Identification and Validation of Hub Genes Associated with Bladder Cancer by Integrated Bioinformatics and Experimental Assays

EPYC functions as a novel prognostic biomarker for pancreatic cancer

Pancreatic cancer (PC) has become a worldwide challenge attributed to its difficult early diagnosis and rapid progression. Treatments continue to be limited besides surgical resection. Hence, we aimed to discover novel biological signatures as clinically effective therapeutic targets for PC via the mining of public tumor databases. We found that epiphycan (EPYC) could function as an independent risk factor to predict the poor prognosis in PC based on integrated bioinformatics analysis. We downloaded associated PC data profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) online websites, then applied the software Rstudio to filter out genes under the strict criteria. After the batch survival analysis using Log-rank test and univariate cox regression, we obtained 39 candidate genes. Subsequently, we narrowed the scope to 8 genes by establishing a Lasso regression model. Eventually, we focused on 2 genes (EPYC and MET) by further building a multivariate cox regression model. Given that the role of EPYC in PC remains obscure, we then performed a series of molecular functional experiments, including RT-qPCR, CCK8, EdU, colony formation, Transwell, western blot, cell live-dead staining, subcutaneous tumor formation, to enhance our insight into its underlying molecular mechanisms. The above results demonstrated that EPYC was highly expressed in PC cell lines and could promote the proliferation of PCs via PI3K-AKT signaling pathway in vivo and in vitro. We arrived at a conclusion that EPYC was expected to be a biological neo-biomarker for PC followed by being a potential therapeutic target.

Disulfidptosis-related signatures for prognostic and immunotherapy reactivity evaluation in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and a nonnegligible health concern on a worldwide scale. Disulfidptosis is a novel mode of cell death, which is mainly caused by the collapse of the actin skeleton. Although many studies have demonstrated that various types of cell death are associated with cancer treatment, the relationship between disulfidptosis and HCC has not been elucidated.

METHODS

Here, we mainly applied bioinformatics methods to construct a disulfidptosis related risk model in HCC patients. Specifically, transcriptome data and clinical information were downloaded from the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) database. A total of 45 co-expressed genes were extracted between the disulfidptosis-related genes (DRGs) and the differential expression genes (DEGs) of liver hepatocellular carcinoma (LIHC) in the TCGA database. The LIHC cohort was divided into two subgroups with different prognosis by k-mean consensus clustering and functional enrichment analysis was performed. Subsequently, three hub genes (CDCA8, SPP2 and RDH16) were screened by Cox regression and LASSO regression analysis. In addition, a risk signature was constructed and the HCC cohort was divided into high risk score and low risk score subgroups to compare the prognosis, clinical features and immune landscape between the two subgroups. Finally, the prognostic model of independent risk factors was constructed and verified.

CONCLUSIONS

High DRGs-related risk score in HCC individuals predict poor prognosis and are associated with poor immunotherapy response, which indicates that risk score assessment model can be utilized to guide clinical treatment strategy.

The neurological and non-neurological roles of the primary microcephaly-associated protein ASPM

Primary microcephaly (MCPH), is a neurological disorder characterized by small brain size that results in numerous developmental problems, including intellectual disability, motor and speech delays, and seizures. Hitherto, over 30 MCPH causing genes (MCPHs) have been identified. Among these MCPHs, MCPH5, which encodes abnormal spindle-like microcephaly-associated protein (ASPM), is the most frequently mutated gene. ASPM regulates mitotic events, cell proliferation, replication stress response, DNA repair, and tumorigenesis. Moreover, using a data mining approach, we have confirmed that high levels of expression of ASPM correlate with poor prognosis in several types of tumors. Here, we summarize the neurological and non-neurological functions of ASPM and provide insight into its implications for the diagnosis and treatment of MCPH and cancer.

Immune-related biomarkers predict the prognosis and immune response of breast cancer based on bioinformatic analysis and machine learning

Breast cancer (BC) is the malignancy with the highest mortality rate among women, identification of immune-related biomarkers facilitates precise diagnosis and improvement of the survival rate in early-stage BC patients. 38 hub genes significantly positively correlated with tumor grade were identified based on weighted gene coexpression network analysis (WGCNA) by integrating the clinical traits and transcriptome analysis. Six candidate genes were screened from 38 hub genes basing on least absolute shrinkage and selection operator (LASSO)-Cox and random forest. Four upregulated genes (CDC20, CDCA5, TTK and UBE2C) were identified as biomarkers with the log-rank p < 0.05, in which high expression levels of them showed a poor overall survival (OS) and recurrence-free survival (RFS). A risk model was finally constructed using LASSO-Cox regression coefficients and it possessed superior capability to identify high risk patients and predict OS (p < 0.0001, AUC at 1-, 3- and 5-years are 0.81, 0.73 and 0.79, respectively). Decision curve analysis demonstrated risk score was the best prognostic predictor, and low risk represented a longer survival time and lower tumor grade. Importantly, multiple immune cell types and immunotherapy targets were observed increase in expression levels in high-risk group, most of which were significantly correlated with four genes. In summary, the immune-related biomarkers could accurately predict the prognosis and character the immune responses in BC patients. In addition, the risk model is conducive to the tiered diagnosis and treatment of BC patients.

Evaluation of the Small Heat Shock Protein Family Members HSPB2 and HSPB3 in Bladder Cancer Prognosis and Progression

Bladder cancer (BlCa) represents the sixth most commonly diagnosed type of male malignancy. Due to the clinical heterogeneity of BlCa, novel markers would optimize treatment efficacy and improve prognosis. The small heat shock proteins (sHSP) family is one of the major groups of molecular chaperones responsible for the maintenance of proteome functionality and stability. However, the role of sHSPs in BlCa remains largely unknown. The present study aimed to examine the association between HSPB2 and HSPB3 expression and BlCa progression in patients, and to investigate their role in BlCa cells. For this purpose, a series of experiments including reverse transcription-quantitative PCR, Western blotting, MTT assay and flow cytometry were performed. Initial analyses revealed increased vs. human transitional carcinoma cells, expression levels of the HSPB2 and HSPB3 genes and proteins in high grade BlCa cell lines. Therefore, we then evaluated the clinical significance of the HSPB2 and HSPB3 genes expression levels in bladder tumor samples and matched adjusted normal bladder specimens. Total RNA from 100 bladder tumor samples and 49 paired non-cancerous bladder specimens were isolated, and an accurate SYBR-Green based real-time quantitative polymerase chain reaction (qPCR) protocol was developed to quantify HSPB2 and HSPB3 mRNA levels in the two cohorts of specimens. A significant downregulation of the HSPB2 and HSPB3 genes expression was observed in bladder tumors as compared to matched normal urothelium; yet, increased HSPB2 and HSPB3 levels were noted in muscle-invasive (T2-T4) vs. superficial tumors (TaT1), as well as in high-grade vs. low-grade tumors. Survival analyses highlighted the significantly higher risk for post-treatment disease relapse in TaT1 patients poorly expressing HSPB2 and HSPB3 genes; this effect tended to be inverted in advanced disease stages (muscle-invasive tumors) indicating the biphasic impact of HSPB2, HSPB3 genes in BlCa progression. The pro-survival role of HSPB2 and HSPB3 in advanced tumor cells was also evident by our finding that HSPB2, HSPB3 genes expression silencing in high grade BlCa cells enhanced doxorubicin toxicity. These findings indicate that the HSPB2, HSPB3 chaperone genes have a likely pro-survival role in advanced BlCa; thus, they can be targeted as novel molecular markers to optimize treatment efficacy in BlCa and to limit unnecessary interventions.

Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma

Background

Multiple myeloma (MM) remains an essentially incurable disease. This study aimed to establish a predictive model for estimating prognosis in newly diagnosed MM based on gene expression profiles.

Methods

RNA-seq data were downloaded from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study and the Genotype-Tissue Expression (GTEx) databases. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction network analysis were performed to identify hub genes. Enrichment analysis was also conducted. Patients were randomly split into training (70%) and validation (30%) datasets to build a prognostic scoring model based on the least absolute shrinkage and selection operator (LASSO). CIBERSORT was applied to estimate the proportion of 22 immune cells in the microenvironment. Drug sensitivity was analyzed using the OncoPredict algorithm.

Results

A total of 860 newly diagnosed MM samples and 444 normal counterparts were screened as the datasets. WGCNA was applied to analyze the RNA-seq data of 1589 intersecting genes between differentially expressed genes and prognostic genes. The blue module in the PPI networks was analyzed with Cytoscape, and 10 hub genes were identified using the MCODE plug-in. A three-gene (TTK, GINS1, and NCAPG) prognostic model was constructed. This risk model showed remarkable prognostic value. CIBERSORT assessment revealed the risk model to be correlated with activated memory CD4 T cells, M0 macrophages, M1 macrophages, eosinophils, activated dendritic cells, and activated mast cells. Furthermore, based on OncoPredict, high-risk MM patients were sensitive to eight drugs.

Conclusions

We identified and constructed a three-gene-based prognostic model, which may provide new and in-depth insights into the treatment of MM patients.

Rad54L promotes bladder cancer progression by regulating cell cycle and cell senescence

Bladder cancer (BCa) is the most prevalent cancer of the urinary system, but its pathogenesis is still poorly understood. Several reports have suggested that gene damage repair is highly correlated with tumor development and drug resistance, in which homologous recombination repair gene Rad54L seems to play an important role, through yet unclear mechanisms. Therefore, this study stratified cancer patients by Rad54L expression in BCa tissue, and high Rad54L expression was associated with a poor prognosis. Mechanistically, we demonstrate that high Rad54L expression promotes abnormal bladder tumor cell proliferation by changing the cell cycle and cell senescence. In addition, this study also suggests that Rad54L may be associated with p53, p21, and pRB in BCa tissue. In summary, this study exposes Rad54L as potential a prognostic biomarker and precision treatment target in BCa.

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.