A four-lncRNA signature for predicting prognosis of recurrence patients with gastric cancer

Novel histone acetylation-related lncRNA signature for predicting prognosis and tumor microenvironment in esophageal carcinoma

Histone acetylation is one of the most common epigenetic modifications and plays a crucial role in tumorigenesis. However, the prognostic significance of histone acetylation-related lncRNAs (HARlncRNAs) in esophageal carcinoma (ESCA) is not well understood. A total of 653 differentially expressed lncRNAs (DElncRNAs) were identified between 162 ESCA tissues and 11 normal tissues in the TCGA database, and 7 of them were correlated with acetylation regulators. We employed univariate Cox regression analysis, combining it with clinical prognosis information, to select 3 prognostic-related HARlncRNAs for further analysis. Subsequently, we used LASSO regression analysis to construct a risk signature for ESCA and identified C21orf62-AS1 and SSTR5.AS1 as potential biomarkers for the prognosis of ESCA patients. Based on the risk score calculated using the risk signature, we categorized patients into high- and low-risk groups. We identified the risk score as an independent risk factor and validated it in the training, test, and GSE53624 datasets. Additionally, patients categorized by their risk scores exhibited distinct immune statuses, tumor mutation burdens, responses to immunotherapy, and drug sensitivities.

Development of a Novel Pyroptosis-Associated lncRNA Biomarker Signature in Lung Adenocarcinoma

Pyroptosis is a novel type of cell death observed in various diseases. Our study aimed to investigate the relationship between pyroptosis-associated-long non-coding RNAs (lncRNAs), immune infiltration, and expression of immune checkpoints in the setting of lung adenocarcinoma and the prognostic value of pyroptosis-related lncRNAs. RNA-seq transcriptome data and clinical information from The Cancer Genome Atlas (TCGA) were downloaded, and consensus clustering analysis was used to separate the samples into two groups. Least absolute shrinkage and selection operator (LASSO) analyses were conducted to construct a risk signature. The association between pyroptosis-associated lncRNAs, immune infiltration, and expression of immune checkpoints were analysed. The cBioPortal tool was used to discover genomic alterations. Gene set enrichment analysis (GSEA) was utilized to investigate downstream pathways of the two clusters. Drug sensitivity was also examined. A total of 43 DEGs and 3643 differentially expressed lncRNAs were identified between 497 lung adenocarcinoma tissues and 54 normal samples. A signature consisting of 11 pyroptosis-related lncRNAs was established as prognostic for overall survival. Patients in the low-risk group have a significant overall survival advantage over those in the high-risk group in the training group. Immune checkpoints were expressed differently between the two risk groups. Risk scores were validated to develop an independent prognostic model based on multivariate Cox regression analysis. The area under time-dependent receiver operating characteristic curve (AUC of the ROC) at 1-, 3-, and 5-years measured0.778, 0.757, and 0.735, respectively. The high-risk group was more sensitive to chemotherapeutic drugs than the low-risk group. This study demonstrates the association between pyroptosis-associated lncRNAs and prognosis in lung adenocarcinoma and enables a robust predictive signature of 11 lncRNAs to inform overall survival.

DARS-AS1 modulates cell proliferation and migration of gastric cancer cells by regulating miR-330-3p/NAT10 axis

The long noncoding RNA DARS-AS1 was aberrantly expressed and participated in several human cancer progressions, whereas whether DARS-AS1 is involved in human gastric cancer remains unclear. This study aimed to investigate the influence of DARS-AS1 on gastric cancer progression and explore the potential regulatory network of DARS-AS1/miR-330-3p/NAT10. The expression levels of DARS-AS1, miR-330-3p, and NAT10 were measured by quantitative real-time polymerase chain reaction. The CCK-8 assay and Transwell assay were used to determine the cell viability, migration, and invasion capacities, respectively. The target association between miR-330-3p and DARS-AS1 or NAT10 was confirmed using a luciferase reporter assay. In result, DARS-AS1 levels were elevated in tumor tissues and associated with shorter overall survival in patients with gastric cancer. Knockdown of DARS-AS1 could hamper cell viability, migration, and invasion in gastric cancer cells. DARS-AS1 acts as a competitive endogenous RNA to regulate the NAT10 expression by sponging miR-330-3p in gastric cancer cells. In conclusion, DARS-AS1 was elevated in gastric cancer, and DARS-AS1/miR-330-3p/NAT10 signaling offered some new horizons for predicting prognosis and a novel therapeutic method for the treatment of gastric cancer.

Prognostic Roles of ceRNA Network-Based Signatures in Gastrointestinal Cancers

Gastrointestinal cancers (GICs) are high-incidence malignant tumors that seriously threaten human health around the world. Their complexity and heterogeneity make the classic staging system insufficient to guide patient management. Recently, competing endogenous RNA (ceRNA) interactions that closely link the function of protein-coding RNAs with that of non-coding RNAs, such as long non-coding RNA (lncRNA) and circular RNA (circRNA), has emerged as a novel molecular mechanism influencing miRNA-mediated gene regulation. Especially, ceRNA networks have proven to be powerful tools for deciphering cancer mechanisms and predicting therapeutic responses at the system level. Moreover, abnormal gene expression is one of the critical breaking events that disturb the stability of ceRNA network, highlighting the role of molecular biomarkers in optimizing cancer management and treatment. Therefore, developing prognostic signatures based on cancer-specific ceRNA network is of great significance for predicting clinical outcome or chemotherapy benefits of GIC patients. We herein introduce the current frontiers of ceRNA crosstalk in relation to their pathological implications and translational potentials in GICs, review the current researches on the prognostic signatures based on lncRNA or circRNA-mediated ceRNA networks in GICs, and highlight the translational implications of ceRNA signatures for GICs management. Furthermore, we summarize the computational approaches for establishing ceRNA network-based prognostic signatures, providing important clues for deciphering GIC biomarkers.