An immune-related lncRNA signature for the prognosis of pancreatic adenocarcinoma.
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ABSTRACT:
SUBMITTER: Qi B
PROVIDER: S-EPMC8351726 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Pancreatic adenocarcinoma (PAAD) is a common, highly malignant, and aggressive gastrointestinal tumor. The conventional treatment of PAAD shows poor results, and patients have poor prognosis. The synthesis and degradation of proteins are essential for the occurrence and development of tumors. Aggrephagy is a type of autophagy that selectively degrades aggregated proteins. It decreases the formation of aggregates by degrading proteins, thus reducing the harm to cells. By breaking down proteins, it decreases the formation of aggregates; thus, minimizing damage to cells. For evaluating the response to immunotherapy and prognosis in PAAD patients, in this study, we developed a reliable signature based on aggrephagy-related genes (ARGs). We obtained 298 AGGLncRNAs. Based on the results of one-way Cox and LASSO analyses, the lncRNA signature was constructed. In the risk model, the prognosis of patients in the low-risk group was noticeably better than that of the patients in the high-risk group. Additionally, the ROC curves and nomograms validated the capacity of the risk model to predict the prognosis of PAAD. The patients in the low-risk and high-risk groups showed considerable variations in functional enrichment and immunological analysis. Regarding drug sensitivity, the low-risk and high-risk groups had different half-maximal inhibitory concentrations (IC50).
| S-EPMC9859148 | biostudies-literature
Project description:N7-Methylguanosine (m7G) modification holds significant importance in regulating posttranscriptional gene expression in epigenetics. Long non-coding RNAs (lncRNAs) have been demonstrated to play a crucial role in cancer progression. m7G-related lncRNA may be involved in the progression of pancreatic cancer (PC), although the underlying mechanism of regulation remains obscure. We obtained RNA sequence transcriptome data and relevant clinical information from the TCGA and GTEx databases. Univariate and multivariate Cox proportional risk analyses were performed to build a twelve-m7G-associated lncRNA risk model with prognostic value. The model was verified using receiver operating characteristic curve analysis and Kaplan–Meier analysis. The expression level of m7G-related lncRNAs in vitro was validated. Knockdown of SNHG8 increased the proliferation and migration of PC cells. Differentially expressed genes between high- and low-risk groups were identified for gene set enrichment analysis, immune infiltration, and potential drug exploration. We conducted an m7G-related lncRNA predictive risk model for PC patients. The model had independent prognostic significance and offered an exact survival prediction. The research provided us with better knowledge of the regulation of tumor-infiltrating lymphocytes in PC. The m7G-related lncRNA risk model may serve as a precise prognostic tool and indicate prospective therapeutic targets for PC patients.
| S-EPMC10217316 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths worldwide. Therefore, the identification of a novel prediction signature for predicting the prognosis risk and survival outcomes is urgently demanded.MethodsWe integrated a machine-learning frame by combing the Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression model to identify the LUAD-related long non-coding RNA (lncRNA) survival biomarkers. Subsequently, the Spearman correlation test was employed to interrogate the relationships between lncRNA signature and tumor immunity and constructed the competing endogenous RNA (ceRNA) network.ResultsHerein, we identified an eight-lncRNA signature (PR-lncRNA signature, NPSR1-AS1, SATB2-AS1, LINC01090, FGF12-AS2, AC005256.1, MAFA-AS1, BFSP2-AS1, and CPC5-AS1), which contributes to predicting LUAD patient's prognosis risk and survival outcomes. The PR-lncRNA signature has also been confirmed as the robust signature in independent datasets. Further parsing of the LUAD tumor immune infiltration showed the PR-lncRNAs were closely associated with the abundance of multiple immune cells infiltration and the expression of MHC molecules. Furthermore, by constructing the PR-lncRNA-related ceRNA network, we interrogated more potential anti-cancer therapy targets.ConclusionlncRNAs, as emerging cancer biomarkers, play an important role in a variety of cancer processes. Identification of PR-lncRNA signatures allows us to better predict patient's survival outcomes and disease risk. Finally, the PR-lncRNA signatures could help us to develop novel LUAD anti-cancer therapeutic strategies.
| S-EPMC8605161 | biostudies-literature
Project description:Cuproptosis is a novel cell death modality but its regulatory role in the colon cancer remains obscure. This study is committed to establishing a cuproptosis-related lncRNA (CRL) signature to forecast the prognosis for colon adenocarcinoma (COAD). The Cancer Genome Atlas (TCGA) samples were randomly divided into training and validation cohorts. LASSO-COX analysis was performed to construct a prognostic signature consisting of five CRLs (AC015712.2, ZEB1-AS1, SNHG26, AP001619.1, and ZKSCAN2-DT). We found the patients with high-risk scores suffered from poor prognosis in training cohort (p < 0.001) and validation cohort (p = 0.004). Nomogram was created based on the 5-CRL signature. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) demonstrated the nomogram performed well in 1‑, 3‑, and 5‑year overall survival (OS). Subsequently, we observed increased infiltration of multiple immune cells and upregulated expression of immune checkpoints and RNA methylation modification genes in high-risk patients. Additionally, gene set enrichment analysis (GSEA) revealed two tumor-related pathways, including MAPK and Wnt signaling pathways. Finally, we found AKT inhibitors, all-trans retinoic acid (ATRA), camptothecin, and thapsigargin had more sensitivity to antitumor therapy in high-risk patients. Collectively, this CRL signature is promising for the prognostic prediction and precise therapy of COAD.
| S-EPMC10113217 | biostudies-literature
Project description:BackgroundCuproptosis, a recently discovered type of programmed cell death (PCD), paves a new avenue for cancer treatment. It has been revealed that PCD-related lncRNAs play a critical role in various biological processes of lung adenocarcinoma (LUAD). However, the role of cuproptosis-related lncRNA (CuRLs) remains unclear. This study aimed to identify and validate a CuRLs-based signature for the prognostic prediction of patients with LUAD.MethodsRNA sequencing data and clinical information of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Pearson correlation analysis was used to identify CuRLs. Univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, and stepwise multivariate Cox analysis were applied to construct a novel prognostic CuRLs signature. A nomogram was developed for the prediction of patient survival outcomes. Gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were utilized to explore potential functions underlying the CuRLs signature. Patients were divided into low- and high-risk groups. Several algorithms, such as tumor immune estimation resource (TIMER), cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and QuanTIseq, were combined to comprehensively investigate the differences in immune landscape between different risk groups. Sensitivity to common anticancer drugs was analyzed using the pRRophetic algorithm.ResultsWe constructed a novel prognostic signature based on 10 CuRLs, including CARD8-AS1, RUNDC3A-AS1, TMPO-AS1, MIR31HG, SEPSECS-AS1, DLGAP1-AS1, LINC01137, ZSCAN16-AS1, APTR, and ELOA-AS1. This 10-CuRLs risk signature showed great diagnostic accuracy combined with traditional clinical risk factors, and a nomogram was constructed for potential clinical translation. The tumor immune microenvironment was significantly different between different risk groups. Among drugs commonly used in the treatment of lung cancer, the sensitivity of cisplatin, docetaxel, gemcitabine, gefitinib, and paclitaxel was higher in low-risk patients, and patients in the low-risk group may benefit more from imatinib.ConclusionsThese results revealed the outstanding contribution of the CuRLs signature to the evaluation of prognosis and treatment modalities for patients with LUAD. The differences in characteristics between different risk groups provide an opportunity for better patient stratification and to explore novel drugs in different risk groups.
| S-EPMC9989802 | biostudies-literature
Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant tumor of the digestive system. Its grim prognosis is mainly attributed to the lack of means for early diagnosis and poor response to treatments. Genomic instability is shown to be an important cancer feature and prognostic factor, and its pattern and extent may be associated with poor treatment outcomes in PDAC. Recently, it has been reported that long non-coding RNAs (lncRNAs) play a key role in maintaining genomic instability. However, the identification and clinical significance of genomic instability-related lncRNAs in PDAC have not been fully elucidated.MethodsGenomic instability-derived lncRNA signature (GILncSig) was constructed based on the results of multiple regression analysis combined with genomic instability-associated lncRNAs and its predictive power was verified by the Kaplan-Meier method. And real-time quantitative polymerase chain reaction (qRT-PCR) was used for simple validation in human cancers and their adjacent non-cancerous tissues. In addition, the correlation between GILncSig and tumor microenvironment (TME) and epithelial-mesenchymal transition (EMT) was investigated by Pearson correlation analysis.ResultsThe computational framework identified 206 lncRNAs associated with genomic instability in PDAC and was subsequently used to construct a genome instability-derived five lncRNA-based gene signature. Afterwards, we successfully validated its prognostic capacity in The Cancer Genome Atlas (TCGA) cohort. In addition, via careful examination of the transcriptome expression profile of PDAC patients, we discovered that GILncSig is associated with EMT and an adaptive immunity deficient immune profile within TME.ConclusionsOur study established a genomic instability-associated lncRNAs-derived model (GILncSig) for prognosis prediction in patients with PDAC, and revealed the potential functional regulatory role of GILncSig.
| S-EPMC9486402 | biostudies-literature
Project description:Colon adenocarcinoma (COAD) is one of the most frequent types of cancer worldwide. Disulfidptosis has been identified as a new mode of cell death recently. The goal of this study was to explore the possibility of a connection between disulfidptosis and COAD. RNA sequencing data from COAD patients were retrieved from the The Cancer Genome Atlas (TCGA) database for this investigation. R software and various methods were used to identify disulfidptosis-related lncRNAs (DRLs) in COAD, and a prognostic model was created based on 6 DRLs (AP003555.1, AL683813.1, SNHG7, ZEB1-AS1, AC074212.1, RPL37A-DT). The prognostic model demonstrated a good accuracy in predicting the prognosis of COAD patients, according to receiver operating characteristic (ROC) curve and Concordance index (C-index) analyses. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed significant differences in biological functions and signaling pathways involved in differential genes in risk subgroups, including protein - DNA complex subunit organization, Hippo signaling pathway, Wnt signaling pathway. TIDE analysis was done on risk groupings in this study, and it found that patients in the high-risk group had more immune escape potential and were less probable to react to immunotherapy. Real-time quantitative pcr (qRT-PCR) was used to identify the relatively high expression of 6 DRLs in colon cancer cell lines. In summary, 6 DRLs were identified as possible novel molecular therapy targets for COAD in this investigation. This prognostic model has the potential to be a novel tool for forecasting COAD prognosis in clinical practice, as well as providing new insights on the potential function and mechanism of disulfidptosis in the COAD process.
| S-EPMC10636996 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most fatal cancers in the world. Previous studies have shown the increase in glycosylation level, and abnormal expressions of related enzymes are closely related to various cancers. Long non-coding RNAs (lncRNAs) play an important role in the proliferation, metabolism, and migration of cancer cells, but the underlying role of glycosyltransferase (GT)-related lncRNAs in LUAD remains to be elucidated.MethodsWe abstracted 14,056 lncRNAs from The Cancer Genome Atlas (TCGA) dataset and 257 GT-related genes from the Gene Set Enrichment Analysis (GSEA) database. Univariate, LASSO-penalized, and multivariate Cox regression analyses were conducted to construct a GT-related lncRNA prognosis model.ResultsA total of 2,726 GT-related lncRNAs were identified through Pearson's correlation analysis, and eight of them were utilized to construct a GT-related lncRNA model. The overall survival (OS) of the low-risk group continued to be superior to that of the high-risk group according to the subgroups classified by clinical features. The risk model was proved to have independent prognostic characteristics for LUAD by univariate and multivariate Cox regression analyses. The status of the tumor immune microenvironment and the relevant immunotherapy response was significantly different between the two risk groups. The candidate drugs aimed at LUAD subtype differentiation were identified.ConclusionWe constructed a risk model comprising eight GT-related lncRNAs which was identified as an independent predictor of prognoses to predict patient survival and guide-related treatments for patients with LUAD.
| S-EPMC9434379 | biostudies-literature
Project description:PurposePancreatic ductal adenocarcinoma (PDAC) is one of the highest fatality rate cancers with poor survival rates. The tumor microenvironment (TME) is vital for tumor immune responses, leading to resistance to chemotherapy and poor prognosis of PDAC patients. This study aimed to provide a comprehensive evaluation of the immune genes and microenvironment in PDAC that might help in predicting prognosis and guiding clinical treatments.MethodsWe developed a prognosis-associated immune signature (i.e., PAIS) based on immune-associated genes to predict the overall survival of patients with PDAC. The clinical significance and immune landscapes of the signature were comprehensively analyzed.ResultsOwing to gene expression profiles from TCGA database, functional enrichment analysis revealed a significant difference in the immune response between PDAC and normal pancreas. Using transcriptome data analysis of a training set, we identified an immune signature represented by 5 genes (ESR2, IDO1, IL20RB, PPP3CA, and PLAU) related to the overall survival of patients with PDAC, significantly. This training set was well-validated in a test set. Our results indicated a clear association between a high-risk score and a very poor prognosis. Stratification analysis and multivariate Cox regression analysis revealed that PAIS was an important prognostic factor. We also found that the risk score was positively correlated with the inflammatory response, antigen-presenting process, and expression level of some immunosuppressive checkpoint molecules (e.g., CD73, PD-L1, CD80, and B7-H3). These results suggested that high-risk patients had a suppressed immune response. However, they could respond better to chemotherapy. In addition, PAIS was positively correlated with the infiltration of M2 macrophages in PDAC.ConclusionsThis study highlighted the relationship between the immune response and prognosis in PDAC and developed a clinically feasible signature that might serve as a powerful prognostic tool and help further optimize the cancer therapy paradigm.
| S-EPMC7945593 | biostudies-literature
Project description:BackgroundColon adenocarcinoma (COAD) is the most common type of colon cancer. To date, however, the prognostic values of m6A RNA methylation-related long non-coding RNAs (lncRNAs) in COAD are largely unknown.Materials and methodsThe m6A-related lncRNAs were identified from The Cancer Genome Atlas (TCGA) data set. Univariate and multivariate Cox regression analyses were performed to explore the prognostic m6A-related lncRNAs. Consistent clustering analysis was performed to classify the COAD patients into different subgroups based on the expression of m6A-related lncRNAs. The potential biological functions as well as differences in the stemness index and tumor immune microenvironment between different subgroups were analyzed. The prognostic m6A-related lncRNAs were used to establish an m6A-related lncRNA risk model to predict prognosis and survival status.ResultsWe identified 31 m6A-associated lncRNAs with prognostic values from the TCGA data set. Based on the expression of prognostic m6A-associated lncRNAs, TCGA-COAD patients were classified into three clusters using consistent clustering analysis. There was a low correlation of tumor stemness between the three clusters but a significant correlation with the tumor immune microenvironment as well as the tumor mutational load. Thirty-one prognostic-related m6A-associated lncRNAs were used to construct a risk model, which was further determined by survival analysis, receiver operating characteristic (ROC) curve, and univariate and multifactor Cox analysis. The m6A-related risk model demonstrates good performance in predicting prognosis and survival status. The model-based high-risk group exhibited poorer overall survival (OS) compared with the low-risk group.ConclusionIn this study, we construct a risk model that consists of 31 m6A-related lncRNAs with independent prognostic values in COAD. Our study shows the critical roles of these 31 m6A-related lncRNAs in the tumor immune microenvironment, indicating the prospect of informing prognostic stratification and the development of immunotherapeutic strategies for COAD patients.
| S-EPMC8321625 | biostudies-literature