Therapeutic Landscape of Human Epidermal Growth Factor Receptor 2–Positive Breast Cancer

Xie, Jun; Zou, Yutian; Gao, Ting; Xie, Liming; Tan, Dandan; Xie, Xiaoming

doi:10.1177/10732748221099230

Cited by 13 publications

(11 citation statements)

References 82 publications

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“…We obtained a set of 185 post-selected genes by combining two GEO validation sets. Instead of using the Lasso method to model multiple marker genes, we used a metric that aggregated the expressions of these genes to construct the final model ( Xie et al, 2022a ; Xie et al, 2022b ). Although the Lasso method aims to minimize the number of variables by compressing the coefficients of the variables and setting certain regression coefficients to zero through a penalty function, it has inherent drawbacks, such as not yielding an explicit solution and the estimated results being less stable and prone to error.…”

Section: Discussionmentioning

confidence: 99%

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients

Yuan¹,

Zhao

et al. 2023

Front. Pharmacol.

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The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC.

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Section: Discussionmentioning

confidence: 99%

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients

Yuan¹,

Zhao

et al. 2023

Front. Pharmacol.

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“…Recently, immunotherapy has emerged as the first-line anti-cancer strategy. Identification of the expression patterns of immunotherapeutic targets may help to improve the survival of patients ( 47 , 48 ). Studies have demonstrated that compared with monotherapy, combination therapy with PD1 and CTLA4 inhibitors results in better survival improvement in several cancers ( 49 – 52 ).…”

Section: Discussionmentioning

confidence: 99%

A degradome-based prognostic signature that correlates with immune infiltration and tumor mutation burden in breast cancer

Luo

Chen

et al. 2023

Front. Immunol.

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IntroductionFemale breast cancer is the most common malignancy worldwide, with a high disease burden. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity. Dysregulation of the degradome may disrupt cellular homeostasis and trigger carcinogenesis. Thus we attempted to understand the prognostic role of degradome in breast cancer by means of establishing a prognostic signature based on degradome-related genes (DRGs) and assessed its clinical utility in multiple dimensions.MethodsA total of 625 DRGs were obtained for analysis. Transcriptome data and clinical information of patients with breast cancer from TCGA-BRCA, METABRIC and GSE96058 were collected. NetworkAnalyst and cBioPortal were also utilized for analysis. LASSO regression analysis was employed to construct the degradome signature. Investigations of the degradome signature concerning clinical association, functional characterization, mutation landscape, immune infiltration, immune checkpoint expression and drug priority were orchestrated. Cell phenotype assays including colony formation, CCK8, transwell and wound healing were conducted in MCF-7 and MDA-MB-435S breast cancer cell lines, respectively.ResultsA 10-gene signature was developed and verified as an independent prognostic predictor combined with other clinicopathological parameters in breast cancer. The prognostic nomogram based on risk score (calculated based on the degradome signature) showed favourable capability in survival prediction and advantage in clinical benefit. High risk scores were associated with a higher degree of clinicopathological events (T4 stage and HER2-positive) and mutation frequency. Regulation of toll-like receptors and several cell cycle promoting activities were upregulated in the high-risk group. PIK3CA and TP53 mutations were dominant in the low- and high-risk groups, respectively. A significantly positive correlation was observed between the risk score and tumor mutation burden. The infiltration levels of immune cells and the expressions of immune checkpoints were significantly influenced by the risk score. Additionally, the degradome signature adequately predicted the survival of patients undergoing endocrinotherapy or radiotherapy. Patients in the low-risk group may achieve complete response after the first round of chemotherapy with cyclophosphamide and docetaxel, whereas patients in the high-risk group may benefit from 5-flfluorouracil. Several regulators of the PI3K/AKT/mTOR signaling pathway and the CDK family/PARP family were identified as potential molecular targets in the low- and high-risk groups, respectively. In vitro experiments further revealed that the knockdown of ABHD12 and USP41 significantly inhibit the proliferation, invasion and migration of breast cancer cells.ConclusionMultidimensional evaluation verified the clinical utility of the degradome signature in predicting prognosis, risk stratification and guiding treatment for patients with breast cancer.

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“…Breast cancer accounts for a significant portion of the global cancer burden and has become one of the most common tumors in the world, as well as the cancer type with the highest morbidity and mortality in women [25][26][27]. It can be seen that the age trend of breast cancer onset is getting younger and younger, which harms women's physical and mental health [28].…”

Section: Discussionmentioning

confidence: 99%

Significance of liquid-liquid phase separation (LLPS)-related genes in breast cancer: a multi-omics analysis

Xie,

Chen,

et al. 2023

Aging

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Currently, the role of liquid-liquid phase separation (LLPS) in cancer has been preliminarily explained. However, the significance of LLPS in breast cancer is unclear. In this study, single cell sequencing datasets GSE188600 and GSE198745 for breast cancer were downloaded from the GEO database. Transcriptome sequencing data for breast cancer were downloaded from UCSC database. We divided breast cancer cells into high-LLPS group and low-LLPS group by down dimension clustering analysis of single-cell sequencing data set, and obtained differentially expressed genes between the two groups. Subsequently, weighted co-expression network analysis (WGCNA) was performed on transcriptome sequencing data, and the module genes most associated with LLPS were obtained. COX regression and Lasso regression were performed and the prognostic model was constructed. Subsequently, survival analysis, principal component analysis, clinical correlation analysis, and nomogram construction were used to evaluate the significance of the prognostic model. Finally, cell experiments were used to verify the function of the model's key gene, PGAM1. We constructed a LLPS-related prognosis model consisting of nine genes: POLR3GL, PLAT, NDRG1, HMGB3, HSPH1, PSMD7, PDCD2, NONO and PGAM1. By calculating LLPS-related risk scores, breast cancer patients could be divided into high-risk and lowrisk groups, with the high-risk group having a significantly worse prognosis. Cell experiments showed that the activity, proliferation, invasion and healing ability of breast cancer cell lines were significantly decreased after knockdown of the key gene PGAM1 in the model. Our study provides a new idea for prognostic stratification of breast cancer and provides a novel marker: PGAM1.

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Therapeutic Landscape of Human Epidermal Growth Factor Receptor 2–Positive Breast Cancer

Cited by 13 publications

References 82 publications

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients

A degradome-based prognostic signature that correlates with immune infiltration and tumor mutation burden in breast cancer

Significance of liquid-liquid phase separation (LLPS)-related genes in breast cancer: a multi-omics analysis

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