Mingyao Huang's research while affiliated with Fujian Provincial Cancer Hospital and other places

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Publications (2)


Flow diagram of the study method. ACTL6A indicates actin‐like 6A; DepMap, Dependency Map; ER, estrogen receptor; FJMUUH, Fujian Medical University Union Hospital; HER2, human epidermal growth factor receptor 2; IHC, immunohistochemistry; NACT, neoadjuvant chemotherapy; PEG, proliferation essential gene; PR, progesterone receptor; ROC, receiver operating characteristic; RT‐qPCR, real‐time quantitative polymerase chain reaction; TCGA, The Cancer Genome Atlas; TPBC, triple‐positive breast cancer.
Identification of PEGs in TPBC. (A) Heatmap of the expression profiles of 437 differently expressed PEGs in TPBC and normal tissues. (B) Volcano plot of the distribution of differently expressed PEGs. GO‐BP (C) and KEGG (D) pathway enrichment analysis of the PEGs. GO‐BP indicates Gene Ontology Biological Processes; lfc, log2 fold change; KEGG, Kyoto Encyclopedia of Genes and Genomes; mRNA, messenger RNA; PEG, proliferation essential gene; TPBC, triple‐positive breast cancer.
Prognostic values and expression levels of 43 PEGs. (A) Univariate Cox regression analysis of 43 PEGs in patients with TPBC. (B, D) The expression difference of 43 PEGs between TPBC and normal samples. ACTL6A indicates actin‐like 6A; CI, confidence interval; PEG, proliferation essential gene; TPBC, triple‐positive breast cancer.
Construction and validation of a PEG signature for patients with TPBC. (A) Three non‐zero coefficient genes. (B) The partial likelihood deviation curve. (C) The Kaplan‐Meier curve and risk factor plot of PEG signature in the TCGA cohort. (D) The Kaplan‐Meier curve and risk factor plot of PEG signature in the GSE45255 + GSE22226 cohort. (E) The diagnostic ROC curve of PEG signature. (F) Time‐dependent ROC curves at 1‐, 3‐, and 5‐year survival of PEG signature. (G) A TPBC prognostic nomogram based on PEG signature. (H) The Kaplan‐Meier curve of PEG model. (I) The diagnostic ROC curve of PEG model. (J) Time‐dependent ROC curves at 1‐, 3‐, and 5‐year survival of PEG model. (K) Calibration plot for 1‐, 3‐, 5‐year survival probabilities. ACTL6A indicates actin‐like 6A; AUC, area under the curve; CI, confidence interval; FPR, false positive rate; PEG, proliferation essential gene; TCGA, The Cancer Genome Atlas; TPBC, triple‐positive breast cancer; TPR, true positive rate.
Association between the PEG signature and NACT response. (A) The grouping diagram and the proportion histogram showed different PEG signature scores between RCB = 0 and RCB = 2‐3 patients in the GSE22226 + GSE32603 cohort. (B) The grouping diagram and the proportion histogram showed different PEG signature scores between RD and pCR patients in the GSE22226 + GSE32603 cohort. (C) Differential expression levels of three PEGs between NACT‐resistant (n = 5) and NACT‐sensitive (n = 4) tissue specimens from the FJMUUH cohort. Gene set enrichment analysis of ACTL6A (D) and CCT2 (E, F). FDR indicates false discovery rate; NACT, neoadjuvant chemotherapy; NES, normalize enrichment score; pCR, pathological complete response; PEG, proliferation essential gene; RCB, residual cancer burden; RD, residual disease.

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Discovery of a proliferation essential gene signature and actin‐like 6A as potential biomarkers for predicting prognosis and neoadjuvant chemotherapy response in triple‐positive breast cancer
  • Article
  • Publisher preview available

February 2024

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2 Reads

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1 Citation

Cancer

Xiaofen Li

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Shiping Luo

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Wenfen Fu

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[...]

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Chuangui Song

Background Patients with triple‐positive breast cancer (TPBC) have a higher risk of recurrence and lower survival rates than patients with other luminal breast cancers. However, there are few studies on the predictive biomarkers of prognosis and treatment responses in TPBC. Methods Proliferation essential genes (PEGs) were acquired from clustered regularly interspaced short palindromic repeats‐associated protein 9 (CRISPR‐Cas9) technology, and cohorts of patients with TPBC were obtained from public databases and our cohort. To develop a TPBC‐PEG signature, Cox regression and least absolute shrinkage and selection operator regression analyses were applied. Functional analyses were performed with gene set enrichment analysis. The relationship between candidate genes and neoadjuvant chemotherapy (NACT) sensitivity was explored via real‐time quantitative polymerase chain reaction (RT‐qPCR) and immunohistochemistry (IHC) on the basis of clinical samples. Results Among 900 TPBC‐PEGs, 437 showed significant differential expression between TPBC and normal tissues. Three prognostic PEGs (actin‐like 6A [ACTL6A], chaperonin containing TCP1 subunit 2 [CCT2], and threonyl‐TRNA synthetase [TARS]) were identified and used to construct the PEG signature. Patients with high PEG signature scores exhibited a worse overall survival and lower sensitivity to NACT than patients with low PEG signature scores. RT‐qPCR results indicated that ACTL6A and CCT2 expression were significantly upregulated in patients who lacked sensitivity to NACT. IHC results showed that the ACTL6A protein was highly expressed in patients with NACT resistance and nonpathological complete responses. Conclusions This efficient PEG signature prognostic model can predict the outcomes of TPBC. Furthermore, ACTL6A expression level was associated with the response to NACT, and could serve as an important factor in predicting prognosis and drug sensitivity of patients with TPBC.

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Fig. 2 TIGIT was highly expressed in TNBC. A We employed mIHC to analyze the expression of TIGIT and CD226 on CD8 + T cells in TNBC tissues; B The expression of TIGIT in cancer and adjacent tissues was evaluated using the GEO database; C TIGIT expression in both cancerous and adjacent tissues was assessed through IHC, with a sample size of n = 3; D TIGIT expression was quantitatively evaluated through immunohistochemistry in both cancerous and adjacent tissues, with a sample size of n = 3; E The TCGA database was utilized to investigate the correlation between TIGIT expression and PD1 expression in TNBC tissues; F The TCGA database was employed to examine the correlation between TIGIT expression and TIM3 expression in TNBC tissue; G The TCGA database was utilized to analyze the correlation between TIGIT expression and LAG3 expression in TNBC tissues. The data are the mean ± SEM of the experiments
Fig. 7 Targeting CD155/TIGIT to inhibit tumor progression in vivo. A Tumor bearing model by subcutaneously injecting 4T1 cells into BALB/c mice, and treated them with anti-TIGIT mAb or IgG; B Tumor size in mice treated with anti-TIGIT mAb or IgG; C HE staining of mouse tumor tissue treated them with anti-TIGIT mAb or IgG; D Tumor volume in mice that were treated them with anti-TIGIT mAb or IgG; E IHC analyzed the expression of CD8, GLUT1, HK2, PKM2 and LDHA in tumor tissues treated them with anti-TIGIT mAb or IgG; F Tumor bearing model by subcutaneously injecting 4T1-CD155 KD or 4T1-CD155 mock cells into BALB/c mice; G Tumor size in mice treated with anti-TIGIT mAb or IgG; H HE staining of mouse tumor tissues that were injected with 4T1-CD155 KD or 4T1-CD155 mock cells; I Tumor volume in mice that were injected with 4T1-CD155 KD or 4T1-CD155 mock cells; J IHC analysis of CD8 expression in tumor tissue injected with 4T1-CD155 KD or 4T1-CD155 mock cells
The immune checkpoint TIGIT/CD155 promotes the exhaustion of CD8 + T cells in TNBC through glucose metabolic reprogramming mediated by PI3K/AKT/mTOR signaling

January 2024

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30 Reads

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1 Citation

Cell Communication and Signaling

Objective The CD155/TIGIT axis has attracted considerable interest as an emerging immune checkpoint with potential applications in cancer immunotherapy. Our research focused on investigating the role of CD155/TIGIT checkpoints in the progression of triple-negative breast cancer (TNBC). Methods We evaluated CD155 and TIGIT expression in TNBC tissues using both immunohistochemistry (IHC) and gene expression profiling. Our experiments, both in vivo and in vitro, provided evidence that inhibiting the CD155/TIGIT pathway reinstates the ability of CD8 + T cells to generate cytokines. To assess the impact of CD155/TIGIT signaling blockade, we utilized Glucose Assay Kits and Lactate Assay Kits to measure alterations in glucose and lactate levels within CD8 + T cells. We employed western blotting (WB) to investigate alterations in glycolytic-related proteins within the PI3K/AKT/mTOR pathways following the inhibition of CD155/TIGIT signaling. Results CD155 exhibits heightened expression within TNBC tissues and exhibits a negative correlation with the extent of infiltrating CD8 + T cells. Furthermore, patients with TNBC demonstrate elevated levels of TIGIT expression. Our findings indicate that the interaction between CD155 and TIGIT disrupts the glucose metabolism of CD8 + T cells by suppressing the activation of the PI3K/AKT/mTOR signaling pathway, ultimately leading to the reduced production of cytokines by CD8 + T cells. Both in vivo and in vitro experiments have conclusively demonstrated that the inhibition of CD155/TIGIT interaction reinstates the capacity of CD8 + T cells to generate cytokines. Moreover, in vivo administration of the blocking antibody against TIGIT not only inhibits tumor growth but also augments the functionality of CD8 + T lymphocytes. Conclusions Our research findings strongly suggest that CD155/TIGIT represents a promising therapeutic target for treating TNBC.