Wei Huang’s research while affiliated with Tianjin Medical University Cancer Institute and Hospital and other places

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Publications (3)


ERBB2 expression in different molecular types of MIBC. (A) Schematic diagram of consensus types of MIBC, including basal/squamous (Ba/Sq), luminal unstable (LumU), luminal nonspecified (LumNS), luminal papillary (LumP), neuroendocrine-like (NE-like), and stoma-rich. (B) ERBB2 mRNA expression in six MIBC consensus subtypes in the TCGA-MIBC dataset. (C) ERBB2 mRNA expression in luminal and basal cells in CCLE dataset. (D) The luminal and basal-type single-cell clusters were determined by their molecular markers (luminal: FOXA1 and GATA3; basal: CD44 and KRT5), and the distribution of ERBB2 mRNA expression in luminal and basal clusters is shown. (E) The luminal and basal bladder cancer tissues’ IHC were classified by their molecular marker (luminal: KRT20 and GATA3; basal: CD44 and KRT5/6), and ERBB2 protein expression was detected. (F) Quantification of ERBB2 immunohistochemistry protein levels in luminal and basal bladder cancer. (G) Western blot analysis showed the protein expression of ERBB2, KRT20, GATA3, CD44, and KRT5/6 in luminal (HT1376, RT4, and SW780) and basal (5637, J82, and T24) cells. (H) Quantitative chart of ERBB2, KRT20, GATA3, CD44, and KRT5/6 from the Western blot analysis. *, P-value < 0.05; **, P-value < 0.01; ***, P-value < 0.001; ****, P-value < 0.0001.
Luminal cells show higher sensitivity to RC48. (A) The Western blot shows ERBB2 protein expression levels in luminal cells (SW780, HT1376, and RT4) after exposure to RC48 at different concentrations and times. (B) The barplot shows the gDS in MIBC cells representing luminal and basal cells. (C) The percentage of surviving cells after exposing different concentrations of RC48 in three luminal (SW780, RT4, and HT1376) and three basal (J82, T24, and 5637) cells. (D) Colon formation assay reveals the cell growth after exposing different concentrations of RC48 in luminal and basal cells. The percentage of survival cells was quantified. (E) Cell viability of three luminal and three basal cells after exposing RC48 (25 μg/ml) at different times. *, P-value < 0.05; **, P-value < 0.01; ***, P-value < 0.001; ****, P-value < 0.0001; ns, not significant.
ERBB2 expression mediates the sensitivity of luminal and basal cells to RC48. (A) Western blot analysis validated the ERBB2 expression in ERBB2-silenced luminal cells (SW780 and HT1376) and ERBB2-overexpressing basal (T24 and 5637) cells. (B, C) The percentage of surviving cells after exposure to different concentrations of RC48 in ERBB2-silenced luminal cells (SW780 and HT1376)/or ERBB2-overexpressing basal cells (T24 and 5637). (D) Colon formation assay demonstrates cell growth after exposure to RC48 (5 μg/ml) in ERBB2-overexpressing basal cells. (E, F) Cell viability of the ERBB2-overexpressing and control basal cells after exposure to RC48 (25 μg/ml). (G) Tumors with different treatments were shown. (H) Tumor weight was measured after 4 weeks. Data are presented as the mean ± SD. (I) Tumor volume was measured one to two times weekly once the tumor became visible. Data are presented as the mean ± SD. (J) IHC revealed ERBB2 expression in tumor samples from mice receiving different treatments. *, P-value < 0.05; **, P-value < 0.01; ***, P-value < 0.001; ****, P-value < 0.0001.
The STAT3 pathway is compensatively activated in basal cells. (A) The diagram shows the GESA result based on the DEGs between basal/squamous and the other three luminal consensus subtypes. (B) The Venn diagram shows the common altered pathways from the GSEA results based on the DEGs determined between basal and luminal types of four molecular classification systems. (C, D) The heatmap shows the expression of genes positively correlated with STAT3 pathways in basal and luminal bladder cancer tissues and cell lines. (E) STAT3 mRNA expression levels in consensus types of TCGA-MIBC. (F) STAT3 mRNA expression levels in luminal and basal cells. (G) STAT3 protein levels in luminal and basal cells were detected by Western blot analysis. (H) The diagram shows the GSEA results based on the DEGs between RC48-exposed SW780 and control. The top 10 activated pathways are shown. (I) The diagram shows the correlation between ERBB2 and STAT3 in mRNA levels, with the PCC used for analysis. (J, K) In ERBB2-silenced luminal cells and ERBB2-overexpressing basal cells, Western blot analysis was performed to measure the expression changes of ERBB2, STAT3, and pSTAT3 (Tyr705). (L) After exposure to RC48 (HT1376 and SW780), the proteins ERBB2, STAT3, and pSTAT3 (Tyr705) were detected by Western blot analysis. (M) Control and RC48-exposed SW780 and HT1376 cells, or ERBB2-overexpressing T24 and 5637 cells, were fixed and incubated with antibodies against pSTAT3 (Tyr705) for immunofluorescence analysis. *, P-value < 0.05; **, P-value < 0.01; ***, P-value < 0.001; ****, P-value < 0.0001.
STAT3 silencing and inhibition can increase the sensitivity of basal cells to RC48. (A) Western blot analysis validated the STAT3/pSTAT3 (Tyr705) protein expression in STAT3-silenced basal cells (T24 and 5637). (B) The percentage of surviving cells after exposure to RC48 (5 μg/ml) in STAT3-silenced basal cells and their corresponding control cells. (C) Colony formation assay revealed cell growth after exposure to RC48 (5 μg/ml) in STAT3-silenced cells and their corresponding control cells. (D) Dose–response assay of three STAT3 inhibitors in T24 and 5637 cells. (E) Western blot analysis revealed STAT3 and pSTAT3 (Tyr705) expression after exposure to different concentrations of ART. (F) The percentage of surviving cells after exposure to different concentrations of ART in basal (T24 and 5637) and luminal (SW780 and HT1376) bladder cancer cells. (G, H) Cell viability was assessed by combining different concentrations of RC48 and ART in T24 and 5637 cells. (I, J) The CCK-8 proliferation assay was performed on T24 and 5637 cells exposed to saline, a single drug (RC48 or ART), or a combination of both drugs. (K) The colony formation assay was conducted on T24 and 5637 cells exposed to saline, a single drug (RC48 or ART), or a combination of both drugs. **, P-value < 0.01; ***, P-value < 0.001; ****, P-value < 0.0001; ns, not significant.

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The combination treatment of RC48 and STAT3 inhibitor acts as a promising therapeutic strategy for basal bladder cancer
  • Article
  • Full-text available

January 2025

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7 Reads

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Kun Shan

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Wei Huang

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[...]

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E. Du

As an antibody-drug conjugate (ADC), disitamab vedotin (RC48) is a promising treatment targeting ERBB2 for locally advanced and metastatic bladder cancer (BLCA). However, the subtype heterogeneity of muscle-invasive bladder cancer (MIBC) often leads to different therapeutic outcomes. In our study, we aim to explore sensitivity differences and mechanisms of different molecular subtypes of MIBC to RC48 treatment and develop a strategy for combination therapy against cancer. Using large-scale mRNA expression profile datasets, Western blotting, and immunohistochemistry, we first found that ERBB2 is upregulated in the luminal type but downregulated in basal bladder cancer. In addition, luminal cells showed higher sensitivity to RC48 than basal cells. Basal cells with ERBB2 overexpression demonstrated increased sensitivity to RC48 in vitro and in vivo, indicating that ERBB2 expression mediates RC48’s therapeutic efficacy against cancer. In basal or RC48-exposed luminal cells, the JAK/STAT3 pathway was highly enriched, suggesting that downregulation or pharmacological inhibition of ERBB2 leads to compensatory activation of this pathway. Silencing STAT3 increased the inhibitory efficacy of RC48. In addition, artesunate (ART, a STAT3 inhibitor) showed a synergistic effect with RC48 against basal bladder cancer both in vitro and in vivo. In summary, these findings provide a theoretical foundation for subsequent clinical trials combining RC48 and ART in MIBC based on molecular subtypes.

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Single-cell and bulk RNA-sequence identified fibroblasts signature and CD8+ T-cell - fibroblast subtype predicting prognosis and immune therapeutic response of bladder cancer, based on machine-learning bioinformatics retrospective study

May 2024

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25 Reads

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5 Citations

International Journal of Surgery

Background Cancer-associated fibroblasts (CAFs) are found in primary and advanced tumours. They are primarily involved in tumour progression through complex mechanisms with other types of cells in the tumour microenvironment. However, essential fibroblasts-related genes (FRG) in bladder cancer still need to be explored, and there is a shortage of an ideal predictive model or molecular subtype for the progression and immune therapeutic assessment for bladder cancer, especially muscular-invasive bladder cancer based on the FRG. Materials and methods CAF-related genes of bladder cancer were identified by analyzing single-cell RNA sequence datasets, and bulk transcriptome datasets and gene signatures were used to characterize them. Then, ten types of machine learning algorithms were utilized to determine the hallmark FRG and construct the FRG index (FRGI) and subtypes. Further molecular subtypes combined with CD8+ T-cells were established to predict the prognosis and immune therapy response. Results 54 BLCA-related FRG were screened by large-scale scRNA-sequence datasets. The machine learning algorithm established a 3-genes FRG index (FRGI). High FRGI represented a worse outcome. Then, FRGI combined clinical variables to construct a nomogram, which shows high predictive performance for the prognosis of bladder cancer. Furthermore, the BLCA datasets were separated into two subtypes - fibroblast hot and cold types. In five independent BLCA cohorts, the fibroblast hot type showed worse outcomes than the cold type. Multiple cancer-related hallmark pathways are distinctively enriched in these two types. In addition, high FRGI or fibroblast hot type shows a worse immune therapeutic response. Then, four subtypes called CD8-FRG subtypes were established under the combination of FRG signature and activity of CD8+ T-cells, which turned out to be effective in predicting the prognosis and immune therapeutic response of bladder cancer in multiple independent datasets. Pathway enrichment analysis, multiple gene signatures, and epigenetic alteration characterize the CD8-FRG subtypes and provide a potential combination strategy method against bladder cancer. Conclusions In summary, we established a novel FRGI and CD8-FRG subtype by large-scale datasets and organized analyses, which could accurately predict clinical outcomes and immune therapeutic response of BLCA after surgery.


Construction of exosome-related genes risk model in kidney cell carcinoma predicts prognosis and immune therapy response

May 2024

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20 Reads

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1 Citation

Aging

Renal cell carcinoma (RCC) is one of the most prevalent types of urological cancer. Exosomes are vesicles derived from cells and have been found to promote the development of RCC, but the potential biomarker and molecular mechanism of exosomes on RCC remain ambiguous. Here, we first screened differentially expressed exosome-related genes (ERGs) by analyzing The Cancer Genome Atlas (TCGA) database and exoRBase 2.0 database. We then determined prognosis-related ERGs (PRERGs) by univariate Cox regression analysis. Gene Dependency Score (gDS), target development level, and pathway correlation analysis were utilized to examine the importance of PRERGs. Machine learning and lasso-cox regression were utilized to screen and construct a 5-gene risk model. The risk model showed high predictive accuracy for the prognosis of patients and proved to be an independent prognostic factor in three RCC datasets, including TCGA-KIRC, E-MTAB-1980, and TCGA-KIRP datasets. Patients with high-risk scores showed worse outcomes in different clinical subgroups, revealing that the risk score is robust. In addition, we found that immune-related pathways are highly enriched in the high-risk group. Activities of immune cells were distinct in high-/low-risk groups. In independent immune therapeutic cohorts, high-risk patients show worse immune therapy responses. In summary, we identified several exosome-derived genes that might play essential roles in RCC and constructed a 5-gene risk signature to predict the prognosis of RCC and immune therapy response.

Citations (1)


... Single-cell combined transcriptomics is a popular combination in developing BLCA predictive models for predicting patient survival and immune microenvironment [8][9][10] . While this approach has uncovered some gene expression heterogeneity, it has not elucidated inter-patient and intra-tumor heterogeneity. ...

Reference:

Integrated multi-omics analysis reveals key genetic, metabolic, and microbial drivers in bladder cancer insights into molecular subtyping and therapeutic approaches: A tumor marker prognostic study
Single-cell and bulk RNA-sequence identified fibroblasts signature and CD8+ T-cell - fibroblast subtype predicting prognosis and immune therapeutic response of bladder cancer, based on machine-learning bioinformatics retrospective study

International Journal of Surgery