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ONCOLOGY LETTERS 21: 410, 2021 High expression levels of centromere protein A plus upregulation of the phosphatidylinositol 3‑kinase/Akt/mammalian target of rapamycin signaling pathway affect chemotherapy response and prognosis in patients with breast cancer SONGBO ZHANG*, YANYAN XIE*, TING TIAN, QIANRU YANG, YUTING ZHOU, JUANJUAN QIU, LI XU, NAN WEN, QING LV and ZHENGGUI DU Department of Breast Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China Received March 8, 2020; Accepted February 16, 2021 DOI: 10.3892/ol.2021.12671 Abstract. Centromere proteins (CENPs) are involved in overall survival. However, according to the multivariate mitosis, and CENP gene expression levels are associated analyses, only CENPA was an independent risk factor asso‑ with chemotherapy responses in patients with breast cancer. ciated with DRFS in GEO database. In addition, in the RD The present study aimed to examine the roles and underlying subgroup, patients with higher CENPA expression levels had mechanisms of the effects of CENP genes on chemotherapy a worse prognosis compared with those with lower CENPA responses and breast cancer prognosis. Using data obtained expression levels. Among patients with high expression levels from the Gene Expression Omnibus (GEO) database, correla‑ of CENPA, the PI3K/Akt/mTOR pathway was more likely tion and Cox multivariate regression analyses were used to to be activated in the RD compared with the PCR subgroup. determine the CENP genes associated with chemotherapy The same trend was observed in TCGA data. These results responses and survival in patients with breast cancer. Weighted suggested that high CENPA expression levels plus upregulation gene co‑expression network and correlation analyses were used of the PI3K/Akt/mTOR signaling pathway may affect DRFS to determine the gene modules co‑expressed with the identi‑ in patients with breast cancer. fied genes and the differential expression of gene modules associated with the pathological complete response (PCR) and Introduction residual disease (RD) subgroups. CENPA, CENPE, CENPF, CENPI, CENPJ and CENPN were associated with a high Breast cancer is the most common cancer among women nuclear grade and low estrogen and progesterone receptor worldwide, with 2.1 million newly diagnosed cases in 2018 expression levels. In addition, CENPA, CENPB, CENPC according to the World Health Organization (1). In the United and CENPO were independent factors affecting the distant States of America, 249,260 patients were diagnosed with breast relapse‑free survival (DRFS) rates in patients with breast cancer in 2016 (2). According to data released by the Cancer cancer. Patients with high expression levels of CENPA or Center of China in 2014, the total incidence of breast cancer CENPO exhibited poor prognoses, whereas those with high in China was 42.55 per 100,000 individuals, making breast expression levels of CENPB or CENPC presented with favor‑ cancer the most common malignant tumor in women (3). able prognoses. For validation between databases, the Cancer The aim of chemotherapy is to interfere with the process Genome Atlas (TCGA) database analysis also revealed that of cell proliferation; therefore, most chemotherapeutic drugs CENPA, CENPB and CENPO exerted similar effects on exert major effects on proliferating cells, including cancer cells, which are characterized by abnormal and uncontrolled proliferation (4). Chemotherapy is currently the most effective method for systemic treatment of patients with breast cancer and prevents the division and proliferation of cancer cells by Correspondence to: Dr Zhenggui Du, Department of Breast destroying cancer cell DNA and disrupting the intracellular Surgery, West China Hospital, Sichuan University, 37 Guoxue Alley, components involved in mitosis (5). In addition, certain chemo‑ Wuhou, Chengdu, Sichuan 610041, P.R. China therapeutic drugs kill cancer cells by inducing apoptosis (6). E‑mail: [email protected] Centromere proteins (CENPs) serve important roles *Contributed equally in centromere function and mitosis; for example, CENPA encodes centromere protein A, which contains a variant of Key words: breast cancer, centromere protein A, neoadjuvant histone H3 that is specifically expressed on the centromere chemotherapy, phosphatidylinositol 3‑kinase/Akt/mammalian target and kinetochore of the chromosome and is targeted by of rapamycin pathway, residual disease microfilaments during mitosis, resulting in the separation of centromeres during mitosis (7). CENPB supports kinetochore formation and contributes to the maintenance of chromosome 2 ZHANG et al: CENPA PLUS UPREGULATION OF PI3K/Akt/mTOR PATHWAY IN BREAST CANCER segregation fidelity (8). CENPC serves as a scaffold for the Weighted correlation network analysis (WGCNA). Data of this specific recruitment of essential kinetochore proteins and part were extracted from the GEO database publicly mentioned links centromeres and kinetochores in mitosis (9). Thus, above. The ‘WGCNA’ package in the R software (21) was CENPs are important components of chromosomes during used to perform scale‑free network topology analysis of mitosis and can regulate the proliferation of tumor cells. microarray expression data of breast cancer samples. Standard Therefore, we hypothesized that the expression of CENP genes WGCNA parameters were used for analysis. Using WGCNA, may be associated with the responses of breast tumor cells to a co‑expression module of genes associated with patient chemotherapy and may affect patient survival. clinicopathological characteristics was extracted from the The aim of the present study was to systematically explore breast cancer data for subsequent analysis. the associations between 13 CENP genes and chemotherapy responses in patients with breast cancer and to identify genes Kyoto Encyclopedia of Genes and Genomes (KEGG) that were most relevant to the patient prognosis. enrichment analysis of co‑expression modules. KEGG pathway analyses were performed to determine the biological Materials and methods functions of the genes in the green module identified by WGCNA to be highly correlated with CENPA using the Gene datasets. Five breast cancer mRNA expression profiles, DAVID Bioinformatics Tool (version 6.8; https://david.ncifcrf. namely GSE20194 (10,11), GSE20271 (12), GSE22093 (13), gov/home.jsp). P<0.05 was considered to indicate a statistically GSE23988 (13) and GSE25066 (14,15), were extracted significant result. A total of 12 records were extracted. Graphs from the Gene Expression Omnibus (GEO; http://www. were generated using the ‘ggplot2’ R package (22). ncbi.nlm.nih.gov/geo/) database using the key words ‘breast cancer’, ‘neoadjuvant chemotherapy’ and ‘pre‑operative Statistical analysis. Data are presented as the mean, range, chemotherapy’. Studies in which gene expression levels were standard deviation or proportion. Statistical analyses were analyzed in genome profiling data using pretreatment biopsies performed using R software (version 3.5). Univariate and from patients who underwent neoadjuvant chemotherapy multivariate survival analyses were performed using Cox were included. The first four datasets (GSE20194, GSE20271, regression models. The χ2 test was used to analyze the GSE22093 and GSE23988) were used as the training dataset associations between CENP expression levels and patient (n=620 patients), and the fifth dataset (GSE25066) was used clinicopathological characteristics. Kaplan‑Meier curves as the validation dataset (n=508 patients). The mRNA expres‑ were used for survival analysis, and the Cramér‑von Mises sion profiles of breast cancer from The Cancer Genome Atlas and log‑rank tests were used for the curves with and without (TCGA; https://portal.gdc.cancer.gov/) database were used crossovers, respectively. Comparisons of correlations were for validation. In the GEO database, the result of pathological performed using the ‘correlation coefficients’ module in response was classified into pathological complete response MedCalc software (Version 19.6.4; MedCalc Software, Ltd.). (PCR) and residual disease (RD). In TCGA database used for For comparisons of means, continuous variables between validation, the result of pathological response was classified groups were analyzed using the independent‑samples into clinical complete response (CCR) and RD. Student's t‑test or one‑way ANOVA. Comparisons of rates were performed using the χ2 test. P<0.05 was considered to Immunohistochemistry and molecular classification. indicate a statistically significant difference. Immunohistochemistry and molecular classification results were extracted from the GEO database. The hormone receptor Results status of a tumor was defined as positive when the immuno‑ histochemistry results were positive for either estrogen receptor Basic characteristics of the study population. In the present (ER) or progesterone receptor (PR) in ≥1% of cells and as nega‑ study, five datasets including a total of 1,128 patients were eval‑ tive when both ER and PR were negative. According to global uated. The specific characteristics of each study are presented consensus (16), HER2 expression status was defined as negative in Table I. The cancer grades were primarily II and III, and when the immunohistochemistry results were negative or 1+, the nuclear grades were mainly G2 and G3. All patients and as positive when the results were 3+. HER2 positivity was in the present study received neoadjuvant chemotherapy, and evaluated according to the results of fluorescent
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  • 1 AGING Supplementary Table 2

    1 AGING Supplementary Table 2

    SUPPLEMENTARY TABLES Supplementary Table 1. Details of the eight domain chains of KIAA0101. Serial IDENTITY MAX IN COMP- INTERFACE ID POSITION RESOLUTION EXPERIMENT TYPE number START STOP SCORE IDENTITY LEX WITH CAVITY A 4D2G_D 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ B 4D2G_E 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ C 6EHT_D 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ D 6EHT_E 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ E 6GWS_D 41-72 41 72 100 100 3.2Å PCNA X-RAY DIFFRACTION √ F 6GWS_E 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ G 6GWS_F 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ H 6IIW_B 2-11 2 11 100 100 1.699Å UHRF1 X-RAY DIFFRACTION √ www.aging-us.com 1 AGING Supplementary Table 2. Significantly enriched gene ontology (GO) annotations (cellular components) of KIAA0101 in lung adenocarcinoma (LinkedOmics). Leading Description FDR Leading Edge Gene EdgeNum RAD51, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, CENPW, HJURP, NDC80, CDCA5, NCAPH, BUB1, ZWILCH, CENPK, KIF2C, AURKA, CENPN, TOP2A, CENPM, PLK1, ERCC6L, CDT1, CHEK1, SPAG5, CENPH, condensed 66 0 SPC24, NUP37, BLM, CENPE, BUB3, CDK2, FANCD2, CENPO, CENPF, BRCA1, DSN1, chromosome MKI67, NCAPG2, H2AFX, HMGB2, SUV39H1, CBX3, TUBG1, KNTC1, PPP1CC, SMC2, BANF1, NCAPD2, SKA2, NUP107, BRCA2, NUP85, ITGB3BP, SYCE2, TOPBP1, DMC1, SMC4, INCENP. RAD51, OIP5, CDK1, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, ESCO2, CENPW, HJURP, TTK, NDC80, CDCA5, BUB1, ZWILCH, CENPK, KIF2C, AURKA, DSCC1, CENPN, CDCA8, CENPM, PLK1, MCM6, ERCC6L, CDT1, HELLS, CHEK1, SPAG5, CENPH, PCNA, SPC24, CENPI, NUP37, FEN1, chromosomal 94 0 CENPL, BLM, KIF18A, CENPE, MCM4, BUB3, SUV39H2, MCM2, CDK2, PIF1, DNA2, region CENPO, CENPF, CHEK2, DSN1, H2AFX, MCM7, SUV39H1, MTBP, CBX3, RECQL4, KNTC1, PPP1CC, CENPP, CENPQ, PTGES3, NCAPD2, DYNLL1, SKA2, HAT1, NUP107, MCM5, MCM3, MSH2, BRCA2, NUP85, SSB, ITGB3BP, DMC1, INCENP, THOC3, XPO1, APEX1, XRCC5, KIF22, DCLRE1A, SEH1L, XRCC3, NSMCE2, RAD21.
  • Autosomal Recessive Primary Microcephaly

    Autosomal Recessive Primary Microcephaly

    Mahmood et al. Orphanet Journal of Rare Diseases 2011, 6:39 http://www.ojrd.com/content/6/1/39 REVIEW Open Access Autosomal recessive primary microcephaly (MCPH): clinical manifestations, genetic heterogeneity and mutation continuum Saqib Mahmood1, Wasim Ahmad2 and Muhammad J Hassan3* Abstract Autosomal Recessive Primary Microcephaly (MCPH) is a rare disorder of neurogenic mitosis characterized by reduced head circumference at birth with variable degree of mental retardation. In MCPH patients, brain size reduced to almost one-third of its original volume due to reduced number of generated cerebral cortical neurons during embryonic neurogensis. So far, seven genetic loci (MCPH1-7) for this condition have been mapped with seven corresponding genes (MCPH1, WDR62, CDK5RAP2, CEP152, ASPM, CENPJ, and STIL) identified from different world populations. Contribution of ASPM and WDR62 gene mutations in MCPH World wide is more than 50%. By and large, primary microcephaly patients are phenotypically indistinguishable, however, recent studies in patients with mutations in MCPH1, WDR62 and ASPM genes showed a broader clinical and/or cellular phenotype. It has been proposed that mutations in MCPH genes can cause the disease phenotype by disturbing: 1) orientation of mitotic spindles, 2) chromosome condensation mechanism during embryonic neurogenesis, 3) DNA damage- response signaling, 4) transcriptional regulations and microtubule dynamics, 5) certain unknown centrosomal mechanisms that control the number of neurons generated by neural precursor cells. Recent discoveries of mammalian models for MCPH have open up horizons for researchers to add more knowledge regarding the etiology and pathophysiology of MCPH. High incidence of MCPH in Pakistani population reflects the most probable involvement of consanguinity.