Angiogenesis-related lncRNAs predict the prognosis signature of stomach adenocarcinoma.
Stomach adenocarcinoma (STAD), which accounts for approximately 95% of gastric cancer types, is a malignancy cancer with high morbidity and mortality. Tumor angiogenesis plays important roles in the progression and pathogenesis of STAD, in which long noncoding RNAs (lncRNAs) have been verified to be crucial for angiogenesis. Our study sought to construct a prognostic signature of angiogenesis-related lncRNAs (ARLncs) to accurately predict the survival time of STAD.
The RNA-sequencing dataset and corresponding clinical data of STAD were acquired from The Cancer Genome Atlas (TCGA). ARLnc sets were obtained from the Ensemble genome database and Molecular Signatures Database (MSigDB, Angiogenesis M14493, INTegrin pathway M160). A ARLnc-related prognostic signature was then constructed via univariate Cox and multivariate Cox regression analysis in the training cohort. Survival analysis and Cox regression were performed to assess the performance of the prognostic signature between low- and high-risk groups, which was validated in the validation cohort. Furthermore, a nomogram that combined the clinical pathological characteristics and risk score conducted to predict the overall survival (OS) of STAD. In addition, ARLnc-mRNA coexpression pairs were constructed with Pearson's correlation analysis and visualized to infer the functional annotation of the ARLncs by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The expression of four ARLncs in STAD and their correlation with the angiogenesis markers, CD34 and CD105, were also validated by RT-qPCR in a clinical cohort.
A prognostic prediction signature including four ARLncs (PVT1, LINC01315, AC245041.1, and AC037198.1) was identified and constructed. The OS of patients in the high-risk group was significantly lower than that of patients in the low-risk group (p < 0.001). The values of the time-dependent area under the curve (AUC) for the ARLnc signature for 1-, 3-, and 5- year OS were 0.683, 0.739, and 0.618 in the training cohort and 0.671, 0.646, and 0.680 in the validation cohort, respectively. Univariate and multivariate Cox regression analyses indicated that the ARLnc signature was an independent prognostic factor for STAD patients (p < 0.001). Furthermore, the nomogram and calibration curve showed accurate prediction of the survival time based on the risk score. In addition, 262 mRNAs were screened for coexpression with four ARLncs, and GO analysis showed that mRNAs were mainly involved in biological processes, including angiogenesis, cell adhesion, wound healing, and extracellular matrix organization. Furthermore, correlation analysis showed that there was a positive correlation between risk score and the expression of the angiogenesis markers, CD34 and CD105, in TCGA datasets and our clinical sample cohort.
Our study constructed a prognostic signature consisting of four ARLnc genes, which was closely related to the survival of STAD patients, showing high efficacy of the prognostic signature. Thus, the present study provided a novel biomarker and promising therapeutic strategy for patients with STAD.
Han C
，Zhang C
，Wang H
，Li K
，Zhao L
... -
《BMC CANCER》
Immune Infiltration Subtypes Characterization and Identification of Prognosis-Related lncRNAs in Adenocarcinoma of the Esophagogastric Junction.
The incidence of adenocarcinoma of the esophagogastric junction (AEG) has markedly increased worldwide. However, the precise etiology of AEG is still unclear, and the therapeutic options thus remain limited. Growing evidence has implicated long non-coding RNAs (lncRNAs) in cancer immunomodulation. This study aimed to examine the tumor immune infiltration status and assess the prognostic value of immune-related lncRNAs in AEG. Using the ESTIMATE method and single-sample GSEA, we first evaluated the infiltration level of 28 immune cell types in AEG samples obtained from the TCGA dataset (N=201). Patients were assigned into high- and low-immune infiltration subtypes based on the immune cell infiltration's enrichment score. GSEA and mutation pattern analysis revealed that these two immune infiltration subtypes had distinct phenotypes. We identified 1470 differentially expressed lncRNAs in two immune infiltration subtypes. From these differentially expressed lncRNAs, six prognosis-related lncRNAs were selected using the Cox regression analysis. Subsequently, an immune risk signature was constructed based on combining the values of the six prognosis-associated lncRNAs expression levels and multiple regression coefficients. To determine the risk model's prognostic capability, we performed a series of survival analyses with Kaplan-Meier methods, Cox proportional hazards regression models, and the area under receiver operating characteristic (ROC) curve. The results indicated that the immune-related risk signature could be an independent prognostic factor with a significant predictive value in patients with AEG. Furthermore, the immune-related risk signature can effectively predict the response to immunotherapy and chemotherapy in AEG patients. In conclusion, the proposed immune-related lncRNA prognostic signature is reliable and has high survival predictive value for patients with AEG and is a promising potential biomarker for immunotherapy.
Hu X
，Wu L
，Liu B
，Chen K
... -
《Frontiers in Immunology》
Construction of store-operated calcium entry-related gene signature for predicting prognosis and indicates immune microenvironment infiltration in stomach adenocarcinomas.
Gastric adenocarcinoma (STAD) is the most prevalent malignancy of the human digestive system and the fourth leading cause of cancer-related death. Calcium pools, especially Ca2+ entry (SOCE) for storage operations, play a crucial role in maintaining intracellular and extracellular calcium balance, influencing cell activity, and facilitating tumor progression. Nevertheless, the prognostic and immunological value of SOCE in STAD has not been systematically studied. The objective of this study was to develop a risk model for SOCE signature and to explore its correlation with clinical characteristics, prognosis, tumor microenvironment (TME), as well as response to immunotherapy, chemotherapy, and targeted drugs. We used the TCGA, GEO (GSE84437 and GSE159929), cBioPortal and TIMER databases to acquire mRNA expression data for STAD, along with patient clinical indicators, single-cell sequencing data, genomic variation information, and correlations of immune cell infiltration. An analysis of SOCE genes based on tumor vs. normal tissue comparisons, pan-cancer dimension, single-cell sequencing, DNA mutation, and copy number variation revealed that SOCE genes significantly impact the survival of STAD patients and are differentially involved in the immune response. SOCE co-expressed genes were identified by Pearson analysis, and subsequently protein-protein interaction (PPI) and gene function enrichment analysis indicated that coexpressed genes were associated with multicellular pathways. Based on TCGA and GSE84437 datasets, a multifactor Cox proportional hazard regression analysis was conducted to identify SOCE co-expressed genes associated with overall survival in STAD patients. Several mRNA prognostic genes, including PTPRJ, GPR146, LTBP3, FBLN1, EFEMP2, ADAMTS7 and LBH, were identified, which could be used as effective prognostic predictors for STAD patients. In both training and test datasets, receiver operating characteristic (ROC) curves were utilized to evaluate and illustrate the predictive capability of this characteristic in forecasting overall survival of STAD patients. The qPCR and human protein atlas (HPA) were employed to assess mRNA expression and protein levels. Furthermore, the ESTIMATE, TIMER, CIBERSORT, QUANTISEQ, MCPCOUNTER, xCell and EPIC algorithms were utilized to perform immune score and analyze immune cell infiltration. It effectively revealed the difference in prognosis and immune cell infiltration in TME between high-risk and low-risk groups based on the risk signature associated with SOCE. Notably, significant differences in tumor immune dysfunction and rejection (TIDE) scores between the two groups, suggesting that patients in the low-risk group may exhibit a more favorable response to ICIS treatment. The GDSC database and R packages for predictive analysis were utilized to analyze responses to chemotherapy and targeted drugs in high-risk and low-risk groups. In summary, the 7-gene signature associated with SOCE serves as a significant biomarker for evaluating the TME and predicting the prognosis of STAD patients. In addition, it may provide valuable insights for developing effective immunotherapy and chemotherapy regiments for patients with STAD.
Zhang Z
，Wang C
，Shi W
，Wang Z
，Fu W
... -
《Scientific Reports》
ResearchGate
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钛学术
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