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Due to the lack of specific symptoms, characteristic diagnostic markers and effective comprehensive treatment, gallbladder cancer (GBC) is currently considered one of the most malignant abdominal tumors. With the rapid development of biological technologies, long non‑coding RNAs (lncRNAs), once regarded as transcriptional junk, have been demonstrated to participate in almost the whole process of the central dogma of molecular biology. The central dogma deals with the transfer of sequential information at the level of individual residues. LncRNAs have an effect on multiple cancer types. However, evidence of dysregulated lncRNA functions in GBC is limited. In the present review, the regulatory mechanisms of lncRNA function on gene expression were examined, including epigenetic modification, transcriptional regulation and post‑translational modulation. These mechanisms are strongly associated with tumor development and metastasis. Next, it was summarized how lncRNAs affect GBC diverse malignant phenotypes through various mechanisms. Moreover, predictions of lncRNA interactions with other functional molecules in malignancies were made using several valuable databases, including crosstalk between lncRNA and DNA, mRNA, microRNA, and protein. Additionally, several potential therapeutic methods targeting pathological lncRNAs in tumors were identified. Finally, perspectives about lncRNA research and applications in GBC were presented in the current study, including viewpoints of coding potential of lncRNAs and feasible usage of micropeptides encoded by lncRNAs; roles of lncRNAs in tumor cell metabolic reprogramming and tumor microenvironment; and function of lncRNAs as possible biomarkers and therapeutic targets for improving GBC diagnosis, treatment and prognosis.

Long non-coding RNAs (lncRNAs) may play a role in oxidative stress by altering the tumor microenvironment, thereby affecting pancreatic cancer progression. There is currently limited information on oxidative stress-related lncRNAs as novel prognostic markers of pancreatic cancer. Gene expression and clinical data of patients with pancreatic cancer were downloaded from The Cancer Genome Atlas (TCGA-PAAD) and the International Cancer Genome Consortium (ICGC-PACA) database. A weighted gene co-expression network analysis (WGCNA) was constructed to identify genes that were differentially expressed between normal and tumor samples. Based on the TCGA-PAAD cohort, a prediction model was established using lasso regression and Cox regression. The TCGA-PAAD and ICGC-PACA cohorts were used for internal and external validation, respectively. Furthermore, a nomogram based on clinical characteristics was used to predict mortality of patients. Differences in mutational status and tumor-infiltrating immune cells between risk subgroups were also explored and model-based lncRNAs were analyzed for potential immune-related therapeutic drugs. A prediction model for 6-lncRNA was established using lasso regression and Cox regression. Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves indicated that patients with lower risk scores had a better prognosis. Combined with Cox regression analysis of clinical features, risk score was an independent factor predicting overall survival of patients with pancreatic cancer in both the TCGA-PAAD and ICGC-PACA cohorts. Mutation status and immune-related analysis indicated that the high-risk group had a significantly higher gene mutation rate and a higher possibility of immune escape, respectively. Furthermore, the model genes showed a strong correlation with immune-related therapeutic drugs. A pancreatic cancer prediction model based on oxidative stress-related lncRNA was established, which may be used as a biomarker related to the prognosis of pancreatic cancer to evaluate the prognosis of pancreatic cancer patients., (© 2023. The Author(s).)

Background: Due to their inherent role in cell function, long non-coding ribonucleic acids (lncRNAs) mediate changes in the microenvironment, and thereby participate in the development of cellular senescence., Aims: This study aimed to identify cellular senescence-related lncRNAs that could predict the prognosis of liver cancer., Methods and Results: Gene expression and clinical data were downloaded from the UCSC Xena platform, ICGC, and TCGA databases. Cox regression and LASSO regression were used to establish a cellular senescence-related lncRNA model. ROC curves and Kaplan-Meier survival curves were then constructed to predict patient prognosis. Cox regression analysis and clinical characteristics were used to evaluate the capability of the model. Tumor mutational burden and tumor-infiltrating immune cell analyses were subsequently performed in the risk subgroups and the samples in the entire cohort were reclustered. Finally, potential small molecule immune-targeted drugs were identified based on the model. The cellular senescence-related prognostic model that was constructed based on AGAP11 and FAM182B. Along with the results of Cox regression and Lasso regression, the risk score was found to be an independent factor for predicting overall survival in cohorts. In the subgroup analysis, the prognosis of the low-risk group in each cohort was significantly higher than that of the high-risk group; the area under temporal ROC curves and clinical ROC curves were all greater than 0.65, respectively. C-index shows that the risk scores are greater than 0.6, showing the stability of the model. The high-risk group demonstrated lower tumor microenvironment and higher tumor mutational burden scores, further verifying the reliability of the model grouping results. Analysis of tumor-infiltrating immune cells indicated that CD8+ and γδ T cells were more abundant among patients in the low-risk group; cluster reorganization indicated that the two groups had different prognoses and proportions of immune cells. The p value of potential drugs predicted based on the expression of model lncRNAs were all less than .05, demonstrating the potential of model lncRNAs as therapeutic targets to some extent., Conclusion: A prognostic model based on cellular senescence-associated lncRNAs was established and this may be used as a potential biomarker for the prognosis assessment of liver cancer patients., (© 2023 The Authors. Cancer Reports published by Wiley Periodicals LLC.)

Emerging evidence has suggested that N 6 ‑methyladenosine (m6A) modification, a typical RNA methylation modification, controls the fate of modified transcripts and is involved in the pathogenesis of various human diseases, such as metabolic disorders, nephropathology, osteoarthritis and malignant tumours. Long noncoding RNAs (lncRNAs), transcripts of >200 nt in length, have also been indicated to be involved in various diseases by participating in processes such as epigenetic modifications, transcriptional alternations and posttranslational regulation. Recent studies revealed that lncRNAs were widely modified by m6A, which has a critical role in various cellular processes that are associated with numerous disorders, particularly human cancers. The present review first examined functions of m6A modification of lncRNAs, including changing the lncRNA structure, mediating transcriptional regulation, affecting mRNA precursor splicing, and regulating lncRNA stability and translation. Furthermore, the regulatory mechanisms of m6A‑modified lncRNAs in cancers were summarized and the up‑to‑date detection methods and prediction tools for identifying m6A sites on lncRNAs were presented. In addition, viewpoints on potential future directions in the field were discussed, including more accurate detection methods, roles of lncRNAs‑encoded micropeptides in cancers, the relationship between m6A‑modified lncRNAs and the tumour microenvironment, and m6A‑modified lncRNAs as potential biomarkers and therapeutic targets in human cancer.