Endometrial cancer (EC) is commonly diagnosed cancer in women, and the prognosis of advanced types of EC is extremely poor. Kinesin family member 2C (KIF2C) has been reported as an oncogene in cancers. However, its pathophysiological roles and the correlation with tumor-infiltrating lymphocytes in EC remain unclear. The mRNA and protein levels of KIF2C in EC tissues were detected by qRT-PCR, Western blot (WB), and IHC. CCK8, Transwell, and colony formation assay were applied to assess the effects of KIF2C on cell proliferation, migration, and invasion. Cell apoptosis and cell cycle were analyzed by flow cytometry. The antitumor effect was further validated in the nude mouse xenograft cancer model and humanized mouse model. KIF2C expression was higher in EC. Knockdown of KIF2C prolonged the G1 phases and inhibited EC cell proliferation, migration, and invasion in vitro. Bioinformatics analysis indicated that KIF2C is negatively correlated with the infiltration level of CD8+ T cells but positively with the poor prognosis of EC patients. The apoptosis of CD8+ T cell was inhibited after the knockdown of KIF2C and was further inhibited when it is combined with anti-PD1. Conversely, compared to the knockdown of KIF2C expression alone, the combination of anti-PD1 further promoted the apoptosis of Ishikawa and RL95-2 cells. Moreover, the knockdown of KIF2C inhibited the expression of Ki-67 and the growth of tumors in the nude mouse xenograft cancer model. Our study found that the antitumor efficacy was further evaluated by the combination of anti-PD1 and KIF2C knockdown in a humanized mouse model. This study indicated that KIF2C is a novel prognostic biomarker that determines cancer progression and also a target for the therapy of EC and correlated with tumor immune cells infiltration in EC.

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KIF2C Is a Novel Prognostic Biomarker and Correlated with Immune Infiltration in Endometrial Cancer.

Objective: To investigate the correlation of fibronectin 1 (FN1) expression with prognosis and tumor-infiltrating immune cells in breast cancer (BRCA). Methods: FN1 mRNA and protein expressions were analyzed through Tumor Immune Estimation Resource (TIMER), Gene Set Cancer Analysis (GSCA), Human Protein Atlas (HPA) databases, and immunohistochemical analysis. The clinicopathological characteristics and genetic factors affecting the FN1 mRNA expression were assessed by various public databases. Then, we analyzed the prognostic value of FN1 in BRCA by Kaplan-Meier plotter, receiver operating characteristic, and Cox regression analyses. Further, the UCSC Xena database was used to retrieve TCGA-BRCA expression profiles for functional enrichment analysis and immune cell infiltration analysis. The potential drugs for the BRCA patients with high- FN1 expression were identified using the connectivity map analysis. Results: FN1 was upregulated in BRCA tissues compared with normal tissues. High FN1 mRNA expression was correlated with poor clinical outcomes and had good performance in predicting the survival status of BRCA patients. Further, Cox regression analysis showed that FN1 was an independent prognostic factor for predicting the overall survival of patients with BRCA. Moreover, hypermethylation of FN1 contributed to a better prognosis for BRCA patients. Functional enrichment analyses revealed the ECM-receptor interaction pathway and focal adhesion as the common pathways. Moreover, FN1 showed a significant association with tumor-infiltrating immune cells and immune checkpoint inhibitors. Several drugs such as telmisartan, malotilate, and seocalcitol may have therapeutic effects in BRCA patients with high FN1 expression. Conclusion: FN1 might serve as a novel prognostic biomarker and a novel therapeutic target for BRCA. Besides, the association of FN1 with immune cells and immune checkpoint inhibitors may provide assistance for BRCA treatment.

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FN1 overexpression is correlated with unfavorable prognosis and immune infiltrates in breast cancer

The therapeutic effects of standard cyclophosphamide, doxorubicin, vincristine and prednisone (CHOP) therapy for prevalent lymphoma diffuse large B-cell lymphoma (DLBC, DLBCL) still require improvement. Cancer-related aurora-kinase-A (AURKA) may work as a target for DLBCL treatment and its effect on CHOP therapy was investigated in the present study. The Gene Expression Profiling Interactive Analysis 2 was applied to analyze AURKA expression in DLBC tumor tissues and normal lymphoid tissues. The DLBCL tissues and normal lymphoid tissues were obtained from the DLBCL patients and healthy volunteers. Clinic data of patients were recorded, and AURKA expression in tissues and cells was detected and analyzed using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry. After AURKA in DLBCL cells was silenced or overexpressed and treated with CHOP, viability and apoptosis were detected by Cell Counting Kit-8 (CCK-8) assay and flow cytometry. Expressions of AURKA, β-Catenin, phosphorylated (p)-β-Catenin, extracellular signal-regulated protein kinase (ERK1/2), p-ERK1/2 and RAS were detected using qRT-PCR and Western blot. AURKA was highly expressed in DLBCL tissues and cells. Silencing AURKA inhibited AURKA expression and viability, but promoted apoptosis of DLBCL cells. CHOP had no obvious effects on AURKA expression while reducing viability and promoting apoptosis of DLBCL cells. Silencing AURKA enhanced the effects of CHOP on cell apoptosis of DLBCL cells by inhibiting the expressions of RAS and β-Catenin as well as the ratio of p-ERK1/2/ERK1/2 and promoting the ratio of p-β-Catenin/β-Catenin. Silencing AURKA reinforced the therapeutic effects of CHOP on reducing viability and promoting apoptosis of DLBCL cell via repressing β-Catenin and RAS-ERK1/2 pathway.

Silencing Aurora-kinase-A (AURKA) reinforced the sensitivity of diffuse large B-cell lymphoma cells to cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) via suppressing β-Catenin and RAS-extracellular signal-regulated protein kinase (ERK1/2) pathway.

Identification of crucial hub genes and potential molecular mechanisms in breast cancer by integrated bioinformatics analysis and experimental validation

ASPM is a protein encoded by primary microcephaly 5 (MCPH5) and is responsible for ensuring spindle position during mitosis and the symmetrical division of neural stem cells. We recently reported that ASPM promotes homologous recombination (HR) repair of DNA double strand breaks. However, its potential role in DNA replication and replication stress response remains elusive. Interestingly, we found that ASPM is dispensable for DNA replication under unperturbed conditions. However, ASPM is enriched at stalled replication forks in a RAD17-dependent manner in response to replication stress and promotes RAD9 and TopBP1 loading onto chromatin, facilitating ATR-CHK1 activation. ASPM depletion results in failed fork restart and nuclease MRE11-mediated nascent DNA degradation at the stalled replication fork. The overall consequence is chromosome instability and the sensitization of cancer cells to replication stressors. These data support a role for ASPM in loading RAD17-RAD9/TopBP1 onto chromatin to activate the ATR-CHK1 checkpoint and ultimately ensure genome stability. 

ASPM promotes ATR-CHK1 activation and stabilizes stalled replication forks in response to replication stress

Breast cancer (BC) is a malignancy with high incidence among women in the world. This study aims to screen key genes and potential prognostic biomarkers for BC using bioinformatics analysis. Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and then the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed to investigate the biological function of DEGs. Additionally, hub genes were screened by constructing a protein-protein interaction (PPI) network. Then, we explored the prognostic value and molecular mechanism of these hub genes using Kaplan-Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). As a result, 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. The DEGs were mainly related to cell cycles and cell proliferation by GO and KEGG pathway analysis. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree were identified initially, among which, 11 hub genes were significantly correlated with the prognosis of BC patients based on the Kaplan-Meier-plotter. GSEA reviewed that these hub genes correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. In conclusion, this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

https://www.tandfonline.com/doi/pdf/10.1080/21655979.2021.1960775

Yuanping Dai

Papers

Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis.