Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer

Identification of novel biomarkers and potential molecular targets for uterine cancer using network-based approach

A better understanding of incidences at the cellular level in uterine cancer is necessary for its effective treatment and favourable prognosis. Till date, it lacks appropriate molecular target-based treatment because of unknown molecular mechanisms that proceed to cancer and no drug has shown the required results of treatment with less severe side effects. Uterine Cancer is one of the top five cancer diagnoses and among the ten most common death-causing cancer in the United States of America. There is no FDA-approved drug for it yet. Therefore, it became necessary to identify the molecular targets for molecular targeted therapy of this widely prevalent cancer type. For this study, we used a network-based approach to the list of the deregulated (both up and down-regulated) genes taking adjacent p-Value ≤ 0.05 as significance cut off for the mRNA data of uterine cancer. We constructed the protein-protein interaction (PPI) network and analyzed the degree, closeness, and betweenness centrality-like topological properties of the PPI network. Then we traced the top 30 genes listed from each topological property to find the key regulators involved in the endometrial cancer (ECa) network. We then detected the communities and sub-communities from the PPI network using the Cytoscape network analyzer and Louvain modularity optimization method. A set of 26 (TOP2A, CENPE, RAD51, BUB1, BUB1B, KIF2C, KIF23, KIF11, KIF20A, ASPM, AURKA, AURKB, PLK1, CDC20, CDKN2A, EZH2, CCNA2, CCNB1, CDK1, FGF2, PRKCA, PGR, CAMK2A, HPGDS, and CDCA8) genes were found to be key genes of ECa regulatory network altered in disease state and might be playing the regulatory role in complex ECa network. Our study suggests that among these genes, KIF11 and H PGDS appeared to be novel key genes identified in our research. We also identified these key genes interactions with miRNAs.

Upregulation of ESPL1 is associated with poor prognostic outcomes in endometrial cancer

BACKGROUND

Extra spindle pole bodies-like 1 (ESPL1) is known to play a crucial role in the segregation of sister chromatids during mitosis. Overexpression of ESPL1 is considered to have oncogenic effects in various human cancers. However, the specific biological function of ESPL1 in endometrial cancer (EC) remains unclear.

METHODS

The TCGA and GEO databases were utilized to assess the expression of ESPL1 in EC. Immunohistochemistry was utilized to detect separase expression in EC samples. Kaplan-Meier survival analysis and Cox regression analysis were performed to evaluate the diagnostic and prognostic significance of ESPL1 in EC. Gene Set Enrichment Analysis (GSEA) was employed to explore the potential signaling pathway of ESPL1 in EC. Cell proliferation and colony formation ability were analyzed using CCK-8 and colony formation assay.

RESULTS

Our analysis revealed that ESPL1 is significantly upregulated in EC, and its overexpression is associated with advanced clinical characteristics and unfavourable prognostic outcomes. Suppression of ESPL1 attenuated proliferation of EC cell line.

CONCLUSION

The upregulation of ESPL1 is associated with advanced disease and poor prognosis in EC patients. These findings suggest that ESPL1 has the potential to serve as a diagnostic and prognostic biomarker in EC, highlighting its significance in the management of EC patients.

Clinicopathological role of Cyclin A2 in uterine corpus endometrial carcinoma: Integration of tissue microarrays and ScRNA-Seq

BACKGROUND

The comprehensive expression level and potential molecular role of Cyclin A2 (CCNA2) in uterine corpus endometrial carcinoma (UCEC) remains undiscovered.

METHODS

UCEC and normal endometrium tissues from in-house and public databases were collected for investigating protein and messenger RNA expression of CCNA2. The transcription factors of CCNA2 were identified by the Cistrome database. The prognostic significance of CCNA2 in UCEC was evaluated through univariate and multivariate Cox regression as well as Kaplan-Meier curve analysis. Single-cell RNA-sequencing (scRNA-seq) analysis was performed to explore cell types in UCEC, and the AUCell algorithm was used to investigate the activity of CCNA2 in different cell types.

RESULTS

A total of 32 in-house UCEC and 30 normal endometrial tissues as well as 720 UCEC and 165 control samples from public databases were eligible and collected. Integrated calculation showed that the CCNA2 expression was up-regulated in the UCEC tissues (SMD = 2.43, 95% confidence interval 2.23∼2.64). E2F1 and FOXM1 were identified as transcription factors due to the presence of binding peaks on transcription site of CCNA2. CCNA2 predicted worse prognosis in UCEC. However, CCNA2 was not an independent prognostic factor in UCEC. The scRNA-seq analysis disclosed five cell types: B cells, T cells, monocytes, natural killer cells, and epithelial cells in UCEC. The expression of CCNA2 was mainly located in B cells and T cells. Moreover, CCNA2 was active in T cells and B cells using the AUCell algorithm.

CONCLUSION

CCNA2 was up-regulated and mainly located in T cells and B cells in UCEC. Overexpression of CCNA2 predicted unfavorable prognosis of UCEC.

Novel biomarkers of preterm brain injury from blood transcriptome in sheep model of intrauterine asphyxia

BACKGROUND

Infants born preterm have a higher incidence of neurological deficits. A key step in finding effective treatments is to identify biomarkers that reliably predict outcome.

METHODS

Following umbilical cord occlusion (UCO) in pregnant sheep, whole fetal blood RNA was sequenced pre- and post-UCO, brain injury outcome was determined by battery of neuropathology scoring and the transcriptome signature correlated to the degree of brain injury. Additionally, we developed a novel analytical procedure to deduce cell blood composition over time.

RESULTS

Sixty-one genes were identified with significant altered expression after UCO. In pre-UCO blood, the level of three mRNAs (Trex2, Znf280b, novel miRNA) and in post-UCO, four mRNAs (Fam184a, Angptl2, novel lincRNA and an unknown protein-coding gene) were associated to brain injury (FDR < 0.01). Several of these mRNAs are related to inflammation and angiogenesis. Pathway analysis highlighted genes playing a role in perinatal death and growth failure. Results also indicate that several leukocyte populations undergo significant changes after UCO.

CONCLUSION

We have used a whole transcriptomic approach to uncover novel biomarkers in fetal blood that correlate to neuropathology in the preterm sheep brain. The current data forms a basis for future studies to investigate mechanisms of these mRNAs in the injury progression.

IMPACT

Trend analysis of genes following asphyxia reveal a group of genes associated with perinatal death and growth failure. Several pre-asphyxia transcripts were associated to brain injury severity suggesting genomic susceptibility to injury. Several post-asphyxia transcripts were correlated to brain injury severity, thus, serve as potential novel biomarkers of injury outcome. Successfully adaptation of cell profiling algorithms suggests significant changes in blood cell composition following asphyxia.

A risk model based on 10 ferroptosis regulators and markers established by LASSO-regularized linear Cox regression has a good prognostic value for ovarian cancer patients

Ovarian cancer is the deadliest gynecologic cancer due to its high rate of recurrence and limited early diagnosis. For certain patients, particularly those with recurring disorders, standard treatment alone is insufficient in the majority of cases. Ferroptosis, an iron- and ROS (reactive oxygen species)-reliant cell death, plays a vital role in the occurrence of ovarian cancer. Herein, subjects from TCGA-OV were calculated for immune scores using the ESTIMATE algorithm and assigned into high- (N = 185) or low-immune (N = 193) score groups; 259 ferroptosis regulators and markers were analyzed for expression, and 64 were significantly differentially expressed between two groups. These 64 differentially expressed genes were applied for LASSO-regularized linear Cox regression for establishing ferroptosis regulators and a markers-based risk model, and a 10-gene signature was established. The ROC curve indicated that the risk score-based curve showed satisfactory predictive efficiency. Univariate and multivariate Cox risk regression analyses showed that age and risk score were risk factors for ovarian cancer patients' overall survival; patients in the high-risk score group obtained lower immune scores. The Nomogram analysis indicated that the model has a good prognostic performance. GO functional enrichment annotation confirmed again the involvement of these 10 genes in ferroptosis and immune activities. TIMER online analysis showed that risk factors and immune cells were significantly correlated. In conclusion, the risk model based on 10 ferroptosis regulators and markers has a good prognostic value for ovarian cancer patients.

PRADclass: Hybrid Gleason Grade-Informed Computational Strategy Identifies Consensus Biomarker Features Predictive of Aggressive Prostate Adenocarcinoma

BACKGROUND

Prostate adenocarcinoma (PRAD) is a common cancer diagnosis among men globally, yet large gaps in our knowledge persist with respect to the molecular bases of its progression and aggression. It is mostly indolent and slow-growing, but aggressive prostate cancers need to be recognized early for optimising treatment, with a view to reducing mortality.

METHODS

Based on TCGA transcriptomic data pertaining to PRAD and the associated clinical metadata, we determined the sample Gleason grade, and used it to execute: (i) Gleason-grade wise linear modeling, followed by five contrasts against controls and ten contrasts between grades; and (ii) Gleason-grade wise network modeling via weighted gene correlation network analysis (WGCNA). Candidate biomarkers were obtained from the above analysis and the consensus found. The consensus biomarkers were used as the feature space to train ML models for classifying a sample as benign, indolent or aggressive.

RESULTS

The statistical modeling yielded 77 Gleason grade-salient genes while the WGCNA algorithm yielded 1003 trait-specific key genes in grade-wise significant modules. Consensus analysis of the two approaches identified two genes in Grade-1 (SLC43A1 and PHGR1), 26 genes in Grade-4 (including LOC100128675, PPP1R3C, NECAB1, UBXN10, SERPINA5, CLU, RASL12, DGKG, FHL1, NCAM1, and CEND1), and seven genes in Grade-5 (CBX2, DPYS, FAM72B, SHCBP1, TMEM132A, TPX2, UBE2C). A RandomForest model trained and optimized on these 35 biomarkers for the ternary classification problem yielded a balanced accuracy ∼ 86% on external validation.

CONCLUSIONS

The consensus of multiple parallel computational strategies has unmasked candidate Gleason grade-specific biomarkers. PRADclass, a validated AI model featurizing these biomarkers achieved good performance, and could be trialed to predict the differentiation of prostate cancers. PRADclass is available for academic use at: https://apalania.shinyapps.io/pradclass (online) and https://github.com/apalania/pradclass (command-line interface).

Machine learning and experimental screening of chromatin regulator signatures and potential drugs in hepatitis B related hepatocellular carcinoma

Many evidences have confirmed that chromatin regulator factors (CRs) are involved in the progression of cancer, but its potential mechanism of affecting hepatitis B related hepatocellular carcinoma still needs to be studied. Our study detected the CRs that affect hepatitis B related hepatocellular carcinoma (HBV-HCC) through machine learning analysis, conducted the analysis of immune cells, constructed the relevant risk model and immune function infiltration, and predicted the potential therapeutic drugs. We found that these CRs were significantly related to the immune cells of Macrophages, B cells, CD8+T cells, etc., and PBK, AURKA, TOP2A and AURKB were the potential risk CRs of HBV-HCC. The expression levels of these four CRs increased in HepG2.2.15 cells and the liver of HBV-HCC patients, consistent with the predicted risk model. Subsequently, ten potential drugs closely related to the risk CRs were finally obtained, experimental research on resveratrol has shown that it can inhibit the proliferation of HepG2.2.15 cells and potentially inhibit the occurrence and development of HBV-HCC. Our study provides novel insights into the function of CRs in HBV-HCC and certain ideas for more accurate targeted therapy.Communicated by Ramaswamy H. Sarma.

Inhibition of DHODH Enhances Replication-Associated Genomic Instability and Promotes Sensitivity in Endometrial Cancer

Endometrial carcinoma (EC) is the most common gynecological malignancy in the United States. De novo pyrimidine synthesis pathways generate nucleotides that are required for DNA synthesis. Approximately 38% of human endometrial tumors present with an overexpression of human dihydroorotate dehydrogenase (DHODH). However, the role of DHODH in cancer cell DNA replication and its impact on modulating a treatment response is currently unknown. Here, we report that endometrial tumors with overexpression of DHODH are associated with a high mutation count and chromosomal instability. Furthermore, tumors with an overexpression of DHODH show significant co-occurrence with mutations in DNA replication polymerases, which result in a histologically high-grade endometrial tumor. An in vitro experiment demonstrated that the inhibition of DHODH in endometrial cancer cell lines significantly induced replication-associated DNA damage and hindered replication fork progression. Furthermore, endometrial cancer cells were sensitive to the DHODH inhibitor either alone or in combination with the Poly (ADP-ribose) polymerase 1 inhibitor. Our findings may have important clinical implications for utilizing DHODH as a potential target to enhance cytotoxicity in high-grade endometrial tumors.

A promising anoikis-related prognostic signature predicts prognosis of skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is a highly aggressive disease with a poor prognosis for advanced tumors. Anoikis is a caspase-dependent cell death process triggered by extracellular matrix (ECM) detachment, rectifies detachment-induced metabolic defects that compromise cell survival, recent study revealed the crucial role of anoikis for cancer cells to survive during metastasis. However, limited research focused on the role of anoikis in SKCM.

METHODS

Our study utilized the 27 anoikis-related genes (ARGs) to divide SKCM patients into two clusters, and obtain differentially expressed genes (DEGs) for each cluster. These DEGs were used in stepwise Cox regression analysis to develop a prediction model for SKCM patients consisting of nine ARGs, called the anoikis-related signature (ARS). Subsequently, we used the risk scores calculated from the ARS to divide SKCM patients into two groups and explored differences in immune microenvironment, immune checkpoint reactivity, and drug sensitivity between the groups.

RESULTS

Nine ARGs were identified to stratify SKCM patients into two risk groups, patients in the high-risk group had a poor prognosis and suppressed immune cell infiltration. Moreover, higher expression of immune checkpoint molecules and a greater sensitivity to immunotherapy and chemotherapy drugs were observed in the low-risk group. Finally, all of the ARS hub genes were found to be upregulated in SKCM tissues and cell lines.

CONCLUSION

A novel ARGs signature was identified for predicting the prognosis of SKCM. Based on the immune landscape associated with ARS discovered in our study, targeting ARS hub genes may be a promising treatment for SKCM.

Heat shock protein family A member 8 serving as a co-activator of transcriptional factor ETV4 up-regulates PHLDA2 to promote the growth of liver cancer

Heat shock protein family A member 8 (HSPA8) participates in the folding or degradation of misfolded proteins under stress and plays critical roles in cancer. In this study, we investigated the function of HSPA8 in the development of liver cancer. By analyzing the TCGA transcriptome dataset, we found that HSPA8 was upregulated in 134 clinical liver cancer tissue samples, and positively correlated with poor prognosis. IHC staining showed the nuclear and cytoplasmic localization of HSPA8 in liver cancer cells. Knockdown of HSPA8 resulted in a decrease in the proliferation of HepG2 and Huh-7 cells. ChIP-seq and RNA-seq analysis revealed that HSPA8 bound to the promoter of pleckstrin homology-like domain family A member 2 (PHLDA2) and regulated its expression. The transcription factor ETV4 in HepG2 cells activated PHLDA2 transcription. HSPA8 and ETV4 could interact with each other in the cells and colocalize in the nucleus. From a functional perspective, we demonstrated that HSPA8 upregulated PHDLA2 through the coactivating transcription factor ETV4 to enhance the growth of liver cancer in vitro and in vivo. From a therapeutic perspective, we identified both HSPA8 and PHDLA2 as novel targets in the treatment of HCC. In conclusion, this study demonstrates that HSPA8 serves as a coactivator of ETV4 and upregulates PHLDA2, leading to the growth of HCC, and is a potential therapeutic target in HCC treatment.

An Interaction Network Driven Approach for Identifying Cervical, Endometrial, Vulvar Carcinomic Biomarkers and Their Multi-targeted Inhibitory Agents from Few Widely Available Medicinal Plants

Differently expressed genes (DEGs) across cervical (CC), endometrial (EC), and vulvar carcinoma (VC) may serve as potential biomarkers for these progressive tumor conditions. In this study, DEGs of cervical (CC), endometrial (EC), and vulvar carcinoma (VC) were identified by microarray analysis. The interaction network between the identified 124 DEGs was constructed and analyzed to identify the hub genes and genes with high stress centrality. DEGs, namely, CDK1 and MMP9, were found to show highest degree and highest stress centrality respectively from the gene interaction network of 124 nodes and 1171 edges. DEG CDK1 is found to be overlapping in both cervical and endometrial carcinomic conditions while DEG MMP9 is found in vulvar carcinomic condition. Further, as it is studied that many phytochemicals play an important role as medicinal drugs, we have identified phytochemicals from few widely available medicinal plants and performed comprehensive computational study to identify a multi-targeted phytochemical against the identified DEGs, which are crucially responsible for the progression of these carcinomic conditions. Virtual screening of the phytochemicals against the target DEG protein structures with PDB IDs 4Y72 and 1GKC resulted in identifying the multi-targeted phytochemical against both the proteins. The molecular docking and dynamics simulation studies reveal that luteolin can act as a multi-targeted agent. Thus, the interactional and structural insights of luteolin toward the DEG proteins signify that it can be further explored as a multi-targeted agent against the cervical, endometrial, and vulvar carcinoma.

The predictive significance of a 5-m6A RNA methylation regulator signature in colorectal cancer

Colorectal cancer attacks the colon or rectum, with increasing morbidity and mortality globally. The RNA modification 6-methyladenine (m6A) is related to RNA modifications, playing a critical role in colorectal cancer. We aimed to identify prognostic signatures for colorectal cancer using risk prediction algorithms, and to validate these signatures using independent datasets and clinical samples. In this study, 175 cases in GSE17536 were assigned into two clusters using consistent clustering and PCA analysis. A multivariate Cox risk regression model revealed that among 21 m6A RNA methylation regulators, RBM15B, FTO, IGF2BP2, ZCCHC4, and KIAA1429 were remarkably associated with colorectal cancer patients' overall survival (OS); however, Kaplan-Meier (KM) survival assessment showed no significant association between these five regulators and colorectal cancer patients' prognosis. A 5-m6A RNA methylation regulator signature was established using LASSO algorithm. Risk scores of cases in GSE17536, GSE17537 and GSE75500 were calculated, and lower risk scores were associated with better DSS/OS. receiver operating characteristic (ROC) curve and the nomogram revealed the satisfactory predictive efficiency of the risk score model. The risk score could distinguish cases in Cluster1 and Cluster2 and normal and tumor tissues based on GSE37182. The prognostic variables for colorectal cancer patients were assessed using both univariate and multivariate Cox's proportional hazard regression models, which revealed that the stage and risk score were significant risk factors. In this study, a comprehensive set of integrative bioinformatics analyses was conducted to investigate the prognostic and diagnostic potential of a panel of 5 m6A RNA methylated regulators in colorectal cancer patients. The conducted studies included the use of several statistical methods, such as the LASSO regression model, KM survival evaluation, ROC curve, and univariate and multivariate Cox's proportional hazard regression analyses. The findings from these analyses collectively established the prognostic marker, highlighting its significance in predicting patient outcomes and diagnosing colorectal cancer.

Ion Channels and Personalized Medicine in Gynecological Cancers

Targeted therapy against cancer plays a key role in delivering safer and more efficient treatments. In the last decades, ion channels have been studied for their participation in oncogenic processes because their aberrant expression and/or function have been associated with different types of malignancies, including ovarian, cervical, and endometrial cancer. The altered expression or function of several ion channels have been associated with tumor aggressiveness, increased proliferation, migration, invasion, and metastasis of cancer cells and with poor prognosis in gynecological cancer patients. Most ion channels are integral membrane proteins easily accessible by drugs. Interestingly, a plethora of ion channel blockers have demonstrated anticancer activity. Consequently, some ion channels have been proposed as oncogenes, cancer, and prognostic biomarkers, as well as therapeutic targets in gynecological cancers. Here, we review the association of ion channels with the properties of cancer cells in these tumors, which makes them very promising candidates to be exploited in personalized medicine. The detailed analysis of the expression pattern and function of ion channels could help to improve the clinical outcomes in gynecological cancer patients.

On the modulation of TRPM channels: Current perspectives and anticancer therapeutic implications

The transient melastatin receptor potential (TRPM) ion channel subfamily functions as cellular sensors and transducers of critical biological signal pathways by regulating ion homeostasis. Some members of TRPM have been cloned from cancerous tissues, and their abnormal expressions in various solid malignancies have been correlated with cancer cell growth, survival, or death. Recent evidence also highlights the mechanisms underlying the role of TRPMs in tumor epithelial-mesenchymal transition (EMT), autophagy, and cancer metabolic reprogramming. These implications support TRPM channels as potential molecular targets and their modulation as an innovative therapeutic approach against cancer. Here, we discuss the general characteristics of the different TRPMs, focusing on current knowledge about the connection between TRPM channels and critical features of cancer. We also cover TRPM modulators used as pharmaceutical tools in biological trials and an indication of the only clinical trial with a TRPM modulator about cancer. To conclude, the authors describe the prospects for TRPM channels in oncology.

The Prognostic Value of a lncRNA Risk Model Consists of 9 m6A Regulator-Related lncRNAs in Hepatocellular Carcinoma (HCC)

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver. Although the RNA modification N6-methyladenine (m6A) has been reported to be involved in HCC carcinogenesis, early diagnostic markers and promising personalized therapeutic targets are still lacking. In this study, we identified that 19 m6A regulators and 34 co-expressed lncRNAs were significantly upregulated in HCC samples; based on these factors, we established a prognostic signal of HCC associated with 9 lncRNAs and 19 m6A regulators using LASSO Cox regression analysis. Kaplan-Meier survival estimate revealed correlations between the risk scores and patients' OS in the training and validation dataset. The ROC curve demonstrated that the risk score-based curve has satisfactory prediction efficiency for both training and validation datasets. Multivariate Cox's proportional hazard regression analysis indicated that the risk score was an independent risk factor within the training and validation dataset. In addition, the risk score could distinguish HCC patients from normal non-cancerous samples and HCC samples of different pathological grades. Eventually, 232 mRNAs were co-expressed with these 9 lncRNAs according to GSE101685 and GSE112790; these mRNAs were enriched in cell cycle and cell metabolic activities, drug metabolism, liver disease-related pathways, and some important cancer related pathways such as p53, MAPK, Wnt, RAS and so forth. The expression of the 9 lncRNAs was significantly higher in HCC samples than that in the neighboring non-cancerous samples. Altogether, by using the Consensus Clustering, PCA, ESTIMATE algorithm, LASSO regression model, Kaplan-Meier survival assessment, ROC curve analysis, and multivariate Cox's proportional hazard regression model analysis, we established a prognostic marker consisting of 9 m6A regulator-related lncRNAs that markers may have prognostic and diagnostic potential for HCC.

ImmuneScore of eight-gene signature predicts prognosis and survival in patients with endometrial cancer

Background

Endometrial cancer (EC) is a common gynecological cancer worldwide and the sixth most common female malignant tumor. A large number of studies conducted through database mining have identified many biomarkers that may be related to survival and prognosis. However, the predictive ability of single-gene biomarkers is not sufficiently accurate. In recent years, tumors have been shown to interact closely with their microenvironment, and tumor-infiltrating immune cells in the tumor microenvironment were associated with therapeutic effects. Furthermore, sequencing technology has evolved and allowed the identification of genetic signatures that may improve prediction results. The purpose of this research was to discover the Cancer Genome Atlas (TCGA) data to evaluate new genetic features that can predict the prognosis of EC.

Methods

mRNA expression profiling was analyzed in patients with EC identified in the TCGA database (n = 530). Differentially expressed genes at different stages of EC were screened using the immune cell enrichment score (ImmuneScore). Univariate and multivariate Cox regression analyses was applied to evaluate genes significantly related to overall survival and establish the prognostic risk parameter formula. Kaplan-Meier survival curves and the logarithmic rank method were applied to verify the importance of risk parameters for the prognostic forecast. The accuracy of survival prediction was confirmed using the nomogram and Receiver Operating Characteristic (ROC) curve analysis. The mRNA expression of eight genes were measured by qRT-PCR. According to COX and HR values, NBAT1, a representative gene among 8 genes, was selected for CCK-8 assay, colony formation assay and transwell invasion assay to verify the effect on survival.

Results

Eight related genes (NBAT1, GFRA4, PTPRT, DLX4, RANBP3L, UNQ6494, KLRB1, and PRAC1) were discovered to be significantly associated with the overall survival rate. According to these eight-gene signatures, 530 patients with EC were assigned to high- and low-risk subgroups. The prognostic capability of the eight-gene signature was not influenced by other elements.

Conclusions

Eight related gene markers were identified using ImmuneScore, which could predict prognosis and survival in patients with EC. These findings provide a basis for understanding the application of biological information in tumors and identifying the poor prognosis of EC.

Significance of Identifying Key Genes Involved in HBV-Related Hepatocellular Carcinoma for Primary Care Surveillance of Patients with Cirrhosis

Cirrhosis is frequently the final stage of disease preceding the development of hepatocellular carcinoma (HCC) and is one of the risk factors for HCC. Preventive surveillance for early HCC in patients with cirrhosis is advantageous for achieving early HCC prevention and diagnosis, thereby enhancing patient prognosis and reducing mortality. However, there is no highly sensitive diagnostic marker for the clinical surveillance of HCC in patients with cirrhosis, which significantly restricts its use in primary care for HCC. To increase the accuracy of illness diagnosis, the study of the effective and sensitive genetic biomarkers involved in HCC incidence is crucial. In this study, a set of 120 significantly differentially expressed genes (DEGs) was identified in the GSE121248 dataset. A protein-protein interaction (PPI) network was constructed among the DEGs, and Cytoscape was used to extract hub genes from the network. In TCGA database, the expression levels, correlation analysis, and predictive performance of hub genes were validated. In total, 15 hub genes showed increased expression, and their positive correlation ranged from 0.80 to 0.90, suggesting they may be involved in the same signaling pathway governing HBV-related HCC. The GSE10143, GSE25097, GSE54236, and GSE17548 datasets were used to investigate the expression pattern of these hub genes in the progression from cirrhosis to HCC. Using Cox regression analysis, a prediction model was then developed. The ROC curves, DCA, and calibration analysis demonstrated the superior disease prediction accuracy of this model. In addition, using proteomic analysis, we investigated whether these key hub genes interact with the HBV-encoded oncogene X protein (HBx), the oncogenic protein in HCC. We constructed stable HBx-expressing LO2-HBx and Huh-7-HBx cell lines. Co-immunoprecipitation coupled with mass spectrometry (Co-IP/MS) results demonstrated that CDK1, RRM2, ANLN, and HMMR interacted specifically with HBx in both cell models. Importantly, we investigated 15 potential key genes (CCNB1, CDK1, BUB1B, ECT2, RACGAP1, ANLN, PBK, TOP2A, ASPM, RRM2, NEK2, PRC1, SPP1, HMMR, and DTL) participating in the transformation process of HBV infection to HCC, of which 4 hub genes (CDK1, RRM2, ANLN, and HMMR) probably serve as potential oncogenic HBx downstream target molecules. All these findings of our study provided valuable research direction for the diagnostic gene detection of HBV-related HCC in primary care surveillance for HCC in patients with cirrhosis.

Prognostic values of the immune microenvironment-related non-coding RNA IGF2BP2-AS1 in bladder cancer

Bladder cancer can range from noninvasive to invasive tumors. When non-muscle invasive bladder cancer (NMIBC) recurs, patients could endure long-term invasive malignancies with a high disease-specific death rate. Immune escape frequently results in tumor development, metastases, unfavorable prognosis, and failure of immunotherapy. Based on the median immune score, this study used ESTIMATE scores to evaluate 411 bladder cancer cases from TCGA-BLCA. Two hundred two ncRNAs were differentially expressed in two groups, where 29 candidates appeared to be associated with the overall survival of bladder cancer patients. LASSO algorithm was performed to establish the risk score model of 13-ncRNA. Risk scores were computed for cases in the training set, validation set, and TCGA-BLCA set; Poor prognosis in cases with higher risk scores was based on the training set, validating set, and TCGA-BLCA set. Among the 13 ncRNAs, IGF2BP2-AS1, MAGF-AS1, ARHGAP5-AS1, and LINC00942 were significantly correlated with the overall survival of bladder cancer patients. Pearson's correlation analysis based on TCGA-BLCA identified 2093, 3107, 386, and 936 mRNAs co-expressed with IGF2BP2-AS1, MAGF-AS1, ARHGAP5-AS1, and LINC00942, respectively. Conclusively, the 13 ncRNA signature exhibited a feasible predictive prognostic value for bladder cancer patients. IGF2BP2-AS1 expression was higher in bladder cancer tissues and significantly correlated to immune-related factors, suggesting that IGF2BP2-AS1 represents a promising immune-related target for treating bladder cancer patients.

Identification of hub genes and construction of prognostic nomogram for patients with Wilms tumors

Background

In children, Wilms' tumors are the most common urological cancer with unsatisfactory prognosis, but few molecular prognostic markers have been discovered for it. With the rapid development of high-throughput quantitative proteomic and transcriptomic approaches, the molecular mechanisms of various cancers have been comprehensively explored. This study aimed to uncover the molecular mechanisms underlying Wilms tumor and build predictive models by use of microarray and RNA-seq data.

Methods

Gene expression datasets were downloaded from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. Bioinformatics methods wereutilized to identified hub genes, and these hub genes were validated by experiment. Nomogram predicting OS was developed using genetic risk score model and clinicopathological variables.

Results

CDC20, BUB1 and CCNB2 were highly expressed in tumor tissues and able to affect cell proliferation and the cell cycle of SK-NEP-1 cells. This may reveal molecular biology features and a new therapeutic target of Wilms tumour.7 genes were selected as prognostic genes after univariate, Lasso, and multivariate Cox regression analyses and had good accuracy, a prognostic nomogram combined gene model with clinical factors was completed with high accuracy.

Conclusions

The current study discovered CDC20,BUB1 and CCNB2 as hub-genes associated with Wilms tumor, providing references to understand the pathogenesis and be considered a novel candidate to target therapy and construct novel nomogram, incorporating both clinical risk factors and gene model, could be appropriately applied in preoperative individualized prediction of malignancy in patients with Wilms tumor.

Intraoperative Flow Cytometry for the Characterization of Gynecological Malignancies

Simple Summary

Aneuploidy and high proliferative potential are distinct features of neoplastic cells. Based on the established role of intraoperative flow cytometry in various types of cancer, the aim of the present study was to investigate its role in cancer cell identification during surgery for gynecological malignancies. The analysis time was 5–6 min per sample. A large percentage of tumors were characterized as aneuploid, while all tumor samples had a significantly high proliferation. Flow cytometry was performed in accordance with pathological evaluation, and the method had high sensitivity and specificity. Our results verify the value of intraoperative flow cytometry in gynecological malignancies, and warrant further investigation in multicenter studies.

Abstract

Cell-cycle analysis has shown the presence of aneuploidy to be associated with poor prognosis. We developed an innovative rapid cell-cycle analysis protocol (the Ioannina protocol) that permitted the intraoperative identification of neoplastic cells in a plethora of malignancies. Herein, we aimed to investigate the potential role of cell-cycle analysis in the intraoperative characterization of gynecological malignancies. Women who underwent surgery for gynecological malignancies in our institution over a three-year period were included in this study. Permanent section pathology evaluation was used as the gold standard for malignancy evaluation. Total accordance was observed between flow cytometry and pathology evaluation. In total, 21 aneuploid cancers were detected following DNA index calculation. Of these, 20 were hyperploid and 1 was hypoploid. In addition, tumor samples were characterized by a significantly lower percentage of cells in G0/G1, as well as an induced tumor index. The response time for flow cytometry to obtain results was 5–6 min per sample. It seems that flow cytometry analyses for intraoperative tumor evaluation can be safely expanded to gynecological malignancies. This is a novel practical approach that has been proven valuable in several tumor types to date, and also seems to be reliable for gynecological malignancies. Intraoperative flow cytometry is expected to be crucial in decisions of lymph node dissection in endometrial cancers, due to its rapid response regarding the tumor invasion of part or all of the myometrial thickness. In this way, the surgeon can quickly modify the plane of dissection. Our results warrant the further investigation of applying iFC in larger, multicenter studies.

TRPM4 and TRPV2 are two novel prognostic biomarkers and promising targeted therapy in UVM

Uveal melanoma (UVM) is the most common primary intraocular malignancy tumor in adults. Almost 50% of UVM patients develop metastatic disease, and is usually fatal within 1 year. However, the mechanism of etiology remains unclear. The lack of prognostic, diagnostic and therapeutic biomarkers is a main limitation for clinical diagnosis and treatment. The transient receptor potential (TRP) channels play important roles in the occurrence and development of tumors, which may have the potential as a therapeutic target for UVM. This current study aimed to identify the potential effect and function of the TRPs that could provide survival prediction and new insight into therapy for UVM. Based on the transcriptome data and potential key genes of UVM were screened using the Cancer Genome Atlas (TCGA) databases, Gene expression analysis showed the expression of TRPM4, TRPV2 and other TRPs was high levels in UVM. Using survival analysis, we screened out that the high expression of TRPM4 and TRPV2 was negatively correlated with the prognosis of UVM patients. Cox regression analysis and functional enrichment analysis further indicated that TRPM4 and TRPV2 were the most convincing therapeutic targets of UVM, and the majority of genes involved in ferroptosis pathways in UVM showed positively correlated with the expression levels of TRPM4 and TRPV2. In conclusion, TRPM4 and TRPV2 were considered as two novel prognostic biomarkers and a promising targeted therapy in UVM.

Knockdown of PHLDA2 promotes apoptosis and autophagy of glioma cells through the AKT/mTOR pathway

Pleckstrin homology like domain family A member 2 (PHLDA2) is an imprinted gene expressed in placenta and has been shown to be associated with tumor progression. However, the effect of PHLDA2 on glioma cell growth has not been reported yet. Data based on TCGA database showed that PHLDA2 was up-regulated in glioma tissues. Moreover, PHLDA2 was also elevated in glioma cells. Functional assays showed that siRNA-mediated knockdown of PHLDA2 reduced cell viability of glioma cells and suppressed the cell proliferation. Cell apoptosis of glioma cells was promoted by silencing of PHLDA2 with increased Bax and decreased Bcl-2. Silencing of PHLDA2 reduced protein expression of p62, enhanced LC3 and Beclin1 to promote autophagy. Phosphorylated AKT and mTOR were down-regulated in glioma cells by interference of PHLDA2. In conclusion, downregulation of PHLDA2 inhibited glioma cell proliferation, and promoted cell apoptosis and autophagy through inactivation of AKT/mTOR signaling.

Developing a novel DNA methylation risk score for survival and identification of prognostic gene mutations in endometrial cancer: a study based on TCGA data

Background

Few studies have focused on DNA methylation in endometrial cancer. The aim of our study is identify its role in endometrial cancer prognosis.

Methods

A publicly available dataset was retrieved from The Cancer Genome Atlas. For validation of expression alteration due to methylation, RNA sequencing data were obtained from other independent cohorts. MethSurv was used to search for candidate CpG probes, which were then filtered by least absolute shrinkage and selection operator Cox regression and multivariate Cox regression analyses to identify final set of CpG probes for overall survival. A methylation-based risk model was developed and receiver operating characteristic analysis with area under curve was used for evaluation. Patients were divided into high- and low-risk groups using an optimal cut-off point. Comprehensive bioinformatic analyses were conducted to identify hub genes, key transcription factors, and enriched cancer-related pathways. Kaplan–Meier curve was used for survival analysis.

Results

A 5-CpG signature score was established. Its predictive value for 5-year overall survival was high, with area under curve of 0.828, 0.835 and 0.816 for the training, testing and entire cohorts. cg27487839 and cg12885678 had strong correlation with their gene expression, XKR6 and PTPRN2, and lower PTPRN2 expression was associated with poorer survival in both The Cancer Genome Atlas and the validation datasets. Low-risk group was associated with significantly better survival. Low-risk group harboured more mutations in hub genes and key transcription factors, and mutations in SP1 and MECP2 represented favourable outcome.

Conclusion

We developed a methylation-based prognostic stratification system for endometrial cancer. Low-risk group was associated with better survival and harboured more mutations in the key regulatory genes.

Comprehensive Analysis of the Glycolysis-Related Gene Prognostic Signature and Immune Infiltration in Endometrial Cancer

Glucose metabolic reprogramming and immune imbalance play important roles in the progression of cancers. The purpose of this study is to develop a glycolysis-related prognostic signature for endometrial cancer (EC) and analyze its relationship with immune function. The mRNA expression profiling of the glycolysis-related genes and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA). We identified a glycolysis-related gene prognostic signature for predicting the prognosis of EC by using The Least Absolute Shrinkage and Selection Operator (LASSO) regression and found the patients in the high-risk group had worse survival prognosis. Multivariate Cox regression analysis showed that the gene signature was an independent prognostic factor for EC. The ROC curve confirmed the accuracy of the prognostic signature (AUC = 0.730). Then, we constructed a nomogram to predict the 1–5 years survival rate of EC patients. The association between the gene signature and immune function was analyzed based on the “ESTIMATE” and “CIBERSORT” algorithm, which showed the immune and ESTIMATE scores of patients in the high-risk group were lower, while the low immune and ESTIMATE scores were associated with a worse prognosis of patients. The imbalance of immune cells was also found in the high-risk group. Further, the protein of CDK1, a gene in the signature, was found to be closely related to prognosis of EC and inhibition of CDK1 could inhibit migration and promote apoptosis of EC cells. This study reveals a link between glycolysis-related gene signature and immunity, and provides personalized therapeutic targets for EC.

BIRC5 promotes cancer progression and predicts prognosis in laryngeal squamous cell carcinoma

BACKGROUND

Laryngeal squamous cell carcinoma (LSCC) remains one of the most common respiratory tumors worldwide. Baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5) is a member of the inhibitor-of-apoptosis protein family. BIRC5 plays an important role in various types of cell proliferation, differentiation, migration and invasion. However, the specific role of BIRC5 in LSCC remains unclear.

METHODS

To provide a prognostic biomarker for LSCC, we screened the prognostic genes of LSCC via bioinformatics. PPI network and KEGG pathways were used to select hub genes. Clinical prognoses were performed using a Kaplan-Meier plotter and Cox proportional-hazard analysis. BIRC5 expression in LSCC tissues and cell lines were detected by RT-PCR, Western blot and Immunohistochemistry (IHC). Cell proliferation, cell cycle and cell apoptosis were detected with Cell Counting Kit-8 (CCK-8) and Flow Cytometry assay, respectively.

RESULTS

Here, BIRC5 was strongly correlated with higher tumor grade and differentiation. BIRC5 was highly expressed in LSCC tissues when compared with normal tissues and increased expression of BIRC5 was associated with overall survival in LSCC patients. The suppression of BIRC5 induced cell apoptosis and cell cycle arrest, thereby inhibiting the proliferation of LSCC cells. The survival analysis confirmed that higher level of BIRC5 expression predicted poor prognosis of LSCC patients. BIRC5 may act as an oncogene of LSCC development and was suggested as a promising prognostic biomarker for LSCC.

Identification of a Novel Epithelial-Mesenchymal Transition-Related Gene Signature for Endometrial Carcinoma Prognosis

(1) Background: Endometrial cancer is the most prevalent cause of gynecological malignant tumor worldwide. The prognosis of endometrial carcinoma patients with distant metastasis is poor. (2) Method: The RNA-Seq expression profile and corresponding clinical data were downloaded from the Cancer Genome Atlas database and the Gene Expression Omnibus databases. To predict patients’ overall survival, a 9 EMT-related genes prognosis risk model was built by machine learning algorithm and multivariate Cox regression. Expressions of nine genes were verified by RT-qPCR. Responses to immune checkpoint blockades therapy and drug sensitivity were separately evaluated in different group of patients with the risk model. (3) Endometrial carcinoma patients were assigned to the high- and low-risk groups according to the signature, and poorer overall survival and disease-free survival were showed in the high-risk group. This EMT-related gene signature was also significantly correlated with tumor purity and immune cell infiltration. In addition, eight chemical compounds, which may benefit the high-risk group, were screened out. (4) Conclusions: We identified a novel EMT-related gene signature for predicting the prognosis of EC patients. Our findings provide potential therapeutic targets and compounds for personalized treatment. This may facilitate decision making during endometrial carcinoma treatment.

CKMT1A is a novel potential prognostic biomarker in patients with endometrial cancer

PURPOSE

The International Federation of Gynecology and Obstetrics (FIGO) stage remains the standard staging system for the assessment of endometrial cancer (EC) prognosis. Thus, we aim to identify the significant genes or biomarkers associated with the stage of endometrial cancer, which may also help reveal the mechanism of EC progression and assess the prognosis of patients with EC.

MATERIALS AND METHODS

We compared the mRNA expression levels of EC patients with stages I and II as well as stages III and IV in the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) of EC patients at different stages were selected by volcano plot and Venn analysis. Gene Ontology (GO) and Pathways were applied to analyze the identified genes. Protein protein interaction (PPI) network was employed to identify the correlation. The survival analyses based on TCGA database were conducted for further screening. The Human Protein Atlas, quantitative PCR and immunohistochemistry were utilized to confirm the differences in expression of DEGs in endometrial cancer samples at different FIGO stages.

RESULTS

CKMT1A was identified as a candidate gene. Through survival analyses, we found that CKMT1A may be a poor prognostic factor in the overall survival of endometrial cancer patients. GO and Pathways revealed that CKMT1A is closely associated with the metabolic process. More importantly, Human Protein Atlas and quantitative PCR confirmed the differences in expression of CKMT1A in endometrial cancer samples at different FIGO stages.

CONCLUSION

In summary, this study shows that CKMT1A is a newly identified essential tumor progression regulator of endometrial cancer, which may give rise to novel therapeutic strategies in the management of endometrial cancer patients to prolong its prognosis and prevent tumor progression.

A Systematic Review and Integrated Bioinformatic Analysis of Candidate Genes and Pathways in the Endometrium of Patients With Polycystic Ovary Syndrome During the Implantation Window

Polycystic ovary syndrome (PCOS) is a common disorder with wide-ranging clinical heterogeneity that causes infertility. However, the comprehensive molecular mechanisms of PCOS in causing infertility is remaining unclear. Hence, a comprehensive literature search was conducted using PubMed, Scopus, EBSCOhost, and Science Direct. Medical Subject Heading (MeSH) terms like PCOS, gene expression, implantation window and endometrium were used as the keywords. From 138 studies retrieved, original articles with RNA profiling on human endometrial tissues in PCOS women during the implantation window were included. Study design, sample size, sample type, method, and differentially expressed genes (DEGs) were identified from all publications. The DEGs were analyzed using the software packages DAVID, STRING, and Cytoscape. Three studies that met inclusion criteria were included, and 368 DEGs were identified. Twelve significant clusters from the protein-protein interaction network (PPI) complex were found, and cluster 1 showed very high intermolecular interactions. Five candidate genes (AURKA, CDC25C, KIF23, KIF2C, and NDC80) were identified from the systematic review and integrated bioinformatics analysis. It is concluded that cell cycle is the fundamental biological processes that were dysregulated in the endometrium of PCOS women, affecting decidualization progression in the endometrium during the implantation window.

Pharmacological Modulation and (Patho)Physiological Roles of TRPM4 Channel-Part 2: TRPM4 in Health and Disease

Transient receptor potential melastatin 4 (TRPM4) is a unique member of the TRPM protein family and, similarly to TRPM5, is Ca²⁺ sensitive and permeable for monovalent but not divalent cations. It is widely expressed in many organs and is involved in several functions; it regulates membrane potential and Ca²⁺ homeostasis in both excitable and non-excitable cells. This part of the review discusses the currently available knowledge about the physiological and pathophysiological roles of TRPM4 in various tissues. These include the physiological functions of TRPM4 in the cells of the Langerhans islets of the pancreas, in various immune functions, in the regulation of vascular tone, in respiratory and other neuronal activities, in chemosensation, and in renal and cardiac physiology. TRPM4 contributes to pathological conditions such as overactive bladder, endothelial dysfunction, various types of malignant diseases and central nervous system conditions including stroke and injuries as well as in cardiac conditions such as arrhythmias, hypertrophy, and ischemia-reperfusion injuries. TRPM4 claims more and more attention and is likely to be the topic of research in the future.

Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis

OBJECTIVE

The current research was aimed to identify candidate genes associated with development and progression of epithelial ovarian carcinoma using bioinformatics analysis.

MATERIALS AND METHODS

We screened and validated candidate genes associated with carcinogenesis and development of epithelial ovarian carcinoma via bioinformatic analysis in three microarray datasets (GSE14407, GSE29450, and GSE54388) downloaded from the Gene Expression Omnibus (GEO) database.

RESULTS

Our bioinformatic analysis identified 514 differentially expressed genes (DEGs) and nine candidate hub genes (CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, and TTK). Survival analysis using the Kaplan-Meier plotter showed that high expression levels of seven candidate genes (CCNB1, RRM2, BUB1, CCNA2, AURKA, CDK1, and BUB1B) were associated with poor overall survival (OS). Gene Expression Profiling Interactive Analysis (GEPIA) revealed a higher expression level of these seven candidate genes in ovarian carcinoma samples than in normal ovarian samples. Immunostaining results from the Human Protein Atlas (HPA) database suggested that the protein expression levels of CCNB1, CCNA2, AURKA, and CDK1 were increased in ovarian cancer tissues. No difference was observed in RRM2 protein expression level between normal ovarian and ovarian cancer samples. Oncomine analysis revealed an association between the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 and patient clinicopathological information. Finally, six genes, namely CCNB1, CCNA2, AURKA, BUB1, BUB1B, and CDK1, were identified as hub genes and a transcription factor (TF)-gene regulatory network was constructed to identify TFs, including POLR2A, ZBTB11, KLF9, and ELF1, that were implicated in regulating these hub genes.

CONCLUSION

Six significant hub DEGs associated with a poor prognosis in epithelial ovarian cancer were identified. These could be potential biomarkers for ovarian cancer patients.

Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis

Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382–0.912], p = 0.017; 0.605 [0.371–0.986], p = 0.044; 1.678 [1.132–2.488], p = 0.01; 2.428 [1.372–4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes’ coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.

Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Uterine corpus endometrial carcinoma locally infiltrates numerous immune cells and other tumor immune microenvironment components. These cells are involved in malignant tumor growth and proliferation and the process of resistance toward immunotherapies. Here, we aimed to develop a tumor immune microenvironment-related prognostic signature for high-risk grade III endometrial carcinoma based on The Cancer Genome Atlas. The signature was systematically correlated with immune infiltration characteristics of the tumor microenvironment. The seven-gene Riskscore signature was robust and performed well in training, testing, and Gene Expression Omnibus-independent cohorts. A nomogram comprising the gene signature accurately predicted patient prognosis, with our model performing better than other endometrial cancer-related signatures. Analysis of the IMvigor210 immunotherapy cohort revealed that subgroups with a low Riskscore had a better prognosis than subgroups with a high Riskscore. Subgroups with a low Riskscore exhibited immune cell infiltration and inflammatory profiles, whereas subgroups with a high Riskscore experienced progressive disease. The receiver operating characteristic curve indicated that risk score, neoantigen, and tumor mutation burden models together accurately predicted treatment response. Taken together, we developed a tumor microenvironment-based seven-gene prognostic stratification system to predict the prognosis of patients with high-risk endometrial cancer and guide more effective immunotherapy strategies.

Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer.

Methods

Two groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes.

Results

We identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate.

Conclusion

The findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.

Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods

Background

Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.

Methods

Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.

Results

A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.

Conclusion

Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.

The Promotional Effect of Hollow MnO2 with Brucea Javanica Oil Emulsion (BJOE) on Endometrial Cancer Apoptosis

Endometrial cancer (EC) is a common gynecological malignancy worldwide whose therapy mainly depends on chemotherapy. In past years, an increasing number of studies indicate that hollow MnO2 could serve as a nanoplatform in the drug delivery system. The Brucea javanica oil emulsion (BJOE) has been illustrated to play a vital role in cancers. However, knowledge about the combined effect of H-MnO2-PEG/BJOE in endometrial cancer remains ambiguous up to now. In the present work, we prepared a drug-delivery vector H-MnO2-PEG by chemical synthesis and found that H-MnO2-PEG significantly inhibited cell proliferation in endometrial cancer cells. Moreover, the combination of H-MnO2-PEG/BJOE could repress cell proliferation more efficiently and promote cell apoptosis. Mechanistically, we found that BJOE exerted its role as a promoter of endometrial apoptosis by regulating relative protein expressions. In general, the present study demonstrates that H-MnO2-PEG functions as a critical vector in the tumor microenvironment of endometrial cancer and the significant effect of H-MnO2-PEG/BJOE on cancer cells, suggesting a new paradigm for the treatment of endometrial cancer.

SOCS2 is a potential prognostic marker that suppresses the viability of hepatocellular carcinoma cells

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-associated mortality worldwide. Thus, there is an urgent requirement to identify novel diagnostic and prognostic biomarkers for this disease. The present study aimed to identify the hub genes associated with the progression and prognosis of patients with HCC. A total of three expression profiles of HCC tissues were extracted from the Gene Expression Omnibus (GEO) database, followed by the identification of differentially expressed genes (DEGs) using the GEO2R method. The identified DEGs were assessed for survival significance using Kaplan-Meier analysis. Among the 15 identified DEGs in HCC tissues [cytochrome P450 family 39 subfamily A member 1, cysteine rich angiogenic inducer 61, Fos proto-oncogene, forkhead transcription factor 1 (FOXO1), growth arrest and DNA damage inducible β, Inhibitor of DNA binding 1, interleukin-1 receptor accessory protein, metallothionein-1M, pleckstrin homology-like domain family A member 1, Rho family GTPase 3, serine dehydratase, suppressor of cytokine signaling 2 (SOCS2), tyrosine aminotransferase (TAT), S100 calcium-binding protein P and serine protease inhibitor Kazal-type 1 (SPINK1)]. Low expression levels of FOXO1, SOCS2 and TAT and high SPINK1 expression indicated poor survival outcomes for patients with HCC. In addition, SOCS2 was associated with distinct stages of HCC progression in patients and presented optimal diagnostic value. In vitro functional experiments indicated that overexpression of SOCS2 inhibited HCC cell proliferation and migration. Taken together, the results of the present study suggest that SOCS2 may act as a valuable prognostic marker that is closely associated with HCC progression.

Identification of prognostic significance of BIRC5 in breast cancer using integrative bioinformatics analysis

Aims: Baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5) plays vital roles in carcinogenesis by influencing cell division and proliferation and by inhibiting apoptosis. However, the prognostic significance of BIRC5 remains unclear in breast cancer.

Methods:BIRC5 expression and methylation status were evaluated using the Oncomine and The Cancer Genome Atlas (TCGA) databases. The relevance between BIRC5 and different clinicopathological features as well as survival information was analyzed using the bc-GenExMiner database and Kaplan–Meier Plotter. BIRC5–drug interaction network was obtained using the Comparative Toxicogenomics Database.

Results: Based on the results from databases and own hospital data, BIRC5 was higher expressed in different breast cancer subtypes compared with the matched normal individuals. Hormone receptors were negatively correlated with BIRC5 expression, whereas the Scarff–Bloom–Richardson (SBR) grade, Nottingham Prognostic Index (NPI), human epidermal growth factor receptor-2 (HER-2) status, basal-like status, and triple-negative status were positively related to BIRC5 level in breast cancer samples with respect to normal tissues. High BIRC5 expression was responsible for shorter relapse-free survival, worse overall survival, reduced distant metastasis free survival, and increased risk of metastatic relapse event. BIRC5–drug interaction network indicated that several common drugs could modulate BIRC5 expression. Furthermore, a positive correlation between BIRC5 andcell-division cycle protein 20 (CDC20) gene was confirmed.

Conclusion:BIRC5 may be adopted as a promising predictive marker and potential therapeutic target in breast cancer. Further large-scale studies are needed to more precisely confirm the value of BIRC5 in treatment of breast cancer.

CDK1 serves as a novel therapeutic target for endometrioid endometrial cancer

Background: Endometrial cancer (EC) is one of the most common and prevalent gynecologic malignancies worldwide. The aim of this study was to identify a novel therapeutic target for endometrioid endometrial cancer.

Materials and Methods: Bioinformatic analysis was performed and CDK1 was screen out as one of the hub genes in the pathogenesis of EC. Immunohistochemistry was used to verify the expression of CDK1 in endometrial cancer tissue. Cell viability and colony formation were used to study the effects of CDK1 on the proliferation and colony formation of endometrial cancer cells in vitro. Apoptosis and cell cycle assays were used to elucidate the mechanism of CDK1 affecting cell proliferation. Tumor xenograft transplantation assay was performed to show the effects of CDK1 on the growth of endometrial cancer cells in vivo.

Results: CDK1 was over expressed in endometrioid endometrial cancer, and accumulation of cytoplasmic CDK1 was associated with histological grade of EC. CDK1 promoted endometrial cancer cell growth and colony formation in vitro. The inhibition of CDK1 activity induced cell apoptosis and caused G2/M phase arrest of cell cycle in endometrial cancer cells. The inhibition of CDK1 activity also inhibited endometrial cancer growth in xenograft models.

Conclusion: CDK1 was involved in the pathogenesis of endometrioid endometrial cancer and provided a novel therapeutic target for endometrioid endometrial cancer.

A Ten-N6-Methyladenosine (m6A)-Modified Gene Signature Based on a Risk Score System Predicts Patient Prognosis in Rectum Adenocarcinoma

OBJECTIVES

The study aims to analyze the expression of N6-methyladenosine (m6A)-modified genes in rectum adenocarcinoma (READ) and identify reliable prognostic biomarkers to predict the prognosis of READ.

MATERIALS AND METHODS

RNA sequence data of READ and corresponding clinical survival data were obtained from The Cancer Genome Atlas (TCGA) database. N6-methyladenosine (m6A)-modified genes in READ were downloaded from the "m6Avar" database. Differentially expressed m6A-modified genes in READ stratified by different clinicopathological characteristics were identified using the "limma" package in R. Protein-protein interaction (PPI) network and co-expression analysis of differentially expressed genes (DEGs) were performed using "STRING" and Cytoscape, respectively. Principal component analysis (PCA) was done using R. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to functionally annotate the differentially expressed genes in different subgroups. Univariate Cox regression analyses were conducted to identify the powerful independent prognostic factors in READ associated with overall survival (OS). A robust likelihood-based survival model was built using the "rbsurv" package to screen for survival-associated signature genes. The Support Vector Machine (SVM) was used to predict the prognosis of READ through the risk score of survival-associated signature genes. Correlation analysis were carried out using GraphPad prism 8.

RESULTS

We screened 974 differentially expressed m6A-modified genes among four types of READ samples. Two READ subgroups (group 1 and group 2) were identified by K means clustering according to the expression of DEGs. The two subgroups were significantly different in overall survival and pathological stages. Next, 118 differentially expressed genes between the two subgroups were screened and the expression of 112 genes was found to be related to the prognosis of READ. Next, a panel of 10 survival-associated signature genes including adamtsl1, csmd2, fam13c, fam184a, klhl4, olfml2b, pdzd4, sec14l5, setbp1, tmem132b was constructed. The signature performed very well for prognosis prediction, time-dependent receiver-operating characteristic (ROC) analysis displaying an area under the curve (AUC) of 0.863, 0.8721, and 0.8752 for 3-year survival rate, prognostic status, and pathological stage prediction, respectively. Correlation analysis showed that the expression levels of the 10 m6A-modified genes were positively correlated with that of m6A demethylase FTO and ALKBH5.

CONCLUSION

This study identified potential m6A-modified genes that may be involved in the pathophysiology of READ and constructed a novel gene expression panel for READ risk stratification and prognosis prediction.

TRPM4 in Cancer-A New Potential Drug Target

Transient receptor potential melastatin 4 (TRPM4) is widely expressed in various organs and associated with cardiovascular and immune diseases. Lately, the interest in studies on TRPM4 in cancer has increased. Thus far, TRPM4 has been investigated in diffuse large B-cell lymphoma, prostate, colorectal, liver, breast, urinary bladder, cervical, and endometrial cancer. In several types of cancer TRPM4 is overexpressed and contributes to cancer hallmark functions such as increased proliferation and migration and cell cycle shift. Hence, TRPM4 is a potential prognostic cancer marker and a promising anticancer drug target candidate. Currently, the underlying mechanism by which TRPM4 contributes to cancer hallmark functions is under investigation. TRPM4 is a Ca²⁺-activated monovalent cation channel, and its ion conductivity can decrease intracellular Ca²⁺ signaling. Furthermore, TRPM4 can interact with different partner proteins. However, the lack of potent and specific TRPM4 inhibitors has delayed the investigations of TRPM4. In this review, we summarize the potential mechanisms of action and discuss new small molecule TRPM4 inhibitors, as well as the TRPM4 antibody, M4P. Additionally, we provide an overview of TRPM4 in human cancer and discuss TRPM4 as a diagnostic marker and anticancer drug target.

Integrated analysis of immune-related genes in endometrial carcinoma

Background

Exploring novel and sensitive targets is urgent due to the high morbidity of endometrial cancer (EC). The purpose of our study was to explore the transcription factors and immune-related genes in EC and further identify immune-based lncRNA signature as biomarker for predicting survival prognosis.

Methods

Transcription factors, aberrantly expressed immune-related genes and immune-related lncRNAs were explored through bioinformatics analysis. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were conducted to identify the immune and overall survival (OS) related lncRNAs. The accuracy of model was evaluated by Kaplan-Meier method and receiver operating characteristic (ROC) analysis, and the independent prognostic indicator was identified with Cox analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) were conducted to detect the accuracy of our results.

Results

A network of 29 transcription factors and 17 immune-related genes was constructed. Furthermore, four immune-prognosis-related lncRNAs were screened out. Kaplan-Meier survival analysis and time-dependent ROC analysis revealed a satisfactory predictive potential of the 4-lncRNA model. Consistency was achieved among the results from the training set, testing set and entire cohort. The distributed patterns between the high- and low-risk groups could be distinguished in principal component analysis. Comparisons of the risk score and clinical factors confirmed the four-lncRNA-based signature as an independent prognostic indicator. Last, the reliability of the results was verified by qRT-PCR in 29 cases of endometrial carcinoma and in cells.

Conclusions

Overall, our study constructed a network of transcription factors and immune-related genes and explored a four immune-related lncRNA signature that could serve as a novel potential biomarker of EC.

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Endometrial cancer (EC) is the commonest gynaecological malignancy. Current prognostic markers are inadequate to accurately predict patient survival, necessitating novel prognostic markers, to improve treatment strategies. Telomerase has a unique role within the endometrium, whilst aberrant telomerase activity is a hallmark of many cancers. The aim of the current in silico study is to investigate the role of telomere and telomerase associated genes and proteins (TTAGPs) in EC to identify potential prognostic markers and therapeutic targets. Analysis of RNA-seq data from The Cancer Genome Atlas identified differentially expressed genes (DEGs) in EC (568 TTAGPs out of 3467) and ascertained DEGs associated with histological subtypes, higher grade endometrioid tumours and late stage EC. Functional analysis demonstrated that DEGs were predominantly involved in cell cycle regulation, while the survival analysis identified 69 DEGs associated with prognosis. The protein-protein interaction network constructed facilitated the identification of hub genes, enriched transcription factor binding sites and drugs that may target the network. Thus, our in silico methods distinguished many critical genes associated with telomere maintenance that were previously unknown to contribute to EC carcinogenesis and prognosis, including NOP56, WFS1, ANAPC4 and TUBB4A. Probing the prognostic and therapeutic utility of these novel TTAGP markers will form an exciting basis for future research.

Development and Validation of a Five-immune Gene Pair Signature in Endometrial Carcinoma

BACKGROUND

Endometrial cancer (EC) is a common gynecological malignancy worldwide. Immunity is closely related to the occurrence and prognosis of EC. At the same time, immune-related genes have great potential as prognostic markers in many types of cancer.

OBJECTIVE

Therefore, we attempt to develop immune-related gene markers to enhance prognosis prediction of EC.

METHODS

542 samples of EC gene expression data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA). The samples were randomly divided into two groups, one group as a training set (N=271), and one set as a validation set. (N=271). In the training set, the gene pairs were established based on the relative expression levels of 271 immune genes, and the prognosis-related gene pairs were screened. The lasso was used to select the features, and finally, the robust biomarkers were screened. Finally, the prognostic model of the immune gene pair was established and verified by the validation data set.

RESULTS

10030 immune gene pair (IRGPs) were obtained, and univariate survival analysis was used to identify 1809 prognostic-related IRGPs (p<0.05). 5-IRGPs were obtained by lasso regression feature selection, and multivariate regression was used to establish 5-IRGPs signature, 5-IRGPs signature is an independent prognostic factor for EC patients, and could be risk stratified in patients with TCGA datasets, age, ethnicity, stage, and histological classification (p<0.05). The mean AUC of survival in both the training set and the validation set was greater than 0.7, indicating that 5-IRGPs signature has superior classification performance in patients with EC. In addition, 5-IRGPs have the highest average C index (0.795) compared to the prognostic characteristics of the three endometrial cancers reported in the past and Stage and Age.

CONCLUSION

This study constructed a 5-IRGPs signature as a novel prognostic marker for predicting survival in patients with EC.

Identification of potential crucial genes associated with the pathogenesis and prognosis of pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a type of malignant tumor with the highest mortality rate among all neoplasms worldwide, and its exact pathogenesis is still poorly understood. Timely diagnosis and treatment are of great importance in order to decrease the mortality rate of PAAD. Therefore, identifying new biomarkers for diagnosis and prognosis is essential to enable early detection of PAAD and to improve the overall survival (OS) rate. In order to screen and integrate differentially expressed genes (DEGs) between PAAD and normal tissues, a total of seven datasets were downloaded from the Gene Expression Omnibus database and the ‘limma’ and ‘robustrankggreg’ packages in R software were used. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the DEGs was performed using the Database for Annotation, Visualization and Integrated Discovery website, and the protein-protein interaction network analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. A gene prognostic signature was constructed using the Cox regression model. A total of 10 genes (CDK1, CCNB1, CDC20, ASPM, UBE2C, TPX2, TOP2A, NUSAP1, KIF20A and DLGAP5) that may be associated with pancreatic adenocarcinoma were identified. According to the differentially expressed genes in The Cancer Genome Atlas, the present study set up four prognostic signatures (matrix metalloproteinase 12, sodium voltage-gated channel α subunit 11, tetraspanin 1 and SH3 domain and tetratricopeptide repeats-containing 2), which effectively predicted OS. The hub genes that were highly associated with the occurrence, development and prognosis of PAAD were identified, which may be helpful to further understand the molecular basis of pancreatic cancer and guide the synthesis of drugs for PPAD.

Decreased expression of TRPM4 is associated with unfavorable prognosis and aggressive progression of endometrial carcinoma

Transient Receptor Potential Melastatin 4 (TRPM4) is a nonselective channel conducting monovalent ions and indirectly regulates intracellular Ca²⁺. Aberrant expression has been reported in a number of cancers. However, the biological function of TRPM4 in endometrial carcinoma (EC) is still unknown. We find that decreased TRPM4 expression is significantly correlated with a poor prognosis, overall survival (OS, P<0.001) and recurrence-free survival (P=0.002) through The Cancer Genome Atlas (TCGA) datasets in mRNA level. Multivariate Cox regression analysis suggests that TRPM4 is an independent prognostic factor for OS in EC patients. In vitro assays show that TRPM4-deletion results in significant promotion of proliferation and migration in EC cells. We then conducted a gene set enrichment analysis (GSEA) and according to the results, the expression of TRPM4 is modulated by estrogen, which is inhibited by ER antagonist. Furthermore, the silencing of TRPM4 causes a decreased p53 and hyper-activation of EMT, PI3K/AKT/mTOR signaling pathway in EC, as demonstrated in vitro. Overall, these results indicate that TRPM4 is clinically useful in predicting EC prognosis and represent a potential candidate as a new therapeutic target.

m6A RNA methylation regulators have prognostic value in papillary thyroid carcinoma

BACKGROUND

N⁶-Methyladenosine (m⁶A) is a ubiquitous RNA modification with vital roles in various cancers, but little is known about its role in papillary thyroid carcinoma (PTC), a common endocrine malignancy.

METHODS

In this study, an m⁶A RNA methylation regulator-based biomarker signature was developed for the effective prediction of prognosis in patients with PTC. The gene expression profiles of m⁶A RNA methylation regulators and the corresponding clinical information was downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed m⁶A RNA methylation regulators between tumor and normal control samples, and correlation expression levels, clinical parameters, and outcomes were evaluated. And a prognostic signature was built using a PTC cohort from TCGA.

RESULTS

The expression level of HNRNPC was remarkably upregulated in tumor samples, while WTAP, RBM15, YTHDC2, YTHDC1, FTO, METTL14, METTL3, ALKBH5, KIAA1429, YTHDF1, and ZC3H13 were significantly downregulated in the cancer specimens compared with those in control samples. A three-gene prognostic signature comprising RBM15, KIAA1429, and FTO could predict overall survival in patients with PTC. In addition, the prognostic signature-based risk score was identified as an independent prognostic indicator for PTC.

CONCLUSIONS

We established a robust m⁶A RNA methylation regulator-based molecular signature for predicting prognosis in patients with PTC with high accuracy; this signature might provide important guidance for therapeutic strategies.

Identification of a five-gene signature for predicting the progression and prognosis of stage I endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is often diagnosed at an early clinical stage based on abnormal vaginal bleeding. However, the prognosis of UCEC is poor. The present study was conducted to identify novel tumor grade-related genes with the potential to predict the prognosis and progression of UCEC. A total of three gene expression microarray datasets were downloaded from the Gene Expression Omnibus database, and one RNA-sequencing dataset with corresponding clinical information of patients with UCEC was obtained from The Cancer Genome Atlas database. In summary, 1,447 differentially expressed genes (DEGs) were identified between endometrial cancerous tissues and normal endometrial tissues. Weighted gene co-expression network analysis was performed to assess the associations between DEGs and clinical traits. In total, five genes were found to be highly associated with the tumorigenesis and prognosis of UCEC. Among them, BUB1 mitotic checkpoint serine/threonine kinase B, cyclin B1, cell-division cycle protein 20 and non-SMC condensing I complex subunit G were involved in cell cycle regulation pathways, and DLG-associated protein 5 was involved in the Notch receptor 3 signaling pathway based on functional enrichment analyses. Of the five genes, four were highly expressed in endometrial cancerous tissues compared with normal endometrial tissues at the protein level. In addition, the higher expression of these genes predicted a higher tumor grade and worse overall survival. In conclusion, the present study revealed a 5-gene signature that can be used to predict the progression of UCEC.

Identification of Potential Key Genes for Pathogenesis and Prognosis in Prostate Cancer by Integrated Analysis of Gene Expression Profiles and the Cancer Genome Atlas

Background: Prostate cancer (PCa)is a malignancy of the urinary system with a high incidence, which is the second most common male cancer in the world. There are still huge challenges in the treatment of prostate cancer. It is urgent to screen out potential key biomarkers for the pathogenesis and prognosis of PCa.

Methods: Multiple gene differential expression profile datasets of PCa tissues and normal prostate tissues were integrated analysis by R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the overlapping Differentially Expressed Genes (DEG) were performed. The STRING online database was used in conjunction with Cytospace software for protein-protein interaction (PPI) network analysis to define hub genes. The relative mRNA expression of hub genes was detected in Gene Expression Profiling Interactive Analysis (GEPIA) database. A prognostic gene signature was identified by Univariate and multivariate Cox regression analysis.

Results: Three hundred twelve up-regulated genes and 85 down-regulated genes were identified from three gene expression profiles (GSE69223, GSE3325, GSE55945) and The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset. Seven hub genes (FGF2, FLNA, FLNC, VCL, CAV1, ACTC1, and MYLK) further were detected, which related to the pathogenesis of PCa. Seven prognostic genes (BCO1, BAIAP2L2, C7, AP000844.2, ASB9, MKI67P1, and TMEM272) were screened to construct a prognostic gene signature, which shows good predictive power for survival by the ROC curve analysis.

Conclusions: We identified a robust set of new potential key genes in PCa, which would provide reliable biomarkers for early diagnosis and prognosis and would promote molecular targeting therapy for PCa.