Screening and identification of hub genes in bladder cancer by bioinformatics analysis and KIF11 is a potential prognostic biomarker

Mo, Xiaocong; Zhang, Zi-Tong; Song, Mengjia; Zhou, Ziqi; Zeng, Jianxiong; Du, Yufei; Sun, Fengze; Yang, Jin-Hao; He, Junyi; Huang, Yue; Weng, Desheng

doi:10.3892/ol.2021.12466

Cited by 10 publications

(8 citation statements)

References 37 publications

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“…This outcome could be influenced by different data processing or updated survival information. Thus, we further used, OncoLnc (http://www.oncolnc.org/) and the Kaplan Meier plotter method (27,28) to further update and analyze these data, the final results were consistent with the results of our study.…”

Section: Discussionsupporting

confidence: 75%

A pan-cancer analysis of the oncogenic role of Keratin 17 (KRT17) in human tumors

Li¹,

Teng²,

Wu³

et al. 2021

Transl Cancer Res TCR

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Background Although new evidence from cells or animals suggests a relationship between Keratin 17 ( KRT17 ) and cancer, no pan-cancer analysis is currently available. Methods The expression level of KRT17 in generalized carcinoma was detected by the Tumor Immune Estimation Resource, version 2 (TIMER2) database, and then verified the protein expression of KRT17 in different cancer species in UALCAN database, and analyzed the relationship between the expression level of KRT17 and the clinical stage and survival of different cancers. We further explored the genetic variation of KRT17 in different tumor types included in The Cancer Genome Atlas (TCGA) and the specific mutations in each domain. The changes of KRT17 protein phosphorylation levels and protein expression levels at different phosphorylation sites in different tumors were explored. TIMER2 database was used to explore the potential relationship between the infiltration level of different immune cells and KRT17 gene expression in different TCGA cancer types. Finally, the protein binding to KRT17 and genes related to KRT17 expression were explored by STRING database and TCGA database. Results KRT17 is overexpressed in most malignancies, and we observed a distinct relationship between KRT17 expression and tumor patient prognosis. Enhanced phosphorylation levels of S13, S24, S32, and S39 were observed in several tumors, such as lung adenocarcinoma (LUAD), colon and ovarian cancers, and uterine corpus endometrial carcinoma (UCEC). Intermediate filament cytoskeleton and keratinization may be simultaneously acting with KRT17 on tumor pathogenesis. Conclusions Our pan-cancer analysis provides relatively complete information on the oncogenic functions of KRT17 in various cancers.

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Section: Discussionsupporting

confidence: 75%

A pan-cancer analysis of the oncogenic role of Keratin 17 (KRT17) in human tumors

Li¹,

Teng²,

Wu³

et al. 2021

Transl Cancer Res TCR

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“…Apart from CENPA-containing chromatin assembly, it is involved in the regulation of DNA binding activity, chromosomal segregation, cell mitosis, and regulation of protein-containing complex assembly. Our analysis confirmed previous bioinformatics studies in which HJURP was suggested to be a key hub gene for BCa [157,209]. R. Cao et al found that HJURP is highly overexpressed in BCa tissues at both mRNA and protein levels and suggested that HJURP might regulate cell proliferation and apoptosis in BCa by acting on the PPARγ-SIRT1 negative feedback loop [210].…”

Section: Discussionsupporting

confidence: 91%

“…Hence, CDT1 is highly associated with the cell cycle, cell division, DNA replication, and mitosis. In our study, CDT1 was overexpressed in bladder tissue and urine of BCa patients, which was also confirmed by X. C. Mo et al [157], whereas it was significantly under-expressed in the blood plasma of BCa patients. CDT1 has been considered to contribute to cell proliferation and genome instability [158] and to be often misregulated in cancer [159].…”

Section: Discussionsupporting

confidence: 90%

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Sarafidis

Lambrou

Zoumpourlis

et al. 2022

Cancers

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Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

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“…KIF11, also known as kinesin spindle protein, is important during mitogenesis and cell proliferation [ 40 ]. Furthermore, KIF11 is upregulated in various human cancers, including bladder cancer, renal clear cell carcinomas, prostate carcinomas, meningiomas, breast cancer, and gastric ccancer [ 41 , 42 ]. Because high levels of the KIF11 expression indicate robust cell proliferation, anti-KIF11 therapy is emerging as a promising cancer therapy approach.…”

Section: Discussionmentioning

confidence: 99%

A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA

Chen

2022

BioMed Research International

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Background. Bladder cancer (BLCA) is one of the most common urological malignancies globally, posing a severe threat to public health. In combination with protein-protein interaction (PPI) network analysis of proteomics, Gene Set Variation Analysis (GSVA) and “CancerSubtypes” package of R software for transcriptomics can help identify biomarkers related to BLCA prognosis. This will have significant implications for prevention and treatment. Method. BLCA data were downloaded from The Cancer Genome Atlas (TCGA) database and GEO database (GSE13507). GSVA analysis converted the gene expression matrix to the gene set expression matrix. “CancerSubtypes” classified patients into three subtypes and established a prognostic model based on differentially expressed gene sets (DEGSs) among the three subtypes. For genes from prognosis-related DEGSs, functional and pathway enrichment analyses and PPI network analysis were carried out. The Human Protein Atlas (HPA) database was used for validation. Finally, the proportion of tumor-infiltrating immune cells (TIICs) was determined using the CIBERSORT algorithm. Results. In total, 414 tumor samples and 19 adjacent-tumor samples were obtained from TCGA, with 145 samples belonging to subtype A, 126 samples belonging to subtype B, and 136 samples belonging to subtype C. Then, we identified 83 DEGSs and constituted a prognostic signature with two of them: “GSE1460_CD4_THYMOCYTE_VS_THYMIC_STROMAL_CELL_DN” and “MODULE_253.” Finally, five subnets of two PPI networks were established, and nine core proteins were obtained: CDH2, COL1A1, EIF2S2, PSMA3, NAA10, DNM1L, TUBA4A, KIF11, and KIF23. The HPA database confirmed the expression of the nine core proteins in BLCA tissues. Furthermore, EIF2S2, PSMA3, DNM1L, and TUBA4A could be novel BLCA prognostic biomarkers. Conclusions. In this study, we discovered two gene sets linked to BLCA prognosis. PPI analysis confirmed the network’s core proteins, and several newly discovered biomarkers of BLCA prognosis were identified.

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Screening and identification of hub genes in bladder cancer by bioinformatics analysis and KIF11 is a potential prognostic biomarker

Cited by 10 publications

References 37 publications

A pan-cancer analysis of the oncogenic role of Keratin 17 (KRT17) in human tumors

A pan-cancer analysis of the oncogenic role of Keratin 17 (KRT17) in human tumors

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA

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