Discovery of a novel three-long non-coding RNA signature for predicting the prognosis of patients with gastric cancer

Development and validation of a two glycolysis-related LncRNAs prognostic signature for glioma and in vitro analyses

BACKGROUND

Mounting evidence suggests that there is a complex regulatory relationship between long non-coding RNAs (lncRNAs) and the glycolytic process during glioma development. This study aimed to investigate the prognostic role of glycolysis-related lncRNAs in glioma and their impact on the tumor microenvironment.

METHODS

This study utilized glioma transcriptome data from public databases to construct, evaluate, and validate a prognostic signature based on differentially expressed (DE)-glycolysis-associated lncRNAs through consensus clustering, DE-lncRNA analysis, Cox regression analysis, and receiver operating characteristic (ROC) curves. The clusterProfiler package was applied to reveal the potential functions of the risk score-related differentially expressed genes (DEGs). ESTIMATE and Gene Set Enrichment Analysis (GSEA) were utilized to evaluate the relationship between prognostic signature and the immune landscape of gliomas. Furthermore, the sensitivity of patients to immune checkpoint inhibitor (ICI) treatment based on the prognostic feature was predicted with the assistance of the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Finally, qRT-PCR was used to verify the difference in the expression of the lncRNAs in glioma cells and normal cell.

RESULTS

By consensus clustering based on glycolytic gene expression profiles, glioma patients were divided into two clusters with significantly different overall survival (OS), from which 2 DE-lncRNAs, AL390755.1 and FLJ16779, were obtained. Subsequently, Cox regression analysis demonstrated that all of these lncRNAs were associated with OS in glioma patients and constructed a prognostic signature with a robust prognostic predictive efficacy. Functional enrichment analysis revealed that DEGs associated with risk scores were involved in immune responses, neurons, neurotransmitters, synapses and other terms. Immune landscape analysis suggested an extreme enrichment of immune cells in the high-risk group. Moreover, patients in the low-risk group were likely to benefit more from ICI treatment. qRT-PCR results showed that the expression of AL390755.1 and FLJ16779 was significantly different in glioma and normal cells.

CONCLUSION

We constructed a novel prognostic signature for glioma patients based on glycolysis-related lncRNAs. Besides, this project had provided a theoretical basis for the exploration of new ICI therapeutic targets for glioma patients.

Construction and validation of a glycolysis-related lncRNA signature for prognosis prediction in Stomach Adenocarcinoma

Background: Glycolysis is closely related to the occurrence and progression of gastric cancer (GC). Currently, there is no systematic study on using the glycolysis-related long non-coding RNA (lncRNA) as a model for predicting the survival time in patients with GC. Therefore, it was essential to develop a signature for predicting the survival based on glycolysis-related lncRNA in patients with GC. Materials and methods: LncRNA expression profiles, containing 375 stomach adenocarcinoma (STAD) samples, were obtained from The Cancer Genome Atlas (TCGA) database. The co-expression network of lncRNA and glycolysis-related genes was used to identify the glycolysis-related lncRNAs. The Kaplan-Meier survival analysis and univariate Cox regression analysis were used to detect the glycolysis-related lncRNA with prognostic significance. Then, Bayesian Lasso-logistic and multivariate Cox regression analyses were performed to screen the glycolysis-related lncRNA with independent prognostic significance and to develop the risk model. Patients were assigned into the low- and high-risk cohorts according to their risk scores. A nomogram model was constructed based on clinical information and risk scores. Gene Set Enrichment Analysis (GSEA) was performed to visualize the functional and pathway enrichment analyses of the glycolysis-related lncRNA. Finally, the robustness of the results obtained was verified in an internal validation data set. Results: Seven glycolysis-related lncRNAs (AL353804.1, AC010719.1, TNFRSF10A-AS1, AC005586.1, AL355574.1, AC009948.1, and AL161785.1) were obtained to construct a risk model for prognosis prediction in the STAD patients using Lasso regression and multivariate Cox regression analyses. The risk score was identified as an independent prognostic factor for the patients with STAD [HR = 1.315, 95% CI (1.056-1.130), p < 0.001] via multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were drawn and the area under curve (AUC) values of 1-, 3-, and 5-year overall survival (OS) were calculated to be 0.691, 0.717, and 0.723 respectively. Similar results were obtained in the validation data set. In addition, seven glycolysis-related lncRNAs were significantly enriched in the classical tumor processes and pathways including cell adhesion, positive regulation of vascular endothelial growth factor, leukocyte transendothelial migration, and JAK_STAT signaling pathway. Conclusion: The prognostic prediction model constructed using seven glycolysis-related lncRNA could be used to predict the prognosis in patients with STAD, which might help clinicians in the clinical treatment for STAD.

Construction of a prognostic glycolysis-related lncRNA signature for patients with colorectal cancer

Aerobic glycolysis is a common metabolic phenotype in tumors that helps cancer cells adjust to severe living conditions and can aid metastasis in several types of carcinomas, including colorectal cancer (CRC). Long non-coding RNAs (lncRNAs) can influence tumor biology and have been previously used to assess patients' outcomes and to identify potential therapeutic targets. However, despite the importance of glycolysis-related lncRNAs (GRLs) in the development of CRC, studies on their use as prognostic markers are still limited. Herein, we applied a series of bioinformatic analyses to screen potential prognostic lncRNAs for colorectal cancer. Out of all lncRNAs screened, nine GRLs were selected to constitute a prognostic signature. Based on the signature, two molecular subtypes were classified with distinct prognostic outcomes and excellent diagnostic accuracy (The 1-, 3- and 5-year AUC are 0.756, 0.716, and 0.721, respectively). The prognostic value of this signature was further validated using another cohort. The enriched molecular pathways, immune infiltration, and mutation landscape were also significantly different between the two groups. The different drug sensitivity results between the two groups suggest a potential strategy for precise treatment. Furthermore, we confirmed that AFAP1-AS1 could regulate aerobic glycolysis and metastasis of CRC cells. Overall, we developed a glycolysis-related lncRNA (GRL) signature and suggested that this signature could offer a predictive value and identify potential therapeutic targets for cancer therapy.

Identification of prognostic alternative splicing signature in gastric cancer

Background

Aberrant alternative splicing (AS) events could be viewed as prognostic indicators in a large number of malignancies. This study aims to identify prognostic AS events, illuminate the function of the splicing variants biomarkers and provide reliable evidence for formulating public health strategies for gastric cancer (GC) surveillance.

Methods

RNA-Seq data, clinical information and percent spliced in (PSI) values were available in The cancer genome atlas (TCGA) and TCGA SpliceSeq data portal. A three-step regression method was conducted to identify prognostic AS events and construct multi-AS-based signatures. The associations between prognostic AS events and splicing factors were also investigated.

Results

We identified a total of 1,318 survival-related AS events in GC, parent genes of which were implicated in numerous oncogenic pathways. The final prognostic signatures stratified by seven types of AS events or not stratified performed well in risk prediction for GC patients. Moreover, five signatures based on AA, AD, AT, ES and RI events function as independent prognostic indicators after multivariate adjustment of other clinical variables. Splicing network also showed marked correlation between the expression of splicing factors and PSI value of AS events in GC patients.

Conclusion

Our findings provide a landscape of AS events and regulatory network in GC, indicating that AS events might serve as prognostic biomarkers and therapeutic targets for GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13690-022-00894-3.

Testing lncRNAs signature as clinical stage–related prognostic markers in gastric cancer progression using TCGA database

LncRNA expression can be conducive to gastric cancer (GC) prognosis. The objective of this study is to ascertain five specific lncRNAs involved in tumor progression of GC and their role as prognostic markers to diagnose clinical stage-wise GC. High-throughput RNA sequencing data were obtained from The Cancer Genome Atlas (TCGA) database and performed genome-wide lncRNA expression analysis using edgeR package, Bioconductor.org , and R-statistical computing to analyze differentially expressed lncRNA analysis. Cutoff parameters were FDR < 0.05 and |Log2FC| > 2. Total 351 tumor samples with differentially expressed lncRNAs were divided into group-1 lncRNAs such as AC019117.2 and LINC00941, and group-2 lncRNAs such as LINC02410, AC012317.2, and AC141273.1 by 2:1. The Spearman correlation coefficients ( p < 0.05) and correlation test function (cor.test ()) were performed for lncRNAs as per clinical stage. Cytoscape software was used to construct lncRNA–mRNA interaction networks. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway ( p < 0.05) analysis were conducted using the clusterProfiler package. Kaplan–Meier survival analysis was performed to determine the overall survival of patients based on the expression of five lncRNAs in different clinical stages of GC. AC019117.2 and LINC00941 of group 1 inferred a positive correlation with clinical stages of stage I to stage IV, and their expressions were higher in tumor tissues than normal tissues. On the contrary, LINC02410, AC012317.2, and AC141273.1 of group 2 exhibited a negative correlation with clinical stage, and they exhibited more expression in normal tissues compared to tumor tissues. GO and KEGG pathway analysis reported that AC019117.2 may interact with LINC00941 via ITGA3 and trophoblast glycoprotein (TPBG) to foster tumor progression. Tumor-specific group-1 lncRNAs were conducive to the poor overall survival and exhibited a positive correlation with the clinical stages of stage I to stage IV in GC as per the lncRNA–mRNA networking analysis. These five lncRNAs could be considered as clinically useful lncRNA-based prognostic markers to predict clinical stage-wise GC progression.

A novel 8-genome instability-associated lncRNAs signature predicting prognosis and drug sensitivity in gastric cancer

BACKGROUND

Genome instability lncRNA (GILnc) is prevalently related with gastric cancer (GC) pathophysiology. However, the study on the relationship GILnc and prognosis and drug sensitivity of GC remains scarce.

METHOD

We extracted expression data of 375 GC patients from TCGA cohort and 205 GC patients from GSE26942 cohort. Then, lncRNA was separated from expression data, and systematically characterized the 8 marker lncRNAs using the LASSO method. Next, we constructed a GILnc model (GILnc score) to quantify the GILnc index of each GC patient. Finally, we analyzed the relationship between GILnc score and clinical traits including survival outcomes, TP53, and drug sensitivity of GC.

RESULTS

Based on a computational frame, 205 GILncs in GC has been identified. Then, a 8 GILncs was successfully established to predict overall survival in GC patients based on LASSO analysis, divided GC samples into high GILnc score and low GILnc score groups with significantly different outcome and was validated in multiple independent patient cohorts. Furthermore, GILnc model is better than the prediction performance of two recently published lncRNA signatures, and the high GILnc score group was more sensitive to mitomycin. Besides, the GILnc score has greater prognostic significance than TP53 mutation status alone and is capable of identifying intermediate subtype group existing with partial TP53 functionality in TP53 wild-type patients. Finally, GILnc signature as verified in GSE26942.

CONCLUSION

We applied bioinformatics approaches to suggest that a 8 GILnc signature could serve as prognostic biomarkers, and provide a novel direction to explore the pathogenesis of GC.

Survival-Related lncRNA Landscape Analysis Identifies LINC01614 as an Oncogenic lncRNA in Gastric Cancer

Background: Long non-coding RNAs (lncRNAs) reportedly play important roles in biomarker and tumorigenesis of gastric cancer (GC). This study aimed to determine the potential application of prognostic lncRNA signature and identified the role of LINC01614 in carcinogenesis in GC.

Material and Methods: Data accessed from the Cancer Genome Atlas database was used to construct a lncRNA signature. Joint effect analysis of the signature and clinical parameters was performed to verify the clinical value of the signature. Co-expression analysis was conducted for prognostic lncRNAs and protein-coding genes. Moreover, the relative expression of LINC01614 was validated in GC tissues and cell lines. In vitro and in vivo experiments were conducted to analyze the biological functions of the newly identified gene in GC cells.

Results: A seven-lncRNA (LINC01614, LINC01537, LINC01210, OVAAL, LINC01446, CYMP-AS1, and SCAT8) signature was identified as a promising prognostic signature in GC. Results indicated that the seven-lncRNA was involved in tumorigenesis and progression pathways. LINC01614 expression was identified and found to be upregulated in GC tissues and cells. The study findings revealed that LINC01614 promoted cell proliferation, migration, invasion, and epithelial-mesenchymal transition. Knockdown of LINC01614 arrested cell cycle distribution at the G2/M phase. Further, LINC01614 also promoted tumor growth in vivo.

Conclusion: We developed an independent seven-lncRNA biomarker for prognostic prediction and identified LINC01614 as an oncogenic lncRNA in GC.

Genomic instability-associated lncRNA signature predicts prognosis and distinct immune landscape in gastric cancer

Background

Characterized by multiple features, genomic stability-related markers, such as microsatellite instability (MSI), were regulated as an important predictor of chemotherapy and immunity responses in cancer treatment. The aim of our study was to identify a genomic instability-associated long non-coding RNA (lncRNA) signature to help predict the survival and therapy response of gastric cancers (GCs).

Methods

We used RNA sequencing and single nucleotide variant (SNV) data from The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) datasets to explore genomic instability-associated lncRNAs. Hierarchical cluster analyses of 197 differentially expressed genomic instability-associated lncRNAs were performed to separate GC patients into two groups, namely, the genomically unstable (GU)-like group and the genomically stable (GS)-like group.

Results

Cox regression analysis was conducted to finally identify six lncRNAs (LINC02678, HOXA10-AS, RHOXF1-AS1, AC010789.1, LINC01150, and TGFB2-AS1) with independent prognostic value to establish the genomic instability-associated lncRNA signature (GILncSig). Based on the SNV analysis, GILncSig was correlated with accumulation of gene mutation counts. Further comparisons between different risk score groups were performed to assess chemotherapy drug sensitivity and immune landscape variations.

Conclusions

Our study not only revealed the genomic instability-associated lncRNAs in GCs, but provided a key method and resource for further studies of the role of these lncRNAs play, and introduced a potential new way to identify genomic instability-associated cancer biomarkers.

Applying immune-related lncRNA pairs to construct a prognostic signature and predict the immune landscape of stomach adenocarcinoma

Background: Long noncoding RNAs (lncRNAs) are associated with the survival of cancer patients. We constructed an immune-related lncRNA (irlncRNA) pair signature for stomach adenocarcinoma (STAD).Research design and methods: irlncRNAs were identified via coexpression analysis with immune-related genes. Differentially expressed irlncRNAs (DEirlncRNAs) were paired. Least absolute shrinkage and selection operator (LASSO) and multivariate Cox proportional hazards regression methods were used to construct the signature. We calculated the area under the receiver operating characteristic (ROC) curve and determined the best cutoff value according to the Akaike information criterion (AIC). Patients were divided into high - and low-risk groups, and differences in immune cell infiltration, tumor mutation burden (TMB) and drug treatment effects between the groups were explored according to the risk score.Results: An 8-irlncRNA-pair signature was constructed and proven to be a strong prognosis predictor in STAD patients through external verification. Moreover, the risk score was identified as an independent prognostic factor. There were significant differences in immune cell infiltration and the response to several drug treatments between patients with high and low risk scores, and the risk score was negatively correlated with TMB.Conclusions: The signature consisting of 8 irlncRNA pairs showed good prognostic predictive value.

Development of an Immune-Related LncRNA Prognostic Signature for Glioma

Introduction

Glioma is the most common primary cancer of the central nervous system with dismal prognosis. Long noncoding RNAs (lncRNAs) have been discovered to play key roles in tumorigenesis in various cancers, including glioma. Because of the relevance between immune infiltrating and clinical outcome of glioma, identifying immune-related lncRNAs is urgent for better personalized management.

Materials and methods

Single-sample gene set enrichment analysis (ssGSEA) was applied to estimate immune infiltration, and glioma samples were divided into high immune cell infiltration group and low immune cell infiltration group. After screening differentially expressed lncRNAs in two immune groups, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune-related prognostic signature. Additionally, we explored the correlation between immune infiltration and the prognostic signature.

Results

A total of 653 samples were appropriate for further analyses, and 10 lncRNAs were identified as immune-related lncRNAs in glioma. After univariate Cox regression and LASSO Cox regression analysis, six lncRNAs were identified to construct a prognostic signature for glioma, which could be taken as independent prognostic factors in both univariate and multivariate Cox regression analyses. Moreover, risk score was significantly correlated with all the 29 immune-related checkpoint expression (p < 0.05) in ssGSEA except neutrophils (p = 0.43).

Conclusion

The study constructed an immune-related prognostic signature for glioma, which contributed to improve clinical outcome prediction and guide immunotherapy.