Junjie Zhao
A5025468496- Renal cell carcinoma treatment
- Epigenetics and DNA Methylation
- Phagocytosis and Immune Regulation
- Cancer Immunotherapy and Biomarkers
- Protein Hydrolysis and Bioactive Peptides
- Essential Oils and Antimicrobial Activity
- Architecture and Computational Design
- Piperaceae Chemical and Biological Studies
- Healthcare and Venom Research
- Phytochemicals and Antioxidant Activities
- Pancreatic and Hepatic Oncology Research
- Food composition and properties
- Proteins in Food Systems
Sichuan University
2024-2025
West China Hospital of Sichuan University
2025
Hunan University of Traditional Chinese Medicine
2024
Computer Algorithms for Medicine
2020
544 Background: Non-clear cell renal carcinoma (nccRCC) accounts for approximately 25% of all (RCC) and lacks standards care. More recent data suggests the efficacy anti-PD-(L)1 plus anti-CTLA-4 immune checkpoint inhibitors combined with tyrosine-kinase in RCC. Cadonilimab (AK104) is a first-in-class tetravalent bispecific antibody that targets both PD-1 CTLA-4, showing manageable safety profile favorable clinical benefits. Here we explore cadonilimab combination axitinib patients advanced...
Abstract Purpose: Fumarate hydratase–deficient renal cell carcinoma (FH-deficient RCC) is a rare and lethal subtype of kidney cancer. However, the optimal treatments molecular correlates benefits for FH-deficient RCC are currently lacking. Experimental Design: A total 91 patients with from 15 medical centers between 2009 2022 were enrolled in this study. Genomic bulk RNA-sequencing (RNA-seq) performed on 88 45 untreated RCCs, respectively. Single-cell RNA-seq was to identify biomarkers...
We describe a representation targeted for anatomic objects which is designed to enable strong locational correspondence within object populations and thus provide powerful statistics. The method generates fitted frames on the boundary in interior of produces alignment-free geometric features from them. It accomplishes this by understanding an as diffeomorphic deformation ellipsoid using skeletal throughout produce model target object, where provided initially form mesh. Via classification...
<div>AbstractPurpose:<p>Fumarate hydratase–deficient renal cell carcinoma (FH-deficient RCC) is a rare and lethal subtype of kidney cancer. However, the optimal treatments molecular correlates benefits for FH-deficient RCC are currently lacking.</p>Experimental Design:<p>A total 91 patients with from 15 medical centers between 2009 2022 were enrolled in this study. Genomic bulk RNA-sequencing (RNA-seq) performed on 88 45 untreated RCCs, respectively. Single-cell...
<p>Supplementary Figure 2. Treatment details and survival outcomes of patients with metastatic FH-deficient RCC. (A) Swimmer plot showing the treatment response duration each patient receiving first-line systemic treatments; (B) Progression-free for (C) Overall (OS) treatments.</p>
<p>Supplementary Figure 6. Validation of the FH-deficient RCC immune signature. (A) UMAP plot showing expression selected genes; (B) Correlations between six genes in signature and treatment response, significance differential (q value) was determined by two-sided Wilcoxon rank-sum test with Bonferroni FDR correction; (C) several related hallmark pathways, correction. *, q<0.05; **, q<0.01; ***, q<0.001; ****, q<0.0001.</p>
<p>Supplementary Figure 3. Forest plot showing the prognostic value of clinicopathologic and molecular features in patients treated with first-line ICI+TKI combination therapy. HR<1 indicates better survival comparator group, while HR>1 control group. TMB, tumor mutation burden; MUT, mutation; WT, wild type; TPS, proportion score; CCP, cell cycle progression; Sig., signature.</p>
<p>Supplementary Figure 6. Validation of the FH-deficient RCC immune signature. (A) UMAP plot showing expression selected genes; (B) Correlations between six genes in signature and treatment response, significance differential (q value) was determined by two-sided Wilcoxon rank-sum test with Bonferroni FDR correction; (C) several related hallmark pathways, correction. *, q<0.05; **, q<0.01; ***, q<0.001; ****, q<0.0001.</p>
<p>Supplementary Figure 5. Cell clusters and their distribution in a validation cohort by Dong et al.. (A) UMAP plot showing the sample of T cells; (B) all cells collected from four samples; (C) Dot marker gene expression for clusters; (D) Bar plots tissue (E) Tissue prevalence cell estimated Ro/e score.</p>
<p>Supplementary Figure 2. Treatment details and survival outcomes of patients with metastatic FH-deficient RCC. (A) Swimmer plot showing the treatment response duration each patient receiving first-line systemic treatments; (B) Progression-free for (C) Overall (OS) treatments.</p>
<p>Supplementary Figure 1. Survival outcomes of patients with synchronous and metachronous metastatic disease treatment flows. Overall survival (OS) for FH-deficient RCC in the overall systemic setting (A), ICI+TKI (B), TKI monotherapy (C); (D) Sankey diagram showing flows RCC.</p>
<p>Supplementary Figure 5. Cell clusters and their distribution in a validation cohort by Dong et al.. (A) UMAP plot showing the sample of T cells; (B) all cells collected from four samples; (C) Dot marker gene expression for clusters; (D) Bar plots tissue (E) Tissue prevalence cell estimated Ro/e score.</p>