Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients

Sense and anti-sense: Role of FAM83A and FAM83A-AS1 in Wnt, EGFR, PI3K, EMT pathways and tumor progression

An increasing number of studies have shown that FAM83A, a member of the family with sequence similarity 83 (FAM83), which consists of eight members, is a key tumor therapeutic target involved in multiple signaling pathways. It has been reported that FAM83A plays essential roles in the regulation of Wnt/β-catenin, EGFR, MAPK, EMT, and other signaling pathways and physiological processes in models of pancreatic cancer, lung cancer, breast cancer, and other malignant tumors. Moreover, the expression of FAM83A could be significantly affected by multiple noncoding RNAs that are dysregulated in malignant tumors, the dysregulation of which is essential for the malignant process. Among these noncoding RNAs, the most noteworthy is the antisense long noncoding (Lnc) RNA of FAM83A itself (FAM83A-AS1), indicating an outstanding synergistic carcinogenic effect between FAM83A and FAM83A-AS1. In the present study, the specific mechanisms by which FAM83A and FAM83A-AS1 cofunction in the Wnt/β-catenin and EGFR signaling pathways were reviewed in detail, which will guide subsequent research. We also described the applications of FAM83A and FAM83A-AS1 in tumor therapy and provided a certain theoretical basis for subsequent drug target development and combination therapy strategies.

Identifying OGN as a Biomarker Covering Multiple Pathogenic Pathways for Diagnosing Heart Failure: From Machine Learning to Mechanism Interpretation

BACKGROUND

The pathophysiologic heterogeneity of heart failure (HF) necessitates a more detailed identification of diagnostic biomarkers that can reflect its diverse pathogenic pathways.

METHODS

We conducted weighted gene and multiscale embedded gene co-expression network analysis on differentially expressed genes obtained from HF and non-HF specimens. We employed a machine learning integration framework and protein-protein interaction network to identify diagnostic biomarkers. Additionally, we integrated gene set variation analysis, gene set enrichment analysis (GSEA), and transcription factor (TF)-target analysis to unravel the biomarker-dominant pathways. Leveraging single-sample GSEA and molecular docking, we predicted immune cells and therapeutic drugs related to biomarkers. Quantitative polymerase chain reaction validated the expressions of biomarkers in the plasma of HF patients. A two-sample Mendelian randomization analysis was implemented to investigate the causal impact of biomarkers on HF.

RESULTS

We first identified COL14A1, OGN, MFAP4, and SFRP4 as candidate biomarkers with robust diagnostic performance. We revealed that regulating biomarkers in HF pathogenesis involves TFs (BNC2, MEOX2) and pathways (cell adhesion molecules, chemokine signaling pathway, cytokine-cytokine receptor interaction, oxidative phosphorylation). Moreover, we observed the elevated infiltration of effector memory CD4+ T cells in HF, which was highly related to biomarkers and could impact immune pathways. Captopril, aldosterone antagonist, cyclopenthiazide, estradiol, tolazoline, and genistein were predicted as therapeutic drugs alleviating HF via interactions with biomarkers. In vitro study confirmed the up-regulation of OGN as a plasma biomarker of HF. Mendelian randomization analysis suggested that genetic predisposition toward higher plasma OGN promoted the risk of HF.

CONCLUSIONS

We propose OGN as a diagnostic biomarker for HF, which may advance our understanding of the diagnosis and pathogenesis of HF.