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Results for “MUC16”

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4 recordsLinked to original sources
PubMed2026

Characterization of the genomic and transcriptomic landscape of invasive non-mucinous lung adenocarcinoma based on IASLC grading.

BACKGROUND: The IASLC grading system has prognostic utility and potential therapeutic implications in invasive non-mucinous lung adenocarcinoma (LUAD), but the molecular basis underlying the grading spectrum remains unclear. METHODS: We performed whole-genome sequencing in 138 Chinese patients with invasive non-mucinous LUAD and RNA sequencing of 96 matched tumor-normal tissue pairs to systematically characterize the molecular features across grades, including coding driver events, mutational signatures, non-coding regulatory disruptions, and transcriptional programs. RESULTS: Compared with Grade 1-2 tumors, Grade 3 LUADs exhibited heightened invasive potential, manifested by more advanced stage, more frequent spread through air spaces, and independently worse survival. Grade 3 tumors had elevated tumor mutational burden and were enriched for alterations in genome maintenance and cell-cycle genes, including TP53, as well as genes implicated in DNA damage response, including ZFHX4. APOBEC-associated mutagenesis was selectively enriched in Grade 3 tumors independent of smoking status, consistent with an instability-associated phenotype. Recurrent non-coding regulatory disruptions affected lung lineage-defining genes, particularly surfactant-associated genes, and were correlated with reduced expression. Transcriptomic profiling revealed epithelial dedifferentiation, loss of pulmonary homeostatic programs, and activation of proliferative and stress-related pathways. Notably, MUC16 emerged as a convergent event linking genomic and transcriptional dysregulation, with coding mutations associated with higher expression and increased expression in Grade 3 tumors correlating with the proportion of high-grade histologic patterns. CONCLUSIONS: These findings provide a molecular framework for the IASLC grading spectrum and identify Grade 3 LUAD as a distinct instability-associated and dedifferentiated biological state.

IASLC grading↗
PubMed2025

Personalizing CA125 Levels Using Tumor Marker Variants: A Case-Control Analysis of Diagnostic Performance for Pancreatic Cancer.

BACKGROUND: Cancer antigen 125 (CA125) is widely recognized as a useful biomarker for the surveillance of patients with ovarian and other cancers. Prior genome-wide association studies have identified variants that influence CA125 levels. We evaluated the utility of stratifying CA125 levels by such variants and evaluated diagnostic performance in control subjects and patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: We measured CA125 levels in 807 control subjects and 450 patients with PDAC and genotyped 10 variants involving four genes (GAL3ST2, MSLN, D2HGDH, and MUC16). We compared CA125 levels in controls by variant and generated variant-defined CA125 cutoffs and then classified cases and controls into functional groups based on their variant profile. We used this variant classification to evaluate the diagnostic performance of CA125 in patients with PDAC. RESULTS: Six variants associated with CA125 levels were used to group controls into one of four groups. Mean CA125 levels in the highest variant group were approximately fourfold higher than in the lowest group. African Americans were more likely to have a variant group associated with low CA125 levels. After setting diagnostic cutoffs by variant group, the diagnostic sensitivity of CA125 for PDAC was 20.2% at 98% specificity (areas under the ROC curve, 0.702), not significantly different from a uniform CA125 diagnostic cutoff (areas under the ROC curve, 0.700). CONCLUSIONS: Gene variants can be used to generate personalized CA125 reference ranges. This approach did not significantly improve CA125's diagnostic performance for pancreatic cancer, but it merits evaluation in other diagnostic settings, such as detecting ovarian cancer. IMPACT: Gene variants can be used to personalize CA125 levels.

Humans↗
PubMed2024

Genomic landscape of hepatocellular carcinoma in Egyptian patients by whole exome sequencing.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer. Chronic hepatitis and liver cirrhosis lead to accumulation of genetic alterations driving HCC pathogenesis. This study is designed to explore genomic landscape of HCC in Egyptian patients by whole exome sequencing. METHODS: Whole exome sequencing using Ion Torrent was done on 13 HCC patients, who underwent surgical intervention (7 patients underwent living donor liver transplantation (LDLT) and 6 patients had surgical resection}. RESULTS: Mutational signature was mostly S1, S5, S6, and S12 in HCC. Analysis of highly mutated genes in both HCC and Non-HCC revealed the presence of highly mutated genes in HCC (AHNAK2, MUC6, MUC16, TTN, ZNF17, FLG, MUC12, OBSCN, PDE4DIP, MUC5b, and HYDIN). Among the 26 significantly mutated HCC genes-identified across 10 genome sequencing studies-in addition to TCGA, APOB and RP1L1 showed the highest number of mutations in both HCC and Non-HCC tissues. Tier 1, Tier 2 variants in TCGA SMGs in HCC and Non-HCC (TP53, PIK3CA, CDKN2A, and BAP1). Cancer Genome Landscape analysis revealed Tier 1 and Tier 2 variants in HCC (MSH2) and in Non-HCC (KMT2D and ATM). For KEGG analysis, the significantly annotated clusters in HCC were Notch signaling, Wnt signaling, PI3K-AKT pathway, Hippo signaling, Apelin signaling, Hedgehog (Hh) signaling, and MAPK signaling, in addition to ECM-receptor interaction, focal adhesion, and calcium signaling. Tier 1 and Tier 2 variants KIT, KMT2D, NOTCH1, KMT2C, PIK3CA, KIT, SMARCA4, ATM, PTEN, MSH2, and PTCH1 were low frequency variants in both HCC and Non-HCC. CONCLUSION: Our results are in accordance with previous studies in HCC regarding highly mutated genes, TCGA and specifically enriched pathways in HCC. Analysis for clinical interpretation of variants revealed the presence of Tier 1 and Tier 2 variants that represent potential clinically actionable targets. The use of sequencing techniques to detect structural variants and novel techniques as single cell sequencing together with multiomics transcriptomics, metagenomics will integrate the molecular pathogenesis of HCC in Egyptian patients.

Humans↗
PubMed2026

Identification of ultrasound-associated gene candidates in myeloid cells and construction of a prognostic risk model for acute myeloid leukemia.

BACKGROUND: Incorporating ultrasound (US) treatment sensitivity analysis may improve the treatment of acute myeloid leukemia (AML). METHODS: This study integrated single-cell and bulk datasets for analysis. Differential expression analysis between US-treated and control samples was performed using limma package. The AUCell package was used to calculate US-associated scores in the single-cell dataset. Differentially expressed genes (DEGs) between the specific groups were identified, followed by intersection analysis with previously identified DEGs. Univariate regression, Least Absolute Shrinkage and Selection Operator (LASSO) analysis (using the glmnet package), and stepwise multivariate regression (using the MASS package) were used to refine the candidate genes and to construct a risk model. The model genes were validated using in vitro experiments. Enrichment analysis was conducted using gene set enrichment analysis (GSEA), and immune infiltration was evaluate by single-sample GSEA (ssGSEA) and ESTIMATE algorithms. The correlations between RiskScores and drug sensitivity were analyzed by oncoPredict package. Finally, tumor mutational burden (TMB) and genomic mutations were compared between the risk groups. RESULTS: Nine prognostic signatures (SPINK2, HNRNPAB, SH3BGRL3, CLEC11A, ITGA4, RPL39L, MX1, HEXIM1, and MAP4K4) were identified. Particularly, low expression of SPINK2 attenuated the activity and invasion of AML cells. High-risk group had higher immune cell infiltration. Eight drugs were predicted to be correlated with the RiskScore model. DNMT3A and RUNX1 showed higher mutation frequencies in the high-risk group, whereas KIT and MUC16 showed higher mutation frequencies in the low-risk group. CONCLUSION: The RiskScore model established in this study provides a theoretical basis for clinically screening responsive populations and optimizing treatment strategies.

Humans↗