Identification of an m6A-Related Long Noncoding RNA Risk Model for Predicting Prognosis and Directing Treatments in Patients With Colon Adenocarcinoma
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ABSTRACT:
SUBMITTER: Liao W
PROVIDER: S-EPMC9326028 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: Long noncoding RNAs (lncRNAs) crucially modulate DNA damage responses/repair in cancer cells. However, the underlying regulatory role of genome integrity and its clinical value in colon adenocarcinoma (COAD) remains unclear. This study links genome instability to lncRNA using computational biology techniques, in attempt to propose novel biomarkers of immunotherapy outcome, and investigated a potential competing endogenous RNA (ceRNA) as a molecular regulatory mechanism. Methods: TCGA-COAD patients were divided into genome unstable (GU)-like and genome stable (GS)-like clusters via hierarchical clustering to predict immunotherapy outcomes. Multivariate Cox model was established to predict the overall survival rate in COAD patients. Additionally, SVM and LASSO algorithms were applied to obtain hub lncRNAs. A novel genome instability-related ceRNA network was predicted with the Starbase 2.0 database. To better understand how these genes fundamentally interact during tumor progression and development, the mutation analysis and single-gene analysis for each gene was performed. Results: In contrast to those in the GS-like cluster, GU-like-cluster patients demonstrated a higher tumor mutational burden (TMB)/microsatellite instability (MSI), DNA polymerase epsilon (POLE) mutation rate, and immune checkpoint expression, all indicate a greater predictive power for response rate for immunotherapy. The novel prognostic signature demonstrated an outstanding predictive performance (AUC > 0.70). The genes in the genome insatiability-related ceRNA network (including four axes: AL161772.1-has-miR-671-5p (hsa-miR-181d-5p, has-miR-106a-5p)-NINL, AL161772.1-has-miR-106a-5p-TNFSF11, AC124067.4-hsa-miR-92b-3p (hsa-miR-589-5p)-PHYHIPL, and BOLA3-AS1-has-miR-130b-3p-SALL4) were identified as critical regulators of tumor microenvironment infiltration, cancer stemness, and drug resistance. qPCR was performed to validate the expression patterns of these genes. Furthermore, the MSI-high proportion was greater in patients with mutated type than in those with the wild type according to all four target genes, indicating that these four genes modulate genomic integrity and could serve as novel immunotherapy biomarkers. Conclusion: We demonstrated that genome instability-related lncRNA is a novel biomarker for immunotherapy outcomes and prognosis. A novel ceRNA network that modulates genomic integrity, including four lncRNA-miRNA-mRNA axes, was proposed.
| S-EPMC8564000 | biostudies-literature
Project description:N6-methyladenosine (m6A) lncRNA plays a pivotal role in cancer. However, little is known about its role in pancreatic ductal adenocarcinoma (PDAC) and its tumor immune microenvironment (TIME). Based on The Cancer Genome Atlas (TCGA) cohort, m6A-related lncRNAs (m6A-lncRNA) with prognostic value were filtered using Pearson analysis and univariate Cox regression analysis. Distinct m6A-lncRNA subtypes were divided using unsupervised consensus clustering. Least absolute shrinkage and selection operator (LASSO) Cox regression was applied to establish an m6A-lncRNA-based risk score signature. The CIBERSORT and ESTIMATE algorithms were employed to analyze the TIME. The expression pattern of TRAF3IP2-AS1 was examined using qRT-PCR. The influence of TRAF3IP2-AS1 knockdown on cell proliferation was estimated by performing CCK8, EdU and colony-formation assays. Flow cytometry was applied to measure the effect of TRAF3IP2-AS1 knockdown on cell cycle and apoptosis. The in vivo anti-tumor effect of TRAF3IP2-AS1 was validated in a tumor-bearing mouse model. Two m6A-lncRNA subtypes with different TIME features were clarified. A risk score signature was constructed as a prognostic predictor based on m6A-lncRNAs. The risk score also correlated with TIME characterization, which facilitated immunotherapy. Finally, the m6A-lncRNA TRAF3IP2-AS1 was proved to be a tumor suppressor in PDAC. We comprehensively demonstrated m6A-lncRNAs to be useful tools for prognosis prediction, TIME depiction and immunotherapeutic guidance in PDAC.
| S-EPMC10056289 | biostudies-literature
Project description:Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have a great impact on OV development and patient immunotherapy response. In this paper, we decided to establish a reliable signature of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis of the TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model of mRLs. K-M analysis, PCA, GSEA and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune feature comparison, consensus clustering analysis and pan-cancer analysis. Results: A prognostic signature comprising four mRLs, WAC-AS1, LINC00997, DNM3OS and FOXN3-AS1, was constructed and verified for OV according to the TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, one can identify patients more effectively. The samples were divided into two clusters, and the clusters had different overall survival rates, clinical features and tumor microenvironments. Finally, pan-cancer analysis further demonstrated that the four mRLs were significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusions: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.
| S-EPMC9406778 | biostudies-literature
Project description:With high morbidity and mortality, colon cancer (CC) is considered as one of the most often diagnosed cancers around the world. M7G-related lncRNA may provide a regulatory function in the formation of CC, but the principle of regulation is still unclear. The purpose of this research was to establish a novel signature that may be used to predict survival and tumour immunity in CC patients. Data about CC in TCGA was collected for analysis, coexpression analysis and univariate Cox analysis were used to screen prognostic m7G-related lncRNAs. A consensus clustering analysis based on prognostic m7G-related lncRNAs was applied, and a prognosis model based on least absolute shrinkage and selection operator (LASSO) regression analysis was established. Independent prognostic analysis, nomogram, PCA, clinicopathological correlation analysis, TMB, survival analysis, immune correlation analysis, qRT–PCR and clinical therapeutic compound prediction were also applied. 90 prognostic m7G-related lncRNAs were found, GO and KEGG analysis showed that prognostic m7G-related lncRNAs were mainly related to cell transcription and translation. The results of the consensus clustering analysis revealed substantial disparities in survival prognosis and tumour immune infiltration between two clusters. We built a risk model with 21 signature m7G-related lncRNAs, patients in the high-risk group had a considerably poorer prognosis than those in the low-risk group. Independent prognostic analysis confirmed that patients’ prognosis was linked to their tumour stage and risk score. PCA, subgroups with distinct clinicopathological characteristics were studied for survival, multi-index ROC curve, c-index curve, the survival analysis of TMB, and model comparison tested the reliability of risk model. A tumour immunoassay revealed a substantial difference in immune infiltration between high-risk and low-risk individuals. Five chemicals were eliminated, and qRT–PCR indicated that the four lncRNAs were expressed differently. Overall, m7G-related lncRNA is closely related to colon cancer and the 21 signature lncRNAs risk model can efficiently evaluate the prognosis of CC patients, which has a possible positive consequence for the future diagnosis and therapy of CC.
| S-EPMC9277109 | biostudies-literature
Project description:Colorectal cancer, especially colon adenocarcinoma (COAD), is associated with significant morbidity and mortality worldwide. Long noncoding RNA (lncRNA) has been implicated in tumorigenesis. The aim of the present study was to elucidate the potential diagnostic and prognostic values of lncRNA FRGCA in COAD.The data of 438 COAD patients were retrieved for analysis. Diagnostic significance was evaluated using tumor and nontumor tissues. Prognostic significance was evaluated using a Cox proportional regression model. Stratified analysis was performed to identify associations between clinical factors and lncRNA FRGCA expression. A nomogram was constructed using the clinical factors and lncRNA FRGCA for survival prediction. Enrichment analysis identified gene ontologies and metabolic pathways of mRNAs with high Pearson correlation coefficients with lncRNA FRGCA.lncRNA FRGCA was highly expressed in tumor tissues of COAD and demonstrated diagnostic value (area under curve = 0.763, P < .0001). Prognostic significance analysis indicated that lncRNA FRGCA had prognostic value in COAD [adjusted P < .001, hazard ratio (HR) = 0.444, 95% confidence interval (95% CI) = 0.288-0.685] and high expression of lncRNA FRGCA indicated better survival in COAD. A nomogram was evaluated for prediction of survival at 1, 3, and 5 years. Enrichment analysis revealed many mRNAs involved in the structural constituents of the mitochondrial inner membrane and translational termination, protein binding, translation, ribosome, oxidative phosphorylation, and metabolic pathways, especially the nucleoplasm.Differentially expressed in tumor vs nontumor tissues, lncRNA FRGCA had both diagnostic and prognostic implications in COAD, which may be associated with ribosome metabolism, oxidative phosphorylation, and nucleoplasm-related metabolic pathways.
| S-EPMC7598802 | biostudies-literature
Project description:Long non-coding RNAs (lncRNAs) have a number of functions in various cellular processes and are potential prognostic factors for lung adenocarcinoma (LUAD). A gene risk model could provide novel evidence to improve the prediction of overall outcomes and provide more potential biomarkers. The present study aimed improve a previously published method of gene signature construction to make it more robust and accurate. The lncRNA expression profiles from 594 patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database and samples were divided into high- and low-risk groups based on median risk scores calculated using a prognosis-related risk score formula. Univariate Cox regression, least absolute shrinkage and selection operator algorithm and multivariate Cox regression were performed to construct a gene signature based on the differentially expressed lncRNAs in patients with LUAD. The robustness and accuracy of the present model was assessed using area under the calculated curves (AUC) and Kaplan-Meier (K-M) survival analysis of the high- and low-risk cohorts. Potential biomarkers associated with survival status were then identified using K-M survival analysis and potential biomarker functions were predicted using enrichment analysis of co-expressed mRNAs. The gene signature constructed contained 44 lncRNAs. The AUCs for 3- and 5-year survival with the model were 0.836 and 0.818, respectively, of a time-dependent receiver operator characteristic curve. Moreover, lncRNAs AC124804.1 and MIR34AHG were identified using K-M survival analysis and the potential function of these two lncRNAs was predicted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment. The present lncRNA model provides novel insight which may improve prediction of prognosis for patients with LUAD and identify potentially novel biomarkers for the diagnosis.
| S-EPMC7068299 | biostudies-literature
Project description:Colon adenocarcinoma (COAD) is among the most common digestive system malignancies worldwide, and its pathogenesis and gene signatures remain unclear. This study explored the genetic characteristics and molecular mechanisms underlying colon cancer development. Three gene expression data sets were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was used to determine differentially expressed genes (DEGs) between COAD and normal tissues. Then, the intersection of the data sets was obtained. Metascape was used to perform the functional enrichment analyses. Next, STRING was used to build protein-protein interaction (PPI) networks. Hub genes were identified and analysed using Cytoscape. Next, survival analysis and expression analysis of the hub genes were performed. ROC curve analysis was performed for further test of the diagnostic efficacy. Finally, alterations in the hub genes were predicted and analysed by cBioPortal. Altogether, 436 DEGs were detected. The DEGs were mainly enriched in cell cycle phase transition, nuclear division, meiotic nuclear division, and cytokinesis. Based on PPI networks, 20 hub genes were selected. Among them, 6 hub genes (CCNB1, CCNA2, AURKA, NCAPG, DLGAP5, and CENPE) showed significant prognostic value in colon cancer (P < 0.05), while 5 hub genes (CDK1, CCNB1, CCNA2, MAD2L1, and DLGAP5) were associated with early colon cancer diagnosis and ROC curve analysis showed good diagnostic accuracy. In conclusion, integrated bioinformatics analysis was used to identify hub genes that reveal the potential mechanism of carcinogenesis and progression of colon cancer. The hub genes might be novel biomarkers for early diagnosis, treatment, and prognosis of colon cancer.
| S-EPMC9239823 | biostudies-literature
Project description:Colon cancer (CC) is one of the most prevalent malignant tumours of the alimentary canal. It is unclear whether pyroptosis-related lncRNA expression is correlated with CC prognosis. We discovered 20 pyroptosis-related lncRNAs that were expressed differently in CC and normal colon tissues in our investigation. Based on differentially expressed genes (DEGs), we grouped all CC patients into two categories (Clusters 1 and 2). Cluster 1 was shown to be connected with a higher overall survival rate, upregulated expression of immune checkpoints, higher immunoscores, higher estimated scores, and immune cell infiltration. Using data from the Cancer Genome Atlas (TCGA), to create a multigene signature, the predictive significance of each lncRNA linked with pyroptosis for survival was assessed. A 9-lncRNA signature was established using the least absolute shrinkage and selection operator (LASSO) Cox regression method, and all CC patients in the TCGA cohort were classified into low-risk or high-risk groups. The low-risk CC patients had a much greater chance of survival than those in the high-risk group. The risk score is an independent prognostic indicator for predicting survival. In addition, risk characteristics are linked to immune characteristics. In summary, pyroptosis-related lncRNAs can be used to predict CC prognosis and participate in tumour immunity.
| S-EPMC8789477 | biostudies-literature
Project description:Colorectal cancer ranks third in terms of incidence and second in terms of mortality worldwide. The homeobox transcript antisense intergenic RNA (HOTAIR), which was found to be located on the antisense chain of the homeobox C (HOXC) gene cluster, is a long non-coding RNA involved in multiple types of tumors. The role of HOXC11 in tumors remains unclear. Reverse transcription-quantitative PCR was performed to detect the expression level of HOXC11 in colon adenocarcinoma. Cell proliferation and invasion were assessed. RNase protection assay was used to test the possibility of RNA duplex formation. The increased expression and co-expression trend of HOXC11 and HOTAIR were identified in multiple types of cancer from The Cancer Genome Atlas and the results were validated in 12 colon adenocarcinoma and paired non-tumor tissue samples. The expression of HOXC11 and HOTAIR was found to be associated with poor prognosis in colon adenocarcinoma and kidney renal clear cell carcinoma. Furthermore, HOXC11 was found to positively regulate HOTAIR by RNA duplex formation and promoted the proliferation and invasion of colon adenocarcinoma cells.
| S-EPMC8461627 | biostudies-literature
Project description:Noncoding RNAs (ncRNAs), especially microRNA (miRNA) and long noncoding RNA (lncRNA), have an impact on a variety of important biological processes during colon adenocarcinoma (COAD) progression. This includes chromatin organization, transcriptional and posttranscriptional regulation, and cell-cell signaling. The aim of this study is to identify the ncRNA-regulated modules that accompany the progression of COAD and to analyze their mechanisms, in order to screen the potential prognostic biomarkers for COAD. An integrative molecular analysis was carried out to identify the crosstalks of gene modules between different COAD stages, as well as the essential ncRNAs in the posttranscriptional regulation of these modules. 31 ncRNA regulatory modules were found to be significantly associated with overall survival in COAD patients. 17 out of the 31 modules (in which ncRNAs played essential roles) had improved the predictive ability for COAD patient survival compared to only the mRNAs of those modules, which were enriched in the core cancer hallmark pathways with closer interactions. These suggest that the ncRNAs' regulatory modules not only exhibit close relation to COAD progression but also reflect the dynamic significant crosstalk of genes in the modules to the different malignant extent of COAD.
| S-EPMC7128050 | biostudies-literature