Biology subjects

T, D.

Publications and source records attributed to T, D..

5 recordsLinked to original sources

bioRxiv2026

Transcriptomics and mutational analysis to screen immunogenic neoantigen peptides and Patient stratification based on immune subtypes for TNBC

Triple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous subtype with limited therapeutic options. In this study, we performed an integrative analysis of TNBC genomics data, including gene expression, somatic mutations, copy number alterations, survival outcomes, immune profiling, and clustering, to identify potential neoantigens, patient populations suitable for vaccination, and biomarkers for evaluating vaccine efficacy. This Integrated analysis identified POSTN and CAP1 as tumor-specific antigens. Incorporation of TNBC-specific mutations into the screened wild-type antigens led to the identification of three neoantigenic peptides with high potential for vaccine development. Immune subtyping stratified TNBC patients into four distinct subtypes, among which IS1 and IS3 were characterized by poor immune infiltration, lower mutation burden, and unfavorable prognosis, whereas IS2 and IS4 exhibited enhanced immune activity and better clinical outcomes. A vaccine incorporating the identified neoantigen peptides may potentially remodel the immune landscape of immune-cold subtypes (IS1 and IS3), converting them into immune-enriched phenotypes through vaccine-induced immune stimulation. Furthermore, weighted gene co-expression network analysis identified ten immune-related biomarkers from the blue and gray modules that were significantly associated with improved survival in IS2 and IS4. Functional enrichment and protein-protein interaction analyses revealed that hub genes primarily involved in immunoglobulin kappa chains and cytokine/TNF signaling pathways may serve as valuable immune biomarkers for prognostic assessment and monitoring vaccine efficacy.

cancer biology↗

bioRxiv2026

Network-based integration of gene expression and DNA methylation identifies prognostic biomarkers for early-stage pancreatic cancer

Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies, largely due to the absence of reliable early-stage biomarkers. Here, we present a network-based multi-omics framework that integrates gene expression and DNA methylation data through partial correlation analysis to uncover prognostic markers. Four distinct networks were constructed: gene expression co-expression, methylation-only, multiplex (inter-layer connections linking the same genes across omics layers), and monoplex (fused multi-omics). Weighted gene co-expression network analysis (WGCNA) was applied to each network to select non-redundant, topologically representative hub genes as features for machine learning classification. Models trained on cross-layer (multiplex) features achieved an ROC of 82%, compared with 50-60% using single-omics features alone. The most strongly associated genes with poor prognosis include TFCP2L1, DHX32, and NCK1.

cancer biology↗

bioRxiv2025

DeepEpitope: Leveraging Transformation-Based protein Embeddings for Accurate linear Cancer B-cell Epitope Identification

Conventional cancer treatments tend to have serious side effects, leading to the quest for safer and more specific treatment modalities. Immunotherapy with vaccines has appeared as a promising option, with B-cell epitopes being crucial for the generation of humoral immunity. But identification of the correct B-cell epitopes of cancer is a severe challenge since current tools are not pre-trained with cancer-generated datasets. To bridge this gap, we introduce DeepEpitope, a command-line tool based on deep learning designed exclusively for the prediction of linear B- cell epitopes from cancer antigens. We compiled a high-quality dataset from the Cancer Epitope Database and used Evolutionary Scale Modeling (ESM) embeddings to represent epitope and non-epitope sequences as vectors of 1280 dimensions. These embeddings were employed to train five machine learning models (Logistic Regression, Random Forest, XGBoost, LightGBM, and Naive Bayes) and three deep learning models (Multilayer Perceptron [MLP], Convolutional Neural Network, and Bidirectional LSTM). Of these, the MLP model performed best with an AUC of 0.85 and a benchmark AUC of 94%. In comparison with other tools like BepiPred (60%) and LBtope (54%), DeepEpitope demonstrated much higher predictive accuracy. It is a Linux- based command-line tool that can be accessed for free at: https://github.com/karthick1087/DeepEpitope.

cancer biology↗

bioRxiv2024

VaxOptiML: Leveraging Machine Learning for Accurate Prediction of MHC-I & II Epitopes for Optimized Cancer Immunotherapy

In the realm of cancer immunotherapy, the ability to accurately predict epitopes is crucial for advancing vaccine development. Here, we introduce VaxOptiML (available at https://vaxoptiml.streamlit.app/), an integrated pipeline designed to enhance epitope prediction and prioritization. Utilizing a curated dataset of experimentally validated epitopes and sophisticated machine learning techniques, VaxOptiML features three distinct models that predict epitopes from target sequences, pair them with personalized HLA types, and prioritize them based on immunogenicity scores. Our rigorous process of data cleaning, feature extraction, and model building has resulted in a tool that demonstrates exceptional accuracy, sensitivity, specificity, and F1-score, surpassing existing prediction methods. The robustness and efficacy of VaxOptiML are further illustrated through comprehensive visual representations, underscoring its potential to significantly expedite epitope discovery and vaccine design in cancer immunotherapy, Additionally, we have deployed the trained ML model using Streamlit for public usage, enhancing accessibility and usability for researchers and clinician.

bioinformatics↗

bioRxiv2021

Phosphorylation of interfacial phosphosite leads to increased binding of Rap-Raf complex

The effect of phosphorylation of a serine residue in the Rap protein, residing at the complex interface of Rap-Raf complex is studied using atomistic molecular dynamics simulations. As the phosphosite of interest (SER39) is buried at the interface of the Rap-Raf complex, phosphorylation of only Rap protein was simulated and then complexed with the RBD of Raf for further analysis of complex stability. Our simulations reveal that the phosophorylation increases the binding of complex through strong electrostatic interactions and changes the charge distribution of the interface significantly. This is manifested as an increase in stable salt-bridge interactions between the Rap and Raf of the complex. Network analysis clearly shows that the phosphorylation of SER39 reorganizes the community network to include the entire region of Raf chain, including, Raf L4 loop potentially affecting downstream signalling.

biophysics↗