PubMed Health⌕ Search

Biomedical subjects

Shafiul Haque

Publications and source records attributed to Shafiul Haque.

2 recordsLinked to original sources
PubMed2026

Unravelling the biological nexus of smoking and postpartum depression: a meta-analysis and functional genomics approach.

PURPOSE: Postpartum depression (PPD) is a prevalent psychological condition among birthing women. While several psycho-socio-economic and neurobiological factors influence its development, its relationship with smoking behavior and nicotine addiction remains largely inconclusive. METHODS: In this combinatorial study, we first evaluate the relationship between smoking and depressive behaviors in postpartum women using data extracted from pertinent primary epidemiological studies. Additionally, to discern the molecular and cellular mechanisms underlying this association, we identified common genetic elements and evaluated their functional attributes using in silico analyses. RESULTS: Meta-analytical assessment of systematically collected data from 38 studies indicated that smoking women are twice as likely to develop PPD, compared to their non-smoking counterparts. While geocultural attributes did not affect this relationship, timing of smoking was a significant moderator, with current and gestational smoking statuses being more strongly linked with PPD outcome, compared to the past smoking habit. Further, depression scores in smoking postpartum women were higher than those in non-smoking controls. Analysis of the common protein-encoding genes underlying the pathophysiology of nicotine addiction and PPD revealed several critical hub proteins (viz., AKT1, JUN, CTNNB1, PTEN, EGFR, ESR1, SRC, STAT3, FN1, IL1B, IL6, TNF, TP53, GAPDH, INS, MYC, and ALB) which were predicted to alter multiple pathophysiological pathways associated with transcriptional expression, intra- and intercellular signaling transduction, metabolism, and immune functions. CONCLUSION: Our results indicate that smoking is strongly associated with depressive behavior in postpartum women, although this association involve mediation of additional environmental and psychosocial elements. Moreover, network analysis of common genetic elements identified several potentially disrupted neurophysiological pathways in postpartum women with smoking and depressive behaviors which may aid in characterizing the underlying relationship between the two conditions.

Humans↗
PubMed2026

Machine learning-enabled multi-omics discovery of prognostic biomarkers and signaling targets in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains difficult to subtype using single omics layers. We conducted an exploratory investigation integrating reverse-phase protein array (RPPA) and DNA methylation data from the cancer genome atlas (TCGA)- pancreatic adenocarcinoma (PAAD) to assess the feasibility of multi-omics subtyping, alongside a supervised machine learning analysis of a small gene expression omnibus (GEO) transcriptomic cohort (n = 26) to identify candidate diagnostic genes. RPPA-based K-means clustering suggested a weak, possible two-subtype structure (silhouette ≈ 0.16) that remained unassociated with overall survival (log-rank p = 0.113) and lacked independent prognostic value. An independently performed similarity network fusion (SNF) analysis integrating RPPA and methylation data showed low concordance with RPPA-derived subtypes (Adjusted Rand Index (ARI) = 0.014), indicating limited convergence between molecular modalities. Supervised machine learning analysis of the GEO cohort using a fully nested leave-one-out cross-validation pipeline achieved a mean (area under the curve) AUC of 0.896 across four classifiers and identified four-fold-stable candidate genes (ESCO2, COL17A1, BCL2L14, and SOWAHB). However, this gene panel demonstrated limited external validity across two independent PDAC cohorts (log-rank p = 0.438 for both GSE62452 and GSE28735), indicating limited generalizability despite robust internal performance. Collectively, these findings provide limited evidence for a robust, prognostically significant multi-omics subtype or a validated diagnostic gene signature; instead, this study serves as a hypothesis-generating resource and highlights the importance of rigorous cross-validation and independent external validation in small-sample transcriptomic biomarker discovery.

Humans↗