Biology subjects

Vindal, V.

Publications and source records attributed to Vindal, V..

8 recordsLinked to original sources

bioRxiv2025

Estimating drivers of breast tissue transitions from normal to tumor state

Tumor tissues are characterized by dysregulated gene expression patterns leading to altered cellular pathways and molecular functions as a result of their transition from normal to tumor state. Further, tumor-adjacent normal tissues (TANTs), utilized as a control in cancer research, are not molecularly normal and differ from healthy normal tissues. These TANTs represent a distinctive transitional state betwixt normal and tumor states. However, the mechanism underlying this state transition, expression dysregulation, and perturbed regulation remain largely unexplored and hence elusive. Herein, the transitions of breast tissues from normal and TANT to tumor states were modeled using gene expression and regulation data to estimate key drivers underlying these transitions. As a result, we identified 645 shared driver genes underlying the transitions of breast tissues from the healthy normal state to the adjacent normal and tumor states. Besides, we identified 635 shared driver genes underlying the transitions of TANTs to different subtypes. When we intersected both lists of shared driver genes, a total of 615 commonly shared driver genes across the state transitions were observed. Subsequently, functional annotations of these driver genes revealed their involvement in the growth and maintenance-related activity of cells. Additionally, key pathways associated with cancer pathogenesis, such as Wnt signaling, Notch signaling, NF-kappa B signaling, and PD-L1 expression and PD-1 checkpoint pathway in cancer, were found significantly enriched with these shared driver genes. Thus, the shared driver genes identified across tissue transitions provide ways forward to devise more efficient diagnostic and therapeutic strategies for early and effective disease management.

bioinformatics↗

bioRxiv2025

Exploring vulnerable building blocks in protein-protein interaction networks of breast tumor and adjacent normal tissues

Tumor-adjacent normal tissues (TANTs) histologically and morphologically look normal and are commonly used as a control in patient-based cancer studies. Previous studies have revealed that TANTs present a unique transitional state between healthy normal and tumor tissues. However, there is little or no knowledge about the landscape of protein-protein interactions (PPIs) in TANTs and how they differ from the tumor tissues. Here, we integrate the PPI data mapped onto the differentially expressed genes in TANTs and tumor tissues compared to healthy normal tissues to comprehensively construct and analyze the PPI networks of TANTs and breast tumor tissues (viz., Luminal A, Luminal B, Her2, Basal, and Normal-Like). First, these PPI networks were analyzed using network influence and vulnerability analyses from the NetVA R package. Consequently, our study revealed 134 vulnerable proteins (VPs), 21 vulnerable protein pairs (VPPs), and 94 influential proteins (IPs) commonly shared across all six tissue networks. Further, we identified a set of 34 proteins as common hubs and another set of seven proteins as common bottlenecks across all six tissue networks. Next, these VPs, IPs, hubs, and bottlenecks were investigated for their associations with various diseases, including cancers, and found to share a significant number of well-known cancer-associated proteins, viz., AR, BRCA1, ERBB2, FN1, FOXA1, JUN, MKI67, and NRAS. Thus, by applying network vulnerability, influence, and gene-disease association-based analyses, we suggest lists of known and novel candidates along with their associated protein complexes potentially involved in breast cancer tumorigenesis and present across TANTs and different breast cancer subtypes.

bioinformatics↗

bioRxiv2025

Decoding DNA methylation and non-coding RNAs mediated regulatory landscape of breast tumor and adjacent normal tissues

Tumorigenesis not only involves perturbations of mRNAs but also microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and DNA methylation (DNAm). Additionally, the tumor-adjacent normal tissues (TANTs) that are used as controls in cancer studies are not completely normal and, if explored systematically, can help to understand tumorigenesis better. In this regard, expression and DNAm profiles of breast tumor tissues (viz., Luminal-A, Luminal-B, Her2+, Basal-like, and Normal-like) and TANTs were used to construct and analyze their integrative networks. These networks included regulations among genes, transcription factors, lncRNAs, and miRNAs, along with gene/lncRNA methylations. This followed the identification of candidate gene PCLAF having transitivity value one across all six tissue networks. Further, 503 common hubs (388 protein-coding, 100 miRNAs, and 15 lncRNAs), including seven methylated genes (SPI1, STAT5A, RORC, SPDEF, ELF5, HOXB2, and RUNX3), and 149 common bottlenecks (145 protein-coding, three miRNAs, and one lncRNA), including three methylated genes (SPI1, SPDEF, and RUNX3) and one methylated lncRNA (PART1), were identified. Moreover, 145 nodes, including three methylated genes (SPI1, SPDEF, and RUNX3) and three miRNAs (hsa-miR-34a, hsa-miR-149, and hsa-miR-141), were identified as both hubs and bottlenecks (HBs) across all six tissues. However, none of the lncRNAs were found HBs therein. Genes and lncRNAs with higher degrees, betweenness, eigenvector centrality, and pagerank were found more preferably methylated than others. Further, pathway analysis of hubs and bottlenecks across all six tissues revealed their significant involvement in the pathway of Transcriptional misregulation in cancer. Overall, these findings uncover new insights into the epigenetic mechanisms of tumorigenesis.

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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).

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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↗

bioRxiv2023

NetVA: An R Package for Network Vulnerability and Influence Analysis

In biological network analysis, identifying key molecules plays a decisive role in the development of potential diagnostic and therapeutic candidates. Among various approaches of network analysis, network vulnerability analysis is quite important, as it assesses significant associations between topological properties and the functional essentiality of a network. Further, some node centralities are also used to screen out key molecules. Among these node centralities, escape velocity centrality (EVC), and its extended version (EVC+) outperform others, viz., Degree, Betweenness, and Clustering coefficient. Keeping this in mind, we aimed to develop a first-of-its-kind R package named NetVA, which analyzes networks to identify key molecular players through network vulnerability and EVC+-based approaches. To demonstrate the application and relevance of our package in network analysis, previously published and publicly available protein-protein interactions (PPIs) data of human breast cancer were analyzed. This resulted in identifying some most important proteins. These included essential proteins, non-essential proteins, hubs, and bottlenecks, which play vital roles in breast cancer development. Thus, the NetVA package, available at https://github.com/kr-swapnil/NetVA with a detailed tutorial to download and use, assists in predicting potential candidates for therapeutic and diagnostic purposes by exploring various topological features of a disease-specific PPIs network.

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bioRxiv2022

BCLncRDB: A comprehensive database of LncRNAs associated with breast cancer

MotivationBreast cancer, the most common cancer in women, is characterized by high morbidity and mortality worldwide. Recent evidence has shown that long non-coding RNAs (lncRNAs) play a crucial role in the development and progression of breast cancer. Despite this, no database exists primarily for lncRNAs associated with only breast cancer. ResultsWe developed BCLncRDB, a manually curated, comprehensive database of lncRNAs associated with breast cancer. For this, we collected, processed, and analyzed data on breast cancer-associated lncRNAs from different sources, including published literature and TCGA. Currently, our database contains 5,279 unique breast cancer-lncRNA associations. It has the following features: (I) Differentially expressed and methylated lncRNAs, (II) Stage and subtype-specific lncRNAs, and (III) Drugs, Subcellular localization, Sequence, and Chromosome information. Thus, the BCLncRDB provides a dedicated platform for exploring breast cancer-related lncRNAs to advance and support the ongoing research on this disease. Availability and implementationThe database BCLncRDB is publicly available for use at http://sls.uohyd.ac.in/new/bclncrdb. Contactvaibhav@uohyd.ac.in

bioinformatics↗

bioRxiv2022

Architecture and topologies of gene regulatory networks associated with breast cancer, adjacent normal, and normal tissues

Most cancer studies employ adjacent normal tissues to tumors (ANTs) as controls, which are not completely normal and represent a pre-cancerous state. However, the regulatory landscape of ANTs and how it differs from tumor and non-tumor-bearing normal tissues is largely unexplored. Among cancers, breast cancer is the most commonly diagnosed cancer and a leading cause of death in women worldwide, with a lack of sufficient treatment regimens due to various reasons. Hence, we aimed to gain deeper insights into normal, pre-cancerous, and cancerous regulatory systems of the breast tissues towards the identification of ANT and subtype-specific candidate genes. For this, we constructed and analyzed eight gene regulatory networks (GRNs), including five different subtypes (viz. Basal, Her2, LuminalA, LuminalB, and Normal-Like), one ANT, and two normal tissue networks. Whereas several topological properties of these GRNs enabled us to identify tumor-related features of ANT; escape velocity centrality (EVC+) identified 24 functionally significant common genes, including well-known genes such as E2F1, FOXA1, JUN, BRCA1, GATA3, ERBB2, and ERBB3 across different subtypes and ANT. Similarly, the EVC+ also helped us to identify tissue-specific key genes (Basal: 18, Her2: 6, LuminalA: 5, LuminalB: 5, Normal-Like: 2, and ANT: 7). Additionally, differential correlation along with functional, pathway, and disease annotations highlighted the cancer-associated role of these genes. In a nutshell, the present study revealed ANT and subtype-specific regulatory features and key candidate genes which can be explored further using in vitro and in vivo experiments for better and effective disease management at an early stage.

bioinformatics↗