文献类型：期刊文献

英文题名：Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis

作者：Tang, Mingzheng[1,2,3,4];Rong, Yao[2,3,4,6];Li, Xiaofeng[2];Pan, Haibang[2];Tao, Pengxian[5];Wu, Zhihang[2];Liu, Songhua[2,3,4,6];Tang, Renmei[7];Liu, Zhilong[8];Cai, Hui[3,4,5]

第一作者：Tang, Mingzheng

通信作者：Cai, H[1];Cai, H[2];Cai, H[3];Tang, RM[4];Liu, ZL[5]

机构：[1]Hainan Med Univ, Affiliated Hosp 2, Dept Breast & Thyroid Surg, Haikou, Peoples R China;[2]Gansu Univ Chinese Med, Clin Med Coll 1, Lanzhou, Peoples R China;[3]Gansu Prov Hosp, Key Lab Mol Diagnost & Precis Med Surg Oncol Gansu, Lanzhou, Peoples R China;[4]Gansu Prov Hosp, NHC Key Lab Diag & Therapy Gastrointestinal Tumor, Lanzhou, Peoples R China;[5]Gansu Prov Hosp, Gen Surg Clin Med Ctr, Lanzhou, Peoples R China;[6]Gen Hosp Southern Theater Command, Gen Surg Dept, Guangzhou, Peoples R China;[7]Qionghai Peoples Hosp Breast & Thyroid Surg, Qionghai, Peoples R China;[8]Gansu Prov Hosp, Dept Anesthesiol, Lanzhou, Peoples R China

第一机构：Hainan Med Univ, Affiliated Hosp 2, Dept Breast & Thyroid Surg, Haikou, Peoples R China

通信机构：[1]corresponding author), Gansu Prov Hosp, Key Lab Mol Diagnost & Precis Med Surg Oncol Gansu, Lanzhou, Peoples R China;[2]corresponding author), Gansu Prov Hosp, NHC Key Lab Diag & Therapy Gastrointestinal Tumor, Lanzhou, Peoples R China;[3]corresponding author), Gansu Prov Hosp, Gen Surg Clin Med Ctr, Lanzhou, Peoples R China;[4]corresponding author), Qionghai Peoples Hosp Breast & Thyroid Surg, Qionghai, Peoples R China;[5]corresponding author), Gansu Prov Hosp, Dept Anesthesiol, Lanzhou, Peoples R China.

年份：2024

卷号：24

期号：1

外文期刊名：BMC CANCER

收录：;Scopus(收录号：2-s2.0-85204512700);WOS:【SCI-EXPANDED(收录号:WOS:001316979500004)】；

基金：This work was supported by grants from Natural Science Foundation of Gansu Province (No.23JRRA1756).

语种：英文

外文关键词：Breast cancer; Anoikis; Immunity; Prognosis

摘要：BackgroundBreast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear.MethodThis study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells.ResultsOne hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan-Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels.ConclusionARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.