Benefits of Targeted Molecular Therapy to Immune Infiltration and Immune-Related Genes Predicting Signature in Breast Cancer

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

OBJECTIVES

Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m¹A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m¹A RNA methylation-related genes in BRCA is indistinct.

METHODS

The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m¹A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m¹A-related genes were selected through overlapping m¹A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m¹A score subgroups. The m¹A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR).

RESULTS

Eighty-five differentially expressed m¹A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues.

CONCLUSIONS

An m¹A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.

SPOCK2 Promotes the Malignant Behavior of Ovarian Cancer via Regulation of the Wnt/β-Catenin Signaling Pathway

Background

Ovarian cancer (OC) is a common clinical gynecological disease, which seriously threatens women's health and life. We investigated the roles of SPOCK2 in OC and its associated molecular mechanism in the current study.

Methods

The expressions and prognostic value of SPOCK2 in OC were identified using the clinical data and data from the GEPIA database. Then, SPOCK2 silence was implemented to search functions of SPOCK2 in OC cells. CCK-8 was used to examine cell proliferation. Cell apoptosis was detected by flow cytometry. The OC cell invasion and migration were evaluated by transwell assays.

Results

Overexpressed SPOCK2 was identified in OC, which was correlated with poor prognosis and a shorter survival rate. SPOCK2 downregulation significantly suppressed OC cell proliferation, migration, and invasion, and cell apoptosis was markedly promoted by SPOCK2 silence. Meanwhile, SPOCK2 knockdown could effectively suppress Wnt/β-catenin.

Conclusion

SPOCK2 exerted crucial functions in OC progression and could serve as a promising candidate for OC targeted therapy.