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Thippana, M.

Publications and source records attributed to Thippana, M..

3 recordsLinked to original sources

A distal CTCF-binding site drives MYC expression plasticity in a negative feed-forward loop

Oncogene expression heterogeneity systemically diversifies cancer cell phenotypes, enabling selection and cancer evolution. Here we document that the level of MYC expression variation is coordinated by a negative feed-forward loop involving the non-coding CCAT1 eRNA and the MYC gating process. While CCAT1 eRNA indirectly antagonizes MYC gating by promoting MYC transcriptional initiation and elongation, the MYC protein promotes gated MYC expression in a feedback loop by inhibiting both MYC transcription and CCAT1 expression. This entire process is coordinated by a CTCF binding site positioned within CCAT1 at a distal, oncogenic super-enhancer, which functions as a master switch by coordinating both CCAT1 and gated MYC expression to diversify cells toward both low and high MYC levels, as determined by heterogeneity metrics. As hallmarks of this principle are frequent in breast cancer and colorectal tumors, the dynamics of this new principle may underlie transitions between therapy-resistant low-MYC, and proliferative high-MYC tumor cells. Highlights- Transcriptional rate regulates MYC gating frequency - CCAT1 expression indirectly inhibits MYC gating by promoting MYC transcription - High MYC protein levels promote gated expression by inhibiting both MYC transcription and CCAT1 expression - These features are controlled by a single CTCF binding in the distant super-enhancer to drive expression plasticity in colorectal cancer cells

cancer biology↗

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↗

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↗