MicroRNA Signatures for Pancreatic Cancer and Chronic Pancreatitis: Expression Profiling by NGS.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease due to the lack of early detection. Because chronic pancreatitis (CP) patients are a high-risk group for pancreatic cancer, this study aimed to assess the differential miRNA profile in pancreatic tissue of patients with CP and pancreatic cancer.

Methods: MiRNAs were isolated from formalin-fixed paraffin-embedded pancreatic tissue of 22 PDAC patients, 18 CP patients, and 10 normal pancreatic tissues from autopsy (C) cases and processed for next-generation sequencing.

Multi-omics approach for identifying CNV-associated lncRNA signatures with prognostic value in prostate cancer.

Background: Prostate cancer, the second most prevalent malignancy among men, poses a significant threat to affected patients' well-being due to its poor prognosis. Novel biomarkers are required to enhance clinical outcomes and tailor personalized treatments. Herein, we describe our research to explore the prognostic value of long non-coding RNAs (lncRNAs) deregulated by copy number variations (CNVs) in prostate cancer.

Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer.

Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast cancer (BrCa) subtype characterized by high mortality and high frequency of distant metastasis cases. The identification of prognostic biomarker can improve prognosis and personalized treatment regimes. Herein, we collected gene expression datasets representing TNBC and Non-TNBC BrCa.

DriverFuse: An R package for analysis of next-generation sequencing datasets to identify cancer driver fusion genes.

We developed the DriverFuse package to integrate orthogonal data types such as Structural Variants (SV) and Copy Number Variations (CNV) to characterize fusion genes in cancer datasets. A fusion gene is reported as a driver or passenger fusion gene, based on mapping SV and CNV profiles. DriverFuse generates a fusion plot of fusion genes with their mapping SV, CNV profile, domain architecture and classification of its role in cancer.

Unsupervised subtyping and methylation landscape of pancreatic ductal adenocarcinoma.

Pancreatic Ductal Adenocarcinoma (PDAC) is an aggressive form of pancreatic cancer that typically manifests itself at an advanced stage and does not respond to most treatment modalities. The survival rate of a PDAC patient is less than 5%, with a median survival of just a couple of months. A better understanding of the molecular pathology of PDAC is needed to guide research for the development of better clinical treatment modalities for PDAC patients.

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning.

Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of IDC.

Traditional uses of medicinal plants among the rural communities of Churu district in the Thar Desert, India.

The traditional uses of medicinal plants in healthcare practices are providing clues to new areas of research; hence its importance is now well recognized. However, information on the uses of indigenous plants for medicine is not well documented from many rural areas of Rajasthan including Churu district. The study aimed to look into the diversity of plant resources that are used by local people for curing various ailments.