastasis lead to poor prognosis. The metastasis-specific ceRNAs (competitive endogenous RNAs) and tumor-infiltrating immune cells might associate with tumor prognosis and distance metastasis. Nonetheless, few studies have concentrated on ceRNAs and Immune cells in COAD. Methods:The gene expression profile and clinical information of COAD were downloaded from TCGA and divided into two groups: primary tumors with or without distance metastasis. We applied comprehensive bioinformatics methods to analyze differential expression genes (DEGs) related to metastasis and establish the ceRNA networks. The Cox analysis and Lasso regression were utilized to screen the pivotal genes and prevent overfitting. Based on them, the prognosis prediction nomograms were established. The cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was then applied to screen significant tumor immune-infiltrating cells associated with COAD metastasis and established another prognosis prediction model. Ultimately, co-expression analysis was applied to explore the relationship between key genes in ceRNA networks and significant immune cells. Multiple databases and preliminary clinical specimen validation were used to test the expressions of key biomarkers at the cellular and tissue levels. Results:We explored 1 significantly differentially expressed lncRNA, 1 significantly differentially expressed miRNA, 8 survival-related immune-infiltrating cells, 5 immune cells associated with distance metastasis. Besides, 3 pairs of important biomarkers associated with COAD metastasis were also identified: T cells follicular helper and hsa-miR-125b-5p (R = -0.200, P < 0.001), Macrophages M0 and hsa-miR-125b-5p (R = 0.170, P < 0.001) and Macrophages M0 and FAS (R = -0.370, P < 0.001). Multidimensional validation and preliminary clinical specimen validation also supported the results. Conclusion:In this research, we found some significant ceRNAs (FAS and hsa-miR-125b-5p) and tumor-infiltrating immune cells (T cells follicular helper and Macrophages M0) might related to distance metastasis and prognosis of COAD. The nomograms could assist scientific and medical researchers in clinical management.
Project description:Growing evidence has shown that long non-coding RNAs (lncRNAs) can serve as prospective markers for survival in patients with colorectal adenocarcinoma. However, most studies have explored a limited number of lncRNAs in a small number of cases. The objective of this study is to identify a panel of lncRNA signature that could evaluate the prognosis in colorectal adenocarcinoma based on the data from The Cancer Genome Atlas (TCGA). Altogether, 371 colon adenocarcinoma (COAD) patients with complete clinical data were included in our study as the test cohort. A total of 578 differentially expressed lncRNAs (DELs) were observed, among which 20 lncRNAs closely related to overall survival (OS) in COAD patients were identified using a Cox proportional regression model. A risk score formula was developed to assess the prognostic value of the lncRNA signature in COAD with four lncRNAs (LINC01555, RP11-610P16.1, RP11-108K3.1 and LINC01207), which were identified to possess the most remarkable correlation with OS in COAD patients. COAD patients with a high-risk score had poorer OS than those with a low-risk score. The multivariate Cox regression analyses confirmed that the four-lncRNA signature could function as an independent prognostic indicator for COAD patients, which was largely mirrored in the validating cohort with rectal adenocarcinoma (READ) containing 158 cases. In addition, the correlative genes of LINC01555 and LINC01207 were enriched in the cAMP signaling and mucin type O-Glycan biosynthesis pathways. With further validation in the future, our study indicates that the four-lncRNA signature could serve as an independent biomarker for survival of colorectal adenocarcinoma.
Project description:Objective:A survival risk assessment model associated with a lung adenocarcinoma (LUAD) microenvironment was established and evaluated to identify effective independent prognostic factors for LUAD. Methods:The public data were downloaded from the TCGA database, and ESTIMATE prediction software was used to score immune cells and stromal cells for tumor purity prediction. The samples were divided into the high-score group and the low-score group by the median value of the immune score (or stromal score). The Wilcoxon test was used for differential analysis. GO and KEGG enrichment analysis of differentially expressed genes (DEGs) was performed using "clusterProfiler" of R package. Meanwhile, univariate and multivariate regression analysis was performed on DEGs to construct a multivariate Cox risk regression model with variable gene expression levels as independent prognostic factors affecting a tumor microenvironment (TME) and tumor immunity. Results:This study found that LUAD patients with high immune cell (stromal cell) infiltration had better prognosis and were in earlier staging. Functional enrichment analysis revealed that most DEGs were related to the proliferation and activation of immune cells or stromal cells. A survival prediction model composed of 6 TME-related genes (CLEC17A, TAGAP, ABCC8, BCAN, FLT3, and CCR2) was established, and finally, the 6 feature genes closely related to the prognosis of LUAD were proved. The AUC value of the ROC curve in this model was 0.7, indicating that the model was reliable. Conclusion:Six genes related to the LUAD microenvironment have a predictive prognostic value in LUAD.
Project description:A robust and accurate gene expression signature is essential to assist oncologists to determine which subset of patients at similar Tumor-Lymph Node-Metastasis (TNM) stage has high recurrence risk and could benefit from adjuvant therapies. Here we applied a two-step supervised machine-learning method and established a 12-gene expression signature to precisely predict colon adenocarcinoma (COAD) prognosis by using COAD RNA-seq transcriptome data from The Cancer Genome Atlas (TCGA). The predictive performance of the 12-gene signature was validated with two independent gene expression microarray datasets: GSE39582 includes 566 COAD cases for the development of six molecular subtypes with distinct clinical, molecular and survival characteristics; GSE17538 is a dataset containing 232 colon cancer patients for the generation of a metastasis gene expression profile to predict recurrence and death in COAD patients. The signature could effectively separate the poor prognosis patients from good prognosis group (disease specific survival (DSS): Kaplan Meier (KM) Log Rank p = 0.0034; overall survival (OS): KM Log Rank p = 0.0336) in GSE17538. For patients with proficient mismatch repair system (pMMR) in GSE39582, the signature could also effectively distinguish high risk group from low risk group (OS: KM Log Rank p = 0.005; Relapse free survival (RFS): KM Log Rank p = 0.022). Interestingly, advanced stage patients were significantly enriched in high 12-gene score group (Fisher's exact test p = 0.0003). After stage stratification, the signature could still distinguish poor prognosis patients in GSE17538 from good prognosis within stage II (Log Rank p = 0.01) and stage II & III (Log Rank p = 0.017) in the outcome of DFS. Within stage III or II/III pMMR patients treated with Adjuvant Chemotherapies (ACT) and patients with higher 12-gene score showed poorer prognosis (III, OS: KM Log Rank p = 0.046; III & II, OS: KM Log Rank p = 0.041). Among stage II/III pMMR patients with lower 12-gene scores in GSE39582, the subgroup receiving ACT showed significantly longer OS time compared with those who received no ACT (Log Rank p = 0.021), while there is no obvious difference between counterparts among patients with higher 12-gene scores (Log Rank p = 0.12). Besides COAD, our 12-gene signature is multifunctional in several other cancer types including kidney cancer, lung cancer, uveal and skin melanoma, brain cancer, and pancreatic cancer. Functional classification showed that seven of the twelve genes are involved in immune system function and regulation, so our 12-gene signature could potentially be used to guide decisions about adjuvant therapy for patients with stage II/III and pMMR COAD.
Project description:Colorectal cancer (CRC) patients, especially those with deficient mismatch repair (dMMR)/microsatellite instability-high (MSI-H) tumors, whose sensitivity to immune checkpoint inhibitors (ICIs) is significantly higher than that of patients with microsatellite-stable (MSS)/microsatellite instability-low (MSI-L) tumors, have derived clinical benefits from immunotherapy. Most studies have not systematically evaluated the immune characteristics and immune microenvironments of MSI-H and MSS/MSI-L CRCs. We analyzed the relationship between the MSI status and prognosis of ICI treatment in an immunotherapy cohort. We further used mutation data for the immunotherapy and The Cancer Genome Atlas (TCGA)-CRC [colon adenocarcinoma (COAD) + rectum adenocarcinoma (READ)] cohorts. For mRNA expression, mutation data analysis of the immune microenvironment and immunogenicity under different MSI statuses was performed. Compared with CRC patients with MSS/MSI-L tumors, those with MSI-H tumors significantly benefited from ICI treatment. MSI-H CRC had more immune cell infiltration, higher expression of immune-related genes, and higher immunogenicity than MSS/MSI-L CRC. The MANTIS score, which is used to predict the MSI status, was positively correlated with immune cells, immune-related genes, and immunogenicity. In addition, subtype analysis showed that COAD and READ might have different immune microenvironments. MSI-H CRC may have an inflammatory tumor microenvironment and increased sensitivity to ICIs. Unlike those of MSI-H READ, the immune characteristics of MSI-H COAD may be consistent with those of MSI-H CRC.