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

Eg5 and Diseases: From the Well-Known Role in Cancer to the Less-Known Activity in Noncancerous Pathological Conditions

Eg5 is a protein encoded by KIF11 gene and is primarily involved in correct mitotic cell division. It is also involved in nonmitotic processes such as polypeptide synthesis, protein transport, and angiogenesis. The scientific literature sheds light on the ubiquitous functions of KIF11 and its involvement in the onset and progression of different pathologies. This review focuses attention on two main points: (1) the correlation between Eg5 and cancer and (2) the involvement of Eg5 in noncancerous conditions. Regarding the first point, several tumors revealed an overexpression of this kinesin, thus pushing to look for new Eg5 inhibitors for clinical practice. In addition, the evaluation of Eg5 expression represents a crucial step, as its overexpression could predict a poor prognosis for cancer patients. Referring to the second point, in specific pathological conditions, the reduced activity of Eg5 can be one of the causes of pathological onset. This is the case of Alzheimer's disease (AD), in which Aβ and Tau work as Eg5 inhibitors, or in acquired immune deficiency syndrome (AIDS), in which Tat-mediated Eg5 determines the loss of CD4+ T-lymphocytes. Reduced Eg5 activity, due to mutations of KIF11 gene, is also responsible for pathological conditions such as microcephaly with or without chorioretinopathy, lymphedema, or intellectual disability (MCLRI) and familial exudative vitreous retinopathy (FEVR). In conclusion, this review highlights the double impact that overexpression or loss of function of Eg5 could have in the onset and progression of different pathological situations. This emphasizes, on one hand, a possible role of Eg5 as a potential biomarker and new target in cancer and, on the other hand, the promotion of Eg5 expression/activity as a new therapeutic strategy in different noncancerous diseases.

Mitotic Functions and Characters of KIF11 in Cancers

Mitosis mediates the accurate separation of daughter cells, and abnormalities are closely related to cancer progression. KIF11, a member of the kinesin family, plays a vital role in the formation and maintenance of the mitotic spindle. Recently, an increasing quantity of data have demonstrated the upregulated expression of KIF11 in various cancers, promoting the emergence and progression of cancers. This suggests the great potential of KIF11 as a prognostic biomarker and therapeutic target. However, the molecular mechanisms of KIF11 in cancers have not been systematically summarized. Therefore, we first discuss the functions of the protein encoded by KIF11 during mitosis and connect the abnormal expression of KIF11 with its clinical significance. Then, we elucidate the mechanism of KIF11 to promote various hallmarks of cancers. Finally, we provide an overview of KIF11 inhibitors and outline areas for future work.

Polygenic risk modeling of tumor stage and survival in bladder cancer

Introduction

Bladder cancer assessment with non-invasive gene expression signatures facilitates the detection of patients at risk and surveillance of their status, bypassing the discomforts given by cystoscopy. To achieve accurate cancer estimation, analysis pipelines for gene expression data (GED) may integrate a sequence of several machine learning and bio-statistical techniques to model complex characteristics of pathological patterns.

Methods

Numerical experiments tested the combination of GED preprocessing by discretization with tree ensemble embeddings and nonlinear dimensionality reductions to categorize oncological patients comprehensively. Modeling aimed to identify tumor stage and distinguish survival outcomes in two situations: complete and partial data embedding. This latter experimental condition simulates the addition of new patients to an existing model for rapid monitoring of disease progression. Machine learning procedures were employed to identify the most relevant genes involved in patient prognosis and test the performance of preprocessed GED compared to untransformed data in predicting patient conditions.

Results

Data embedding paired with dimensionality reduction produced prognostic maps with well-defined clusters of patients, suitable for medical decision support. A second experiment simulated the addition of new patients to an existing model (partial data embedding): Uniform Manifold Approximation and Projection (UMAP) methodology with uniform data discretization led to better outcomes than other analyzed pipelines. Further exploration of parameter space for UMAP and t-distributed stochastic neighbor embedding (t-SNE) underlined the importance of tuning a higher number of parameters for UMAP rather than t-SNE. Moreover, two different machine learning experiments identified a group of genes valuable for partitioning patients (gene relevance analysis) and showed the higher precision obtained by preprocessed data in predicting tumor outcomes for cancer stage and survival rate (six classes prediction).

Conclusions

The present investigation proposed new analysis pipelines for disease outcome modeling from bladder cancer-related biomarkers. Complete and partial data embedding experiments suggested that pipelines employing UMAP had a more accurate predictive ability, supporting the recent literature trends on this methodology. However, it was also found that several UMAP parameters influence experimental results, therefore deriving a recommendation for researchers to pay attention to this aspect of the UMAP technique. Machine learning procedures further demonstrated the effectiveness of the proposed preprocessing in predicting patients’ conditions and determined a sub-group of biomarkers significant for forecasting bladder cancer prognosis.

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

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

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.

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

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.

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

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.

KIF11, a plus end-directed kinesin, as a key gene in benzo(a)pyrene-induced non-small cell lung cancer

Evidence indicates that Benzo(a)pyrenediol-epoxide (BPDE) can damage lung cells, resulting in carcinogenesis with complex mechanisms. We aimed to explore the genes and pathway variations in this process. First, the key gene was screened out and identified through data mining, and then, it was in turn validated by bioinformatics analysis and experimental methods. Consequently, 106 up-regulated and 260 down-regulated differentially expressed genes were yielded, which were enriched in various pathways, such as Cell cycle, and p53 signaling pathway. Then, KIF11 was identified as the key gene. Overexpression of KIF11 in lung cancer had a correlation with advanced pathological grade, advanced T stage, and presence of lymph node metastasis, which predicted poor prognosis. In summary, the present study revealed that KIF11 might be a key gene in the tumorigenesis of BPDE-related lung cancer, raising the possibility of KIF11 as a target for BPDE-induced lung cancer prevention and therapy.