Work

A literature-weighted composite index for integrating reproductive dysfunction and cancer-associated molecular features: a reproducible computational pipeline

Year: 2026

Type: article

Abstract: To construct a literature-weighted composite Disease Activity Index (DAI) integrating gene-expression and DNA-methylation features associated with reproductive dysfunction and cancer, and to establish a reproducible computational pipeline for calculating the index from gene-level data. A hybrid dataset comprising 120 gene-level profiles was assembled from 50 literature-derived profiles seeded from 17 peer-reviewed sources and 70 synthetically augmented profiles. For each gene, DAI was calculated deterministically from cancer- and infertility-associated Log2FC values and a literature-derived weight according to a pre-specified formula, within a general mathematical framework that additionally specifies methylation main and interaction terms. The computational pipeline was implemented to reproduce the index and characterize its distribution. K-means clustering (k = 3) with principal component analysis (PCA) was used descriptively to characterize groupings within the resulting feature space. The pipeline reproduced the pre-specified DAI calculation, and the resulting scores showed three descriptive groupings with mean DAI values of 1.62, 1.17, and 0.91. The groupings reflected variation in the standardized molecular features used to construct the index rather than independently derived biological or clinical risk categories. This study provides a transparent, literature-weighted composite index and a reproducible computational framework for integrating gene-expression and DNA-methylation features at the intersection of reproductive dysfunction and cancer. Because DAI is derived directly from its input features and the dataset includes synthetic augmentation, the present findings are descriptive and hypothesis-generating. Independent evaluation using patient-derived molecular measurements and clinically observed outcomes is required before clinical or diagnostic interpretation.

Source: Scientific Reports

DOI: 10.1038/s41598-026-75779-4

Author: Zahra Dashti

Institution: Islamic Azad University, Bojnourd Branch

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690