Thippana, M. | bioRxiv Science

bioRxiv2025

LCLNCRdb: A Comprehensive Resource for Investigating long non-coding RNAs in Lung Cancer

Lung cancer is a primary cause of death worldwide, accounting for a substantial number of mortalities. It involves several molecular mechanisms that are influenced by long non-coding RNAs (lncRNAs), a specific types of RNA molecules that do not code for proteins. Several research have revealed the importance of long non-coding RNAs (lncRNAs) in the initiation, progression, and development of resistance to lung cancer therapy. However, there are no centralized web resources or databases that collect and integrate information regarding lung cancer associated lncRNAs. This led to the development of the LCLNCRdb, a manually curated database that includes data from various sources, such as published research articles, and The Cancer Genome Atlas (TCGA) data portal. This database contains detailed information on 1102 lncRNAs that have differential expression patterns in lung cancer patients, such as lncRNA name, entrez ID, Ensemble ID, HGNC ID, NONCODE ID, lung cancer type, source, lncRNA expression pattern, experimental techniques, network analysis, and survival analysis details. The database offers a user-friendly platform for browsing, retrieving, and downloading data, and it features a dedicated submission page for researchers to share newly identified lncRNAs related to lung cancer. LCLNCRdb aims to enhance our knowledge of lncRNA deregulation in lung cancer and provides a valuable and timely resource for lncRNA research. The database is freely accessible at (https://dbtcmi.in/tools/lclncrdb/main.html).

bioinformatics↗

bioRxiv2024

Prioritization of Lung Cancer Candidate Genes using Moment of Inertia Tensor Analysis

A variety of factors contribute to the complexity of lung cancer progression. To comprehend the disease, candidate genes must be investigated. Present study aimed to employ an alignment-free method to prioritize candidate genes based on the physicochemical properties of the amino acids. It uses the moment of inertia tensor that measures the mass distribution around an axis of rotation, to compute the rotational energy and angular momentum of amino acids in protein sequences. The computed features were compared to those of established lung cancer genes, leading to the identification of 26 candidate genes with a high degree of similarity. These genes participate in critical biological processes that regulate the mitotic cell cycle and cell development. The prognostic significance of these genes was also assessed and four genes (IL1A, CDC25C, IL4R, and TGFBR1) were found to be associated with poor survival. Additionally, the role of prioritized genes and potential drugs that target these genes in other cancer types was also examined. Our method will help to discover new biomarkers and intervention strategies for lung cancer.

bioinformatics↗