Functional Relevance of the Long Intergenic Non-Coding RNA Regulator of Reprogramming (Linc-ROR) in Cancer Proliferation, Metastasis, and Drug Resistance

Molecular Mechanisms and Clinical Implications of Noncoding RNAs in Cancer

Noncoding RNAs (ncRNAs), which include small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), are RNA molecules that arise from genomic regions without protein-coding potential and display a variety of mechanisms and functions by regulating gene expression at the transcriptional, RNA processing, and translational levels and participating in virtually all cellular processes [...].

Therapeutic significance of long noncoding RNAs in estrogen receptor-positive breast cancer

About 70% of cases of breast cancer are compromised by Estrogen-positive breast cancer. Through its regulation of several processes, including cell proliferation, cell cycle progression, and apoptosis, Estrogen signaling plays a pivotal role in the genesis and progression of this particular kind of breast cancer. One of the best treatment strategies for treating Estrogen-positive breast cancer is blocking Estrogen signaling. However, patients' treatment failure is mainly caused by the emergence of resistance and metastases, necessitating the development of novel therapeutic targets. Numerous studies have shown long noncoding RNAs (lncRNAs) to play a role in Estrogen-mediated carcinogenesis. These lncRNAs interact with co-regulators and the Estrogen signaling cascade components, primarily due to Estrogen activation. Vimentin and E-cadherin are examples of epithelial-to-mesenchymal transition markers, and they regulate genes involved in cell cycle progression, such as Cyclins, to affect the growth, proliferation, and metastasis of Estrogen-positive breast cancer. Furthermore, a few of these lncRNAs contribute to developing resistance to chemotherapy, making them more desirable targets for enhancing results. Thus, to shed light on the creation of fresh approaches for treating this cancer, this review attempts to compile recently conducted studies on the relationship between lncRNAs and the advancement of Estrogen-positive breast cancer.

Decoding the regulatory landscape of lncRNAs as potential diagnostic and prognostic biomarkers for gastric and colorectal cancers

Colorectal cancer (CRC) and gastric cancer (GC) are major contributors to cancer-related mortality worldwide. Despite advancements in understanding molecular mechanisms and improved drug treatments, the overall survival rate for patients remains unsatisfactory. Metastasis and drug resistance are major challenges contributing to the high mortality rate in both CRC and GC. Recent research has shed light on the role of long noncoding RNAs (lncRNAs) in the development and progression of these cancers. LncRNAs regulate gene expression through various mechanisms, including epigenetic modifications and interactions with microRNAs (miRNAs) and proteins. They can serve as miRNA precursors or pseudogenes, modulating gene expression at transcriptional and post-transcriptional levels. Additionally, circulating lncRNAs have emerged as non-invasive biomarkers for the diagnosis, prognosis, and prediction of drug therapy response in CRC and GC. This review explores the intricate relationship between lncRNAs and CRC/GC, encompassing their roles in cancer development, progression, and chemoresistance. Furthermore, it discusses the potential of lncRNAs as therapeutic targets in these malignancies. The interplay between lncRNAs, miRNAs, and tumor microenvironment is also highlighted, emphasizing their impact on the complexity of cancer biology. Understanding the regulatory landscape and molecular mechanisms governed by lncRNAs in CRC and GC is crucial for the development of effective diagnostic and prognostic biomarkers, as well as novel therapeutic strategies. This review provides a comprehensive overview of the current knowledge and paves the way for further exploration of lncRNAs as key players in the management of CRC and GC.

A novel sphingolipid metabolism-related long noncoding RNA signature predicts the prognosis, immune landscape and therapeutic response in pancreatic adenocarcinoma

Sphingolipid metabolism affects prognosis and resistance to immunotherapy in patients with cancer and is an emerging target in cancer therapy with promising diagnostic and prognostic value. Long noncoding ribonucleic acids (lncRNAs) broadly regulate tumour-associated metabolic reprogramming. However, the potential of sphingolipid metabolism-related lncRNAs in pancreatic adenocarcinoma (PAAD) is poorly understood. In this study, coexpression algorithms were employed to identify sphingolipid metabolism-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a sphingolipid metabolism-related lncRNA signature (SMLs). The prognostic predictive stability of the SMLs was validated using Kaplan-Meier. Univariate and multivariate Cox, receiver operating characteristic (ROC) and clinical stratification analyses were used to comprehensively assess the SMLs. Gene set variation analysis (GSVE), gene ontology (GO) and tumor mutation burden (TMB) analysis explored the potential mechanisms. Additionally, single sample gene set enrichment analysis (ssGSEA), ESTIMATE, immune checkpoints and drug sensitivity analysis were used to investigate the potential predictive function of the SMLs. Finally, an SMLs-based consensus clustering algorithm was utilized to differentiate patients and determine the suitable population for immunotherapy. The results showed that the SMLs consists of seven sphingolipid metabolism-related lncRNAs, which can well determine the clinical outcome of individuals with PAAD, with high stability and general applicability. In addition, the SMLs-based consensus clustering algorithm divided the TCGA-PAAD cohort into two clusters, with Cluster 1 showing better survival than Cluster 2. Additionally, Cluster 1 had a higher level of immune cell infiltration than Cluster 2, which combined with the higher levels of immune checkpoints in Cluster 1 suggests that Cluster 1 is more consistent with an immune 'hot tumor' profile and may respond better to immune checkpoint inhibitors (ICIs). This study offers new insights regarding the potential role of sphingolipid metabolism-related lncRNAs as biomarkers in PAAD. The constructed SMLs and the SMLs-based clustering are valuable tools for predicting clinical outcomes in PAAD and provide a basis for clinical selection of individualized treatments.