Dataset Information

Stemness-related LncRNA pair signature for predicting therapy response in gastric cancer.

ABSTRACT:

Objective

As a critical feature of cancers, stemness is acknowledged as a contributor to the development of drug resistance in gastric cancer (GC). LncRNAs have been revealed to participate in this process. In this study, we tried to develop a stemness-related lncRNA pair signature as guidance for clinical decisions.

Methods

The analysis was initiated by collecting stemness-related lncRNAs in TCGA cohort. The differentially expressed stemness-related lncRNAs between normal and tumor tissues in GC patients from TCGA datasets were further collected to establish the signature based on Lasso and Cox regression analyses. The predictive efficacy of the signature for chemotherapy and immunotherapy was also tested. The practicality of this signature was also validated by Zhongshan cohort.

Results

A 13-DEsrlncRNA pair-based signature was established. The cutoff point acquired by the AIC algorithm divided the TCGA cohort into high and low risk groups. We found that the low-risk group presented with better survival (Kaplan-Meier analysis, p < 0.001). Cox regression analyse was also conducted to confirm the signature as an independent risk factor for GC {p < 0.001, HR = 1.300, 95% CI (1.231-1.373)]}. As for the practicality of this signature, the IC50 of cytotoxic chemotherapeutics was significantly higher in the high-risk group. The low-risk group also presented with higher immunophenoscore (IPS) in both the "CTLA4+ PD1+" (Mann-Whitney U test, p = 0.019) and "CTLA4- PD1+" (Mann-Whitney U test, p = 0.013) groups, indicating higher sensitivity to immunotherapy. The efficacy of the signature was also validated by Zhongshan cohort.

Conclusions

This study could not only provide a stemness-related lncRNA signature for survival prediction in GC patients but also established a model with predictive potentials for GC patients' sensitivity to chemotherapy and immunotherapy.

SUBMITTER: Jiang Q

PROVIDER: S-EPMC8482617 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: The construction of ferroptosis-related lncRNA prognostic models in malignancies has been an intense area of research recently. However, most of the studies focused on the exact expression of lncRNAs and had limited application values. Herein, we aim to establish a novel prognostic model for gastric cancer (GC) patients and discuss its correlation with immune landscapes and treatment responses. Methods: The present study retrieved transcriptional data of GC patients from the Cancer Genome Atlas (TCGA) database. We identified differentially expressed ferroptosis-related lncRNAs between tumor and normal controls of GC samples. Based on a new method of cyclically single pairing, we constructed a 0 or 1 matrix of ferroptosis-related lncRNA pairs (FRLPs). A risk score signature consisting of 10 FRLPs was established using multi-step Cox regression analysis. Next, we performed a series of systematic analyses to investigate the association of the FRLP model and tumor microenvironment, biological function, and treatment responses. An alternative model to the FRLP risk score signature, the gene set score (GS) model was also constructed, which could represent the former when lncRNA expression was not available. Results: We established a novel prognostic signature of 10 ferroptosis-related lncRNA pairs. High-risk patients in our risk score model were characterized by high infiltration of immune cells, upregulated carcinogenic and stromal activities, and heightened sensitivity to a wide range of anti-tumor drugs, whereas low-risk patients were associated with better responses to methotrexate treatment and elevated immunotherapeutic sensitivity. The practicability of the FRLP risk score model was also validated in two independent microarray datasets downloaded from Gene Expression Omnibus (GEO) using the GS model. Finally, two online dynamic nomograms were built to enhance the clinical utility of the study. Conclusion: In this study, we developed a ferroptosis-related lncRNA pair-based risk score model that did not rely on the exact lncRNA expression level. This novel model might provide insights for the accurate prediction and comprehensive management for GC patients.

Project description:BackgroundGastric cancer (GC), the most commonly diagnosed cancer worldwide with poor 5-year survival rate in advanced stages. Although immune-related and survival-related biomarkers, which typically comprise aberrantly expressed long non-coding RNAs (lncRNAs) and genes, have been identified, there are no reports of immune-related lncRNA pair (IRLP) signatures for GC.MethodsIn this study, we acquired lncRNA expression profiles from The Cancer Genome Atlas (TCGA) and used the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model (iteration = 1000) to develop a IRLP prognostic signature. The area under curve (AUC) was used to assess the prognosis predictive power. The multivariate Cox regression analysis was performed to identify whether this signature was an independent prognostic factor. The immune cell infiltration analysis was performed between the two risk groups. Last, molecular experiments were performed to explore LINC01082 is involved in the development of GC.ResultsWe acquired lncRNA expression profiles and used the LASSO Cox model to develop an 18-IRLP signature with a strong prognostic predictive power. The 5-year AUC values of the training, validation, and overall TCGA datasets were 0.77, 0.86, and 0.80, respectively. The different prognostic outcomes between the high- and low-risk groups were determined using our 18-IRLP signature. Moreover, our 18-IRLP signature was an independent prognostic factor as per the multivariate Cox regression analysis, and showed better prognostic evaluation than the traditional TNM staging system as well as other clinical features. We also found differences in cancer-associated fibroblast and macrophage M2 infiltration and the expression of PD-L1, CTLA4, LAG3, and HLA were also observed between the two risk groups (P < 0.05). Analysis of biological functions revealed that target genes of the lncRNAs in the IRLP signature were enriched in focal adhesion and regulation of actin cytoskeleton. Finally, as one of significant candidates of IRLP signature, overexpression of LINC01082 suppressed the invasion ability of GC cells as well as PD-L1 expression profiles.ConclusionsOur novel 18-IRLP signature provides new insights regarding immunological biomarkers, imparts a better understanding of the tumor immune microenvironment, and can be used for predicting prognosis and evaluating immune response in GC.

Project description:Background: Gastric cancer (GC) is a multifactorial progressive disease with high mortality and heterogeneous prognosis. Effective prognostic biomarkers for GC were critically needed. Hippo signaling pathway is one of the critical mechanisms regulating the occurrence and development of GC, and has potential clinical application value for the prognosis and treatment of GC patients. However, there is no effective signature based on Hippo signaling pathway-related genes (HSPRGs) to predict the prognosis and treatment response of GC patients. Our study aimed to build a HSPRGs signature and explore its performance in improving prognostic assessment and drug therapeutic response in GC. Methods: Based on gene expression profiles obtained from The Cancer Genome Atlas (TCGA) database, we identified differentially expressed HSPRGs and conducted univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a multigene risk signature. Subsequently, the Kaplan-Meier curve and receiver operating characteristic (ROC) were performed to evaluate the predictive value of the risk signature in both training and validation cohort. Furthermore, we carried out univariate and multivariate Cox regression analysis to investigate the independent prognostic factors and establish a predictive nomogram. The enriched signaling pathways in risk signature were analyzed by gene set enrichment analysis (GSEA). Tumor immune dysfunction and exclusion (TIDE) and drug sensitivity analysis were performed to depict therapeutic response in GC. Results: In total, 38 differentially expressed HSPRGs were identified, and final four genes (DLG3, TGFB3, TGFBR1, FZD6) were incorporated to build the signature. The ROC curve with average 1-, 3-, and 5-year areas under the curve (AUC) equal to .609, .634, and .639. Clinical ROC curve revealed that risk signature was superior to other clinicopathological factors in predicting prognosis. Calibration curves and C-index (.655) of nomogram showed excellent consistency. Besides, in the immunotherapy analysis, exclusion (p < 2.22 × 10-16) and microsatellite instability (p = .0058) performed significantly differences. Finally, our results suggested that patients in the high-risk group were more sensitive to specific chemotherapeutic agents. Conclusion: Results support the hypothesis that Hippo-related signature is a novel prognostic biomarker and predictor, which could help optimize GC prognostic stratification and inform clinical medication decisions.

Project description:BackgroundGastric cancer (GC) is one of the most common malignancies. Cuproptosis is a newly discovered type of cell death caused by protein toxicity stress, with copper having considerable importance in GC development.MethodsFirst, differentially expressed (DE) cuproptosis-related genes (CRGs) were screened in GC. The tumor mutation burden (TMB) of CRGs was analyzed. We then performed enrichment analyses of DE-CRGs. Next, we constructed a GC cuproptosis-related (CR) signature (CRs) using Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. The predictive efficacy was assessed using receiver operating characteristic (ROC) curves. Furthermore, we performed gene set enrichment analysis (GSEA). Different methods were used to assess tumor immunity of the CRs, and the Wilcoxon test was used to examine the expressions of m6A-, m7G-, and ferroptosis-related genes. The "pRRophetic" R package (The R Foundation for Statistical Computing) was used to predict the half maximal inhibitory concentration IC50 of common chemotherapeutic agents. Finally, the expression of CRGs in different clusters was analyzed using single-cell RNA sequencing (scRNA-seq).ResultsWe identified 8 DE-CRGs in GC. There were 9 CRGs with TMB values >1%. We constructed gene expression networks and CRs for GC. The DE-CRGs were involved in important mitochondrial metabolic pathways, and the CRs was a valuable independent prognosis factor. The GSEA revealed that angiogenesis and metabolic-related pathways were enriched in the high-risk group, whereas the low-risk group showed enrichment in DNA replication mismatch and repair pathways. The expressions of immunological checkpoints, ferroptosis-, m6A-, and m7G-related genes, type II interferon (INF) response, major histocompatibility complex (MHC class-I), and the IC50 of the copper-based carrier drug elesclomol were significantly different between the 2 groups of the CRs. Furthermore, the scRNA-seq analysis showed that most CRGs were mainly upregulated in endothelial cells.ConclusionsThe novel CRs could predict the prognosis of GC.

Project description:Glioma is one of the most common intracranial malignancies that plagues people around the world. Despite current improvements in treatment, the prognosis of glioma is often unsatisfactory. Necroptosis is a form of programmed cell death. As research progresses, the role of necroptosis in tumors has gradually attracted the attention of researchers. And lncRNA is regarded as a critical role in the development of cancer. Therefore, this study is aimed at establishing a prognostic model based on necroptosis-associated lncRNAs to accurately assess the prognosis and immune response of patients with glioma. The RNA sequences of glioma patients and normal brain samples were downloaded from The Cancer Genome Atlas (TCGA) and GTEx databases, respectively. The coexpression analysis was performed to identify the necroptosis-related lncRNAs. Then, we utilized LASSO analysis following univariate Cox analysis to construct a prognostic model. Subsequently, we applied the Kaplan-Meier curve, time-dependent receiver operating characteristics (ROC), and univariate and multivariate Cox regression analyses to assess the effectiveness of this model. And the functional enrichment analyses and immune-related analyses were employed to investigate the potential biological functions. A validation set was obtained from the Chinese Glioma Genome Atlas (CGGA) database. And qRT-PCR was employed to further validate the expression levels of selected necroptosis-associated lncRNAs. Seven necroptosis-related lncRNAs (FAM13A-AS1, JMJD1C-AS1, LBX2-AS1, ZBTB20-AS4, HAR1A, SNHG14, and LINC00900) were determined to construct a prognostic model. The area under the ROC curve (AUC) was 0.871, 0.901, and 0.911 at 1, 2, and 3 years, respectively. The risk score was shown to be an important independent predictor in both univariate and multivariate Cox regression analyses. Through functional enrichment analyses, we found that the differentially expressed genes (DEGs) were mainly enriched in protein binding and signaling-related biological functions and immune-associated pathways. In conclusion, we established and validated a novel necroptosis-related lncRNA signature, which could accurately predict the overall survival of glioma patients and serve as potential therapeutic targets.