Dataset Information

Comprehensive exploration of tumor immune microenvironment feature and therapeutic response in colorectal cancer based on a novel immune-related long non-coding RNA prognostic signature.

ABSTRACT: Colorectal cancer (CRC) is one of the most common malignant tumors with a high incidence rate and mortality. LncRNA is an important regulator of the immune system. It is of great significance to study immune-related lncRNAs (IR-lncRNAs) for CRC. In this study, we screened IR-lncRNAs differentially expressed in normal and CRC tissues, and Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator were applied to construct IR-lncRNA prognostic signature in TCGA training dataset, and its predictive capability for the prognosis of CRC patients was verified in GSE39582 validation dataset. The novel signature was identified as an independent predictor of prognosis in CRC patients. In addition, the signature could accurately predict the feature of the immune microenvironment and therapeutic response in CRC patients. The CMap database was adopted to screen for small molecule candidate drugs that can reverse and treat high-risk CRC patients. Finally, the expression of six IR-lncRNAs were verified by qRT-PCR in clinical specimens from our patient cohort. In conclusion, we construct an IR-lncRNA prognostic signature, which is a powerful biomarker of CRC and can accurately predict the prognosis, immune microenvironment feature, and therapeutic response of CRC patients.

SUBMITTER: Zhou X

PROVIDER: S-EPMC9454821 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Comprehensive exploration of tumor immune microenvironment feature and therapeutic response in colorectal cancer based on a novel immune-related long non-coding RNA prognostic signature.

Zhou Xueliang X Aikemu Batuer B Li Shuchun S Shao Yanfei Y Jia Hongtao H Huang Ling L Hong Hiju H Zhang Sen S Tang Qiushi Q Pan Ruijun R Sun Jing J Zheng Minhua M

Frontiers in genetics 20220825

Colorectal cancer (CRC) is one of the most common malignant tumors with a high incidence rate and mortality. LncRNA is an important regulator of the immune system. It is of great significance to study immune-related lncRNAs (IR-lncRNAs) for CRC. In this study, we screened IR-lncRNAs differentially expressed in normal and CRC tissues, and Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator were applied to construct IR-lncRNA prognostic signature in TCGA training data ...[more]

PMID: 36092924

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Project description:BackgroundAccumulating evidence demonstrated that tumor microenvironmental cells played important roles in predicting clinical outcomes and therapeutic efficacy. We aimed to develop a reliable immune-related gene signature for predicting the prognosis of ovarian cancer (OC).MethodsSingle sample gene-set enrichment analysis (ssGSEA) of immune gene-sets was used to quantify the relative abundance of immune cell infiltration and develop high- and low-abundance immune subtypes of 308 OC samples. The presence of infiltrating stromal/immune cells in OC tissues was calculated as an estimate score. We estimated the correlation coefficients among the immune subtype, clinicopathological feature, immune score, distribution of immune cells, and tumor mutation burden (TMB). The differentially expressed immune-related genes between high- and low-abundance immune subtypes were further used to construct a gene signature of a prognostic model in OC with lasso regression analysis.ResultsThe ssGSEA analysis divided OC samples into high- and low-abundance immune subtypes based on the abundance of immune cell infiltration, which was significantly related to the estimate score and clinical characteristics. The distribution of immune cells was also significantly different between high- and low-abundance immune subtypes. The correlation analysis showed the close relationship between TMB and the estimate score. The differentially expressed immune-related genes between high- and low-abundance immune subtypes were enriched in multiple immune-related pathways. Some immune checkpoints (PDL1, PD1, and CTLA-4) were overexpressed in the high-abundance immune subtype. Furthermore, the five-immune-related-gene-signature prognostic model (CCL18, CXCL13, HLA-DOB, HLA-DPB2, and TNFRSF17)-based high-risk and low-risk groups were significantly related to OC overall survival.ConclusionImmune-related genes were the promising predictors of prognosis and survival, and the comprehensive landscape of tumor microenvironmental cells of OC has potential for therapeutic schedule monitoring.

Project description:Purpose: Our research developed immune-related long noncoding RNAs (lncRNAs) for risk stratification in cervical cancer (CC) and explored factors of prognosis, inflammatory microenvironment infiltrates, and chemotherapeutic therapies. Methods: The RNA-seq data and clinical information of CC were collected from the TCGA TARGET GTEx database and the TCGA database. lncRNAs and immune-related signatures were obtained from the GENCODE database and the ImPort database, respectively. We screened out immune-related lncRNA signatures through univariate Cox, LASSO, and multivariate Cox regression methods. We established an immune-related risk model of hub immune-related lncRNAs to evaluate whether the risk score was an independent prognostic predictor. The xCell and CIBERSORTx algorithms were employed to appraise the value of risk scores which are in competition with tumor-infiltrating immune cell abundances. The estimation of tumor immunotherapy response through the TIDE algorithm and prediction of innovative recommended medications on the target to immune-related risk model were also performed on the basis of the IC50 predictor. Results: We successfully established six immune-related lncRNAs (AC006126.4, EGFR-AS1, RP4-647J21.1, LINC00925, EMX2OS, and BZRAP1-AS1) to carry out prognostic prediction of CC. The immune-related risk model was constructed in which we observed that high-risk groups were strongly linked with poor survival outcomes. Risk scores varied with clinicopathological parameters and the tumor stage and were an independent hazard factor that affect prognosis of CC. The xCell algorithm revealed that hub immune-related signatures were relevant to immune cells, especially mast cells, DCs, megakaryocytes, memory B cells, NK cells, and Th1 cells. The CIBERSORTx algorithm revealed an inflammatory microenvironment where naive B cells (p < 0.01), activated dendritic cells (p < 0.05), activated mast cells (p < 0.0001), CD8+ T cells (p < 0.001), and regulatory T cells (p < 0.01) were significantly lower in the high-risk group, while macrophages M0 (p < 0.001), macrophages M2 (p < 0.05), resting mast cells (p < 0.0001), and neutrophils (p < 0.01) were highly conferred. The result of TIDE indicated that the number of immunotherapy responders in the low-risk group (124/137) increased significantly (p = 0.00000022) compared to the high-risk group (94/137), suggesting that the immunotherapy response of CC patients was completely negatively correlated with the risk scores. Last, we compared differential IC50 predictive values in high- and low-risk groups, and 12 compounds were identified as future treatments for CC patients. Conclusion: In this study, six immune-related lncRNAs were suggested to predict the outcome of CC, which is beneficial to the formulation of immunotherapy.

Project description:ObjectiveStromal cells and immune cells have important clinical significance in the microenvironment of colorectal cancer (CRC). This study is aimed at developing a CRC gene signature on the basis of stromal and immune scores.MethodsA cohort of CRC patients (n = 433) were adopted from The Cancer Genome Atlas (TCGA) database. Stromal/immune scores were calculated by the ESTIMATE algorithm. Correlation between prognosis/clinical characteristics and stromal/immune scores was assessed. Differentially expressed stromal and immune genes were identified. Their potential functions were annotated by functional enrichment analysis. Cox regression analysis was used to develop an eight-gene risk score model. Its predictive efficacies for 3 years, 5 years, overall survival (OS), and progression-free survival interval (PFI) were evaluated using time-dependent receiver operating characteristic (ROC) curves. The correlation between the risk score and the infiltering levels of six immune cells was analyzed using TIMER. The risk score was validated using an independent dataset.ResultsImmune score was in a significant association with prognosis and clinical characteristics of CRC. 736 upregulated and two downregulated stromal and immune genes were identified, which were mainly enriched into immune-related biological processes and pathways. An-eight gene prognostic risk score model was conducted, consisting of CCL22, CD36, CPA3, CPT1C, KCNE4, NFATC1, RASGRP2, and SLC2A3. High risk score indicated a poor prognosis of patients. The area under the ROC curves (AUC) s of the model for 3 years, 5 years, OS, and PFI were 0.71, 0.70, 0.73, and 0.66, respectively. Thus, the model possessed well performance for prediction of patients' prognosis, which was confirmed by an external dataset. Moreover, the risk score was significantly correlated with immune cell infiltration.ConclusionOur study conducted an immune-related prognostic risk score model, which could provide novel targets for immunotherapy of CRC.

Project description:BackgroundThe tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC).MethodsTranscriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database.ResultsGene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C-index of 0.769 (95%CI, 0.717-0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen-IRGs signature. The AUC for 1-, 3-, and 5-year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates.ConclusionsIn this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.

Project description:BackgroundGastric cancer (GC) is one of the most common causes of cancer-related death worldwide. As a novel form of programmed cell death, disulfidptosis is characterized by excessive cysteine accumulation, disulfide stress and actin destruction. There is evidence that targeting disulfidptosis is a promising anticancer strategy. Further improvement of GC risk stratification based on disulfidptosis has positive clinical significance.MethodsWe analyzed the expression levels of disulfidptosis-associated genes (DPAGs) in normal and GC tissues and characterized the molecular subtypes of GC patients. Based on the characteristics of DPAG subtypes, differentially expressed prognosis-related genes were selected by LASSO-univariate Cox analysis and multivariate Cox analysis analyzed to establish a prognostic model. Using single-cell sequencing analysis reveals the cell subpopulation for GC. The function of the selected target in GC was verified by in vitro experimental means, including siRNA, qRT-PCR, Western blot, CCK-8, and Transwell assay.ResultsDPAG score was verified to be an independent prognostic factor of GC and was significantly associated with poor prognosis of gastric cancer. Subsequent studies on subgroup immunoinfiltration characteristics, drug sensitivity analysis, immunotherapy response and somatic mutation characteristics of DPAG score comprehensively confirmed the potential guiding significance of DPAG score for individualized treatment of gastric cancer patients. Single-cell sequencing analysis revealed the expression characteristics of DPAG-related prognostic signatures across cell subpopulations. In vitro experiments showed APC11, as one of the selected DPAGs, was highly expressed in gastric cancer, and knockdown of APC11 could significantly inhibit the proliferation and migration of GC cells, demonstrating the reliability of bioinformatics results.ConclusionThe results of this study provide a new perspective for exploring the role of disulfidptosis in the occurrence and development of GC.

Project description:BackgroundGlutathione (GSH) metabolism supports tumor redox balance and drug resistance, while long non-coding RNAs (lncRNAs) influence lung adenocarcinoma (LUAD) progression. This study developed a prognostic model using GSH-related lncRNAs to predict LUAD outcomes and assess tumor immunity.MethodsThis study analyzed survival data from The Cancer Genome Atlas (TCGA) and identified GSH metabolism-related lncRNAs using Pearson correlation. A prognostic model was built with Cox and Least Absolute Shrinkage and Selection Operator (LASSO) methods and validated by Kaplan-Meier analysis, Receiver Operating Characteristic (ROC) curves, and Principal Component Analysis (PCA). Functional analysis revealed immune infiltration and drug sensitivity differences. Quantitative PCR and experimental studies confirmed the role of lnc-AL162632.3 in LUAD.ResultsOur model included a total of nine lncRNAs, namely AL162632.3, AL360270.1, LINC00707, DEPDC1-AS1, GSEC, LINC01711, AL078590.2, AC026355.2, and AL096701.4. The model effectively forecasted patient survival, and the nomogram, incorporating additional clinical risk factors, satisfied clinical needs adequately. Patient stratification based on model scores revealed significant disparities in immune cell composition, functionality, and mutations between groups. Additionally, variations were noted in the IC50 values for key lung cancer medications such as Cisplatin, Docetaxel, and Paclitaxel. In vitro cell experiment results showed that AL162632.3 was markedly upregulated, while AC026355.2 tended to be downregulated across these cell lines. Ultimately, suppressing lnc-AL162632.3 markedly reduced the growth, mobility, and invasiveness of lung cancer cells.ConclusionThis study identified GSH metabolism-related lncRNAs as key prognostic factors in LUAD and developed a model for risk stratification. High-risk patients showed increased tumor mutation burden (TMB) and stemness, emphasizing the potential of personalized immunotherapy to improve survival outcomes.

Project description:Background: Anoikis is a type of apoptosis associated with cell detachment. Resistance to anoikis is a focal point of tumor metastasis. This study aimed to explore the relationship among anoikis-related genes (ARGs), immune infiltration, and prognosis in colorectal cancer (CRC). Methods: The transcriptome profile and clinical data on patients with CRC were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Patients were divided into two clusters based on the expression of ARGs. Differences between the two ARG molecular subtypes were analyzed in terms of prognosis, functional enrichment, gene mutation frequency, and immune cell infiltration. An ARG-related prognostic signature for predicting overall survival in patients with CRC was developed and validated using absolute value convergence and selection operator (LASSO) regression analysis. The correlation between the signature risk score and clinicopathological features, immune cell infiltration, immune typing, and immunotherapy response was analyzed. The risk score combined with clinicopathological characteristics was used to construct a nomogram to assess CRC patients' prognosis. Results: Overall, 151 ARGs were differentially expressed in CRC. Two ARG subtypes, namely, ARG-high and ARG-low groups, were identified and correlated with CRC prognosis. The gene mutation frequency and immune, stromal, and ESTIMATE scores of the ARG-high group were higher than those of the ARG-low group. Moreover, CD8, natural killer cells, M1 macrophages, human leukocyte antigen (HLA), and immune checkpoint-related genes were significantly increased in the ARG-high group. An optimized 25-gene CRC prognostic signature was successfully constructed, and its prognostic predictive ability was validated. The high-risk score was correlated with T, N, M, and TNM stages. Risk scores were negatively correlated with dendritic cells, eosinophils, and CD4 cells, and significantly positively correlated with regulatory T cells. Patients in the high-risk group were more likely to exhibit immune unresponsiveness. Finally, the nomogram model was constructed and showed good prognostic predictive power. Conclusion: ARGs are associated with clinicopathological features and the prognosis of CRC, and play important roles in the immune microenvironment. Herein, we underpinned the usefulness of ARGs in CRC to develop more effective immunotherapy techniques.

Dataset Information

Comprehensive exploration of tumor immune microenvironment feature and therapeutic response in colorectal cancer based on a novel immune-related long non-coding RNA prognostic signature.

Publications

Comprehensive exploration of tumor immune microenvironment feature and therapeutic response in colorectal cancer based on a novel immune-related long non-coding RNA prognostic signature.

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