Jun Liu

Dr. Jun Liu


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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Verified
Journal Article
Open access
PDF file
YMYiming Ma
Yiming Ma
YMYu Ming
Yu Ming
ZHZhiyong Hou
Zhiyong Hou
YYYanan Yu
Yanan Yu
JLJun Liu
Jun Liu
ZWZhong Wang
Zhong Wang
Depression and breast cancer (BC) demonstrate significant clinical comorbidity, yet their shared molecular mechanisms remain unclear, particularly regarding immune pathway regulation. This study systematically analyzed Depression-associated gene expression profiles (Gene Expression Omnibus (GEO) database) and BC transcriptomic data (The Cancer Genome Atlas (TCGA) database), identifying overlapping differentially expressed genes (DEGs). Functional enrichment (Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)) and protein–protein interaction (PPI) network analyses (STRING/Cytoscape) were employed to elucidate biological processes, followed by least absolute shrinkage and selection operator (LASSO) regression and receiver operating characteristic (ROC) curve validation to prioritize key genes. Immune infiltration patterns were assessed via the xCell algorithm, with Spearman correlation linking genes to immune subsets, and single-gene Gene Set Enrichment Analysis (GSEA) evaluating pathway activity. In total, 93 overlapping genes were identified, with predominant involvement in immune-related pathways being revealed by functional enrichment analysis. BHLHE41, EpCAM, and GSTM2 were prioritized as mechanism-associated genes through integrated LASSO regression and ROC analyses. Significant correlations were observed between these genes and specific immune cell populations. GSEA further linked these genes to immune response pathways, suggesting their regulatory roles. These findings highlight immune dysregulation as a shared mechanism underlying Depression-BC comorbidity, providing a foundation for developing early diagnostic strategies and therapeutic strategies targeting both conditions.

Verified
Journal Article
Open access
PDF file
BYBei Yao
Bei Yao
YMYiming Ma
Yiming Ma
SGShuang Guan
Shuang Guan
QZQiguang Zheng
Qiguang Zheng
YYYanan Yu
Yanan Yu
JLJun Liu
Jun Liu
Primary sclerosing cholangitis (PSC) is a chronic inflammatory precursor associated with an increased risk of intrahepatic cholangiocarcinoma (ICC), yet identifying malignant features within the persistent inflammatory background remains challenging. In this study, a background-deviation framework was applied to explore malignant-associated determinants during PSC-associated cholangiocarcinogenesis. Single-cell RNA sequencing data from PSC, ICC tumor tissues, and adjacent non-tumor tissues were integrated, followed by functional enrichment, CellChat analysis, Monocle 2 pseudotime reconstruction, Non-negative Matrix Factorization (NMF), STRING/Cytoscape network analysis, and diagnostic signature construction using LASSO regression and exhaustive best subset selection. Single-cell profiling suggested disease-associated cellular remodeling, including cholangiocyte expansion in ICC samples. Functional and intercellular communication analyses indicated a putative transition from an immune-dominant PSC state toward a hyper-biosynthetic ICC-associated phenotype, accompanied by a possible MIF receptor-usage shift from CXCR4 to CD44. Monocle 2 and NMF further identified candidate malignant-associated trajectories and meta-programs, with MYC/TP63-related regulatory signals emerging as potential contributors. Based on these exploratory findings, best subset selection identified a two-gene transcriptomic candidate signature comprising PMAIP1 and GADD45A, which showed promising discriminative performance in internal cross-validation and an external tumor-versus-adjacent validation cohort. These findings provide a transcriptomic basis for further validation of PSC-associated cholangiocarcinogenesis and potential ICC surveillance markers.

12 August 2026 in Frontiers in Psychiatry
Verified
Journal Article
Open access
PDF file
YMYiming Ma
Yiming Ma
ZHZhiyong Hou
Zhiyong Hou
BYBei Yao
Bei Yao
RJRan Jia
Ran Jia
YYYanan Yu
Yanan Yu
ZWZhong Wang
Zhong Wang
Background: Insulin resistance (IR) has been implicated in depression, yet the dynamic interplay between concurrent changes in IR and depressive symptoms over time, and whether systemic inflammation mediates this relationship, remains unclear. Methods: Data were obtained from 3,404 older adults (aged ≥50 years) in the English Longitudinal Study of Ageing (ELSA) across Waves 2, 4, and 6. IR was assessed using the estimated glucose disposal rate (eGDR). Depressive symptoms were measured with the CES-D scale (0-8). C-reactive protein (CRP) was log-transformed as the mediator. Linear mixed models were used to assess the longitudinal association between changes in eGDR and CES-D score. Restricted cubic splines were applied to explore potential nonlinear dose-response relationships. Mediation analysis was conducted to test whether CRP mediated the association. Subgroup and sensitivity analyses were performed to evaluate the robustness of the findings. Results: In the overall population, eGDR was significantly associated with CES-D score (P = 0.003), with no evidence of nonlinearity (P = 0.074). The overall eGDR × time interaction was not significant (P = 0.349). When stratified by joint trajectories of eGDR and CES-D, significant eGDR × time interactions were observed in two groups: the decrease-increase group (eGDR declined and CES-D increased, β = –0.018, P < 0.001), and the increase-decrease group, (eGDR increased and CES-D decreased, β = 0.021, P < 0.001). CRP partially mediated the association, with an indirect effect of -0.00092 (95% CI: -0.00155 to -0.00042), accounting for 10.8% of the total effect. Subgroup and sensitivity analyses confirmed robustness. Conclusions: The longitudinal association between IR and depressive symptoms differs across developmental trajectories and appears to be partially mediated by systemic inflammation. Our findings suggest that improvements in insulin sensitivity are associated with a lower risk of depressive symptoms in older adults.