Biomedical subjects

Li Su

Publications and source records attributed to Li Su.

3 recordsLinked to original sources

PubMed2026

ARHGAP22 as a Potential Prognostic Biomarker in Clear Cell Renal Cell Carcinoma: Insights into Tumor Immunity and Co-Expression Networks.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer and is characterized by substantial clinical heterogeneity, highlighting the need for reliable prognostic biomarkers. This study evaluated the expression pattern, prognostic relevance, and immune-related associations of ARHGAP22 in ccRCC using transcriptomic and clinical data from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) cohort, together with external validation data and protein-expression information from the Human Protein Atlas (HPA). ARHGAP22 expression was compared between tumor and adjacent normal tissues, and its associations with overall survival, clinicopathological characteristics, tumor microenvironment scores, and estimated immune-cell fractions were assessed. Co-expression and functional-enrichment analyses were also performed to characterize potential biological associations. ARHGAP22 was significantly upregulated in ccRCC tissues at the transcriptomic level, with corresponding differences observed in immunohistochemical images. High ARHGAP22 expression was associated with shorter overall survival, advanced clinicopathological features, and higher ImmuneScore, StromalScore, and ESTIMATEScore values. CIBERSORT-based analysis showed that the high-expression group had higher estimated fractions of M2 macrophages and regulatory T cells and lower estimated fractions of naïve B cells, resting mast cells, and activated dendritic cells after false discovery rate correction. Functional-enrichment analyses linked ARHGAP22-associated genes to immune-related processes, cell migration, and chemokine- and cytokine-mediated signaling pathways. These findings suggest that ARHGAP22 may represent a potential prognostic and immune-related biomarker in ccRCC, although further independent clinical and experimental validation is required.

Humans↗

PubMed2026

Germline determinants of risk and molecular subtype in young-onset lung cancer.

Young-onset lung cancer is enriched for never-smoking and oncogene-driven tumors, yet its inherited genetic basis remains poorly defined. We performed germline whole-genome sequencing in 251 young-onset lung cancer cases (median age 37), which we jointly analyzed with never-smoking cases (n=196; median age 68) and cancer-free controls (n=1,883). We identified enrichments of rare deleterious coding variants across 55 cancer-related gene sets, including EGFR/ERBB2 signaling and genes implicated by prior lung cancer GWAS. Exome-wide analyses of rare coding variants affirmed TP53 as a penetrant lung cancer predisposition gene (odds ratio [OR]=36.1, p=1.02x10-7) and discovered two novel exome-wide significant tumor subtype-dependent associations: IREB2 in cases with fusion-driven tumors (p=1.39x10-6) and SMAD6 in fusion-negative tumors (p=2.05x10-6). Structural variants contributed distinct risk, with enrichment in constrained, lung-expressed genes (OR=5.79, p=5.8x10-5) and very large germline deletions being markedly enriched in cases with fusion-driven tumors. Polygenic risk scores for lung cancer were inversely correlated with rare variant burden, consistent with additive risk from rare and common variants. Collectively, these findings delineate a complex germline architecture underlying susceptibility and molecular subtype in young-onset lung cancer.

Journal Article↗

PubMed2025

scBSP: a fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.

MOTIVATION: Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in 2D and 3D space. However, the limited sequencing depth, increasing spatial resolution, and growing spatial spots in spatial omics technologies present significant computational challenges in identifying biologically meaningful molecules with variable spatial distributions across various omics modalities. RESULTS: We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in large-scale spatial omics data. scBSP demonstrates significantly enhanced computational efficiency, processing high-resolution spatial omics data within seconds, and exhibits robust cross-platform performance by consistently identifying spatially variable features with high reproducibility across various sequencing platforms. AVAILABILITY AND IMPLEMENTATION: scBSP is available for download from R CRAN at https://cran.r-project.org/web/packages/scBSP/index.html and PyPI at https://pypi.org/project/scbsp/.

Software↗