RSK4 confers paclitaxel resistance to ovarian cancer cells, which is resensitized by its inhibitor BI-D1870.

Many ovarian cancers initially respond well to chemotherapy, but often become drug-resistant after several years. Therefore, analysis of drug resistance mechanisms and overcoming resistance are urgently needed. Paclitaxel is one of the first-choice and widely-used drugs for ovarian cancer, but like most drugs, drug resistance is observed in subsequent use. RSK4 is known as a tumor-suppressor, however, it has increasingly been reported to lead to drug resistance. Here, we found that RSK4 expression was elevated in paclitaxel-resistant ovarian cancer cells using DNA microarray, quantitative real-time PCR, and western blotting analysis. We examined the contribution of RSK4 to paclitaxel resistance and found that paclitaxel sensitivity was restored by RSK inhibitor co-treatment. We analyzed the mechanism by which resistance is developed when RSK4 level is elevated, and accelerated phosphorylation of the downstream translation factor eIF4B was discovered. In the Kaplan-Meier plot, the overall survival time was longer with RSK4 high, supporting its role as a tumor suppressor, as in previous findings, but the tendency was reversed when focusing on paclitaxel treatment. In addition, RSK4 levels were higher in non-responders than in responders in the ROC plotter. Finally, external expression of RSK4 in ovarian cancer cells increased the cell viability under paclitaxel treatment. These findings suggest that RSK4 may contribute to paclitaxel resistance, and that co-treatment with RSK4 inhibitors is effective treatment of paclitaxel-resistant ovarian cancer in which RSK4 is elevated.

Stress-Inducible SCAND Factors Suppress the Stress Response and Are Biomarkers for Enhanced Prognosis in Cancers.

The cell stress response is an essential system present in every cell for responding and adapting to environmental stimulations. A major program for stress response is the heat shock factor (HSF)-heat shock protein (HSP) system that maintains proteostasis in cells and promotes cancer progression. However, less is known about how the cell stress response is regulated by alternative transcription factors. Here, we show that the SCAN domain (SCAND)-containing transcription factors (SCAN-TFs) are involved in repressing the stress response in cancer. SCAND1 and SCAND2 are SCAND-only proteins that can hetero-oligomerize with SCAN-zinc finger transcription factors, such as MZF1(ZSCAN6), for accessing DNA and transcriptionally co-repressing target genes. We found that heat stress induced the expression of SCAND1, SCAND2, and MZF1 bound to HSP90 gene promoter regions in prostate cancer cells. Moreover, heat stress switched the transcript variants' expression from long noncoding RNA (lncRNA-SCAND2P) to protein-coding mRNA of SCAND2, potentially by regulating alternative splicing. High expression of HSP90AA1 correlated with poorer prognoses in several cancer types, although SCAND1 and MZF1 blocked the heat shock responsiveness of HSP90AA1 in prostate cancer cells. Consistent with this, gene expression of SCAND2, SCAND1, and MZF1 was negatively correlated with HSP90 gene expression in prostate adenocarcinoma. By searching databases of patient-derived tumor samples, we found that MZF1 and SCAND2 RNA were more highly expressed in normal tissues than in tumor tissues in several cancer types. Of note, high RNA expression of SCAND2, SCAND1, and MZF1 correlated with enhanced prognoses of pancreatic cancer and head and neck cancers. Additionally, high expression of SCAND2 RNA was correlated with better prognoses of lung adenocarcinoma and sarcoma. These data suggest that the stress-inducible SCAN-TFs can function as a feedback system, suppressing excessive stress response and inhibiting cancers.

The maraca plot: A novel visualization of hierarchical composite endpoints.

BACKGROUND: Hierarchical composite endpoints are complex endpoints combining outcomes of different types and different clinical importance into an ordinal outcome that prioritizes the clinically most important (e.g. most severe) event of a patient. Hierarchical composite endpoint can be analysed with the win odds, an adaptation of win ratio to include ties. One of the difficulties in interpreting hierarchical composite endpoint is the lack of proper tools for visualizing the treatment effect captured by hierarchical composite endpoint, given the complex nature of the endpoint which combines events of different types. METHODS: Hierarchical composite endpoints usually combine time-to-event outcomes and continuous outcomes into a composite; hence, it is important to capture not only the shift from more severe categories to less severe categories in the active group in comparison to the control group (as in any ordinal endpoint), but also changes occurring within each category. We introduce the novel maraca plot which combines violin plots (with nested box plots) to visualize the density of the distribution of the continuous outcome and Kaplan-Meier plots for time-to-event outcomes into a comprehensive visualization. CONCLUSION: The novel maraca plot is suggested for visualization of hierarchical composite endpoints consisting of several time-to-event outcomes and a continuous outcome. It has a very simple structure and therefore easily communicates both the overall treatment effect and the effect on individual components.

Q-omics: Smart Software for Assisting Oncology and Cancer Research.

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer.

INTRODUCTION: Extensive research is directed to uncover new biomarkers capable to stratify breast cancer patients into clinically relevant cohorts. However, the overall performance ranking of such marker candidates compared to other genes is virtually absent. Here, we present the ranking of all survival related genes in chemotherapy treated basal and estrogen positive/HER2 negative breast cancer. METHODS: We searched the GEO repository to uncover transcriptomic datasets with available follow-up and clinical data. After quality control and normalization, samples entered an integrated database. Molecular subtypes were designated using gene expression data. Relapse-free survival analysis was performed using Cox proportional hazards regression. False discovery rate was computed to combat multiple hypothesis testing. Kaplan-Meier plots were drawn to visualize the best performing genes. RESULTS: The entire database includes 7,830 unique samples from 55 independent datasets. Of those with available relapse-free survival time, 3,382 samples were estrogen receptor-positive and 696 were basal. In chemotherapy treated ER positive/ERBB2 negative patients the significant prognostic biomarker genes achieved hazard rates between 1.76 and 3.33 with a p value below 5.8E-04. The significant prognostic genes in adjuvant chemotherapy treated basal breast cancer samples reached hazard rates between 1.88 and 3.61 with a p value below 7.2E-04. Our integrated platform was extended enabling the validation of future biomarker candidates. CONCLUSIONS: A reference ranking for all genes in two chemotherapy treated breast cancer cohorts is presented. The results help to neglect those with unlikely clinical significance and to focus future research on the most promising candidates.

Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation.

BACKGROUND: Survival analysis is a cornerstone of medical research, enabling the assessment of clinical outcomes for disease progression and treatment efficiency. Despite its central importance, no commonly used spreadsheet software can handle survival analysis and there is no web server available for its computation. OBJECTIVE: Here, we introduce a web-based tool capable of performing univariate and multivariate Cox proportional hazards survival analysis using data generated by genomic, transcriptomic, proteomic, or metabolomic studies. METHODS: We implemented different methods to establish cut-off values for the trichotomization or dichotomization of continuous data. The false discovery rate is computed to correct for multiple hypothesis testing. A multivariate analysis option enables comparing omics data with clinical variables. RESULTS: We established a registration-free web-based survival analysis tool capable of performing univariate and multivariate survival analysis using any custom-generated data. CONCLUSIONS: This tool fills a gap and will be an invaluable contribution to basic medical and clinical research.

Expression and Prognostic Significance of the MMP Family Molecules in Bladder Cancer.

BACKGROUND: Bladder cancer (BC) is the 10th most common cancer worldwide with significantly varied prognosis in different pathological subtypes. MMPs, a group of enzymes, could involve in the invasion and metastasis of numerous malignancies. The function of MMPs in BC is partly reported in several studies but with great conflicts; hence, a systematic analysis of expression levels and prognostic values of these MMP genes are still to be determined. METHODS: Firstly, differentially expressed genes (DEGs) of MMPs were identified in ONCOMINE, GEPIA, and UALCAN databases, and these DEGs were also detected by real-time RT-qPCR. More importantly, we investigated the clinical significance of these DEGs in BC patients via Kaplan- Meier (KM) Plotter, UALCAN, and cBioPortal databases. RESULTS: The study found that the mRNA expression of MMP1/11 in BC samples was significantly higher than that in normal bladder tissues, and MMP2/3 was lower in the former than in the latter. The expression level of MMP1/2/7/9/11/13/23B was significantly related to the tumor stages. Furthermore, the prognostic analysis suggested that the high transcription levels of MMP7 and low transcription levels of MMP23A were correlated with favorable relapse-free survival and overall survival in the patients with BC, respectively. Notably, high MMP11/13 expression levels indicated poor overall survival (OS) and relapse-free survival (RFS) in patients with BC. CONCLUSION: This study revealed that MMP1/2/3/7/9/11/13/23A/23B are possible prognostic biomarkers and clinical therapeutic targets for patients with BC.

Survival analysis: A primer for the clinician scientists.

Survival analysis is a collection of statistical procedures employed on time-to-event data. The outcome variable of interest is time until an event occurs. Conventionally, it dealt with death as the event, but it can handle any event occurring in an individual like disease, relapse from remission, and recovery. Survival data describe the length of time from a time of origin to an endpoint of interest. By time, we mean years, months, weeks, or days from the beginning of being enrolled in the study. The major limitation of time-to-event data is the possibility of an event not occurring in all the subjects during a specific study period. In addition, some of the study subjects may leave the study prematurely. Such situations lead to what is called censored observations as complete information is not available for these subjects. Life table and Kaplan-Meier techniques are employed to obtain the descriptive measures of survival times. The main objectives of survival analysis include analysis of patterns of time-to-event data, evaluating reasons why data may be censored, comparing the survival curves, and assessing the relationship of explanatory variables to survival time. Survival analysis also offers different regression models that accommodate any number of covariates (categorical or continuous) and produces adjusted hazard ratios for individual factor.

CCTs as new biomarkers for the prognosis of head and neck squamous cancer.

The chaperonin-containing T-complex protein 1 (CCT) subunits participate in diverse diseases. However, little is known about their expression and prognostic values in human head and neck squamous cancer (HNSC). This article aims to evaluate the effects of CCT subunits regarding their prognostic values for HNSC. We mined the transcriptional and survival data of CCTs in HNSC patients from online databases. A protein-protein interaction network was constructed and a functional enrichment analysis of target genes was performed. We observed that the mRNA expression levels of CCT1/2/3/4/5/6/7/8 were higher in HNSC tissues than in normal tissues. Survival analysis revealed that the high mRNA transcriptional levels of CCT3/4/5/6/7/8 were associated with a low overall survival. The expression levels of CCT4/7 were correlated with advanced tumor stage. And the overexpression of CCT4 was associated with higher N stage of patients. Validation of CCTs' differential expression and prognostic values was achieved by the Human Protein Atlas and GEO datasets. Mechanistic exploration of CCT subunits by the functional enrichment analysis suggests that these genes may influence the HNSC prognosis by regulating PI3K-Akt and other pathways. This study implies that CCT3/4/6/7/8 are promising biomarkers for the prognosis of HNSC.

Circulating Tumor-Cell-Associated White Blood Cell Clusters in Peripheral Blood Indicate Poor Prognosis in Patients With Hepatocellular Carcinoma.

Aim: Circulating tumor cells (CTC) are a precursor to metastasis in several types of cancer and are occasionally found in the bloodstream in association with immune cells, such as white blood cells (WBCs). CTC-associated WBC (CTC-WBC) clusters can promote CTC appreciation and metastasis, suggesting that patients with CTC-WBC clusters found in the peripheral blood may have a worse prognosis. However, it is unclear whether CTC-WBC clusters are present in the peripheral blood of patients with hepatocellular carcinoma (HCC) and suggest a poor prognosis for HCC. Methods: We collected peripheral blood from 214 patients with HCC from January 2014 to December 2016. CanPatrol™ CTC analysis technology was used to isolate and count CTCs and CTC-WBC clusters in the patients' peripheral blood. Chi-squared analysis was used to calculate the correlation between the CTC-WBC clusters and clinicopathological characteristics. Kaplan-Meier survival analysis and Cox regression analysis were used to assess patient prognosis. Results: We used CanPatrol™ CTC analysis technology to count different types of CTCs and CTC-WBC clusters. The results showed that CTC-WBC clusters and tumor size (P = 0.001), tumor number (P = 0.005), portal vein tumor thrombus (P = 0.026), BCLC stage (P < 0.001), AFP level (P = 0.002), and total number of CTCs (P < 0.001) were statistically related. Cox regression analysis revealed that CTC-WBC clusters are an independent prognostic indicator of DFS (HR = 1.951, 95%CI:1.348-2.824, P < 0.001) and OS (HR = 3.026, 95%CI:1.906-4.802, P < 0.001) in HCC patients. Using Kaplan-Meier analysis, we found that positive CTC-WBC cluster patients had significantly shorter DFS and OS than patients with negative CTC-WBC (P < 0.001 and P < 0.001, respectively). Conclusions: CTC-WBC clusters in the peripheral blood are an independent predictor of DFS and OS, and their presence indicates poor prognosis in patients with HCC.

A non-linear optimisation method to extract summary statistics from Kaplan-Meier survival plots using the published P value.

BACKGROUND: Meta-analyses of studies evaluating survival (time-to-event) outcomes are a powerful technique to assess the strength of evidence for a given disease or treatment. However, these studies rely on the adequate reporting of summary statistics in the source articles to facilitate further analysis. Unfortunately, many studies, especially within the field of prognostic research do not report such statistics, making secondary analyses challenging. Consequently, methods have been developed to infer missing statistics from the commonly published Kaplan-Meier (KM) plots but are liable to error especially when the published number at risk is not included. METHODS: We therefore developed a method using non-linear optimisation (nlopt) that only requires the KM plot and the commonly published P value to better estimate the underlying censoring pattern. We use this information to then calculate the natural logarithm of the hazard ratio (ln (HR)) and its variance (var) ln (HR), statistics important for meta-analyses. RESULTS: We compared this method to the Parmar method which also does not require the number at risk to be published. In a validation set consisting of 13 KM studies, a statistically significant improvement in calculating ln (HR) when using an exact P value was obtained (mean absolute error 0.014 vs 0.077, P = 0.003). Thus, when the true HR has a value of 1.5, inference of the HR using the proposed method would set limits between 1.49/1.52, an improvement of the 1.39/1.62 limits obtained using the Parmar method. We also used Monte Carlo simulations to establish recommendations for the number and positioning of points required for the method. CONCLUSION: The proposed non-linear optimisation method is an improvement on the existing method when only a KM plot and P value are included and as such will enhance the accuracy of meta-analyses performed for studies analysing time-to-event outcomes. The nlopt source code is available, as is a simple-to-use web implementation of the method.

Identification of key differentially expressed mRNAs and microRNAs in non-small cell lung cancer using bioinformatics analysis.

Non-small cell lung cancer (NSCLC) is a leading cause of mortality worldwide. However, the pathogenesis of NSCLC remains to be fully elucidated. Therefore, the present study aimed to explore the differential expression of mRNAs and microRNAs (miRNAs/miRs) in NSCLC and to determine how these RNA molecules interact with one another to affect disease progression. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified from the GSE18842, GSE32863 and GSE29250 datasets downloaded from the Gene Expression Omnibus (GEO database). Functional and pathway enrichment analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. STRING, Cytoscape and MCODE were applied to construct a protein-protein interaction (PPI) network and to screen hub genes. The interactions between miRNAs and mRNAs were predicted using miRWalk 3.0 and a miRNA-mRNA regulatory network was constructed. The prognostic value of the identified hub genes was then evaluated via Kaplan-Meier survival analyses using datasets from The Cancer Genome Atlas. A total of 782 DEGs and 46 DEMs were identified from the 3 GEO datasets. The enriched pathways and functions of the DEGs and target genes of the DEMs included osteoclast differentiation, cell adhesion, response to a drug, plasma membrane, extracellular exosome and protein binding. A subnetwork composed of 11 genes was extracted from the PPI network and the genes in this subnetwork were mainly involved in the cell cycle, cell division and DNA replication. A miRNA-gene regulatory network was constructed with 247 miRNA-gene pairs based on 6 DEMs and 210 DEGs. Kaplan-Meier survival analysis indicated that the expression of ubiquitin E2 ligase C, cell division cycle protein 20, DNA topoisomerase IIα, aurora kinase A and B, cyclin B2, maternal embryonic leucine zipper kinase, slit guidance ligand 3, phosphoglucomutase 5, endomucin, cysteine dioxygenase type 1, dihydropyrimidinase-like 2, miR-130b, miR-1181 and miR-127 was significantly associated with overall survival of patients with lung adenocarcinoma. In the present study, a miRNA-mRNA regulatory network in NSCLC was established, which may provide future avenues for scientific exploration and therapeutic targeting of NSCLC.

Prognostic Significance of Sodium Iodide Symporter and Deiodinase Enzymes mRNA Expression in Gastric Cancer.

CONTEXT: Thyroid hormones (THs) are critically important for development, homeostasis, and metabolic regulation in mammals. Iodine, one of the constituents of TH, is actively supplied by sodium iodide symporter (NIS) into the thyroid gland. TH is subsequently transported to distant organs where its activation and deactivation is catalyzed by isoforms of deiodinases (DIOs). NIS protein has been known to overexpress in cancer cases of the breast and gastrointestinal organs. Recent studies show a possible role of DIOs in various cancers. AIMS: In the present investigation, the prognostic significance of NIS and DIO-1, 2 and 3 was studied in gastric cancer using a data mining bioinformatic approach. METHODS: "The Kaplan-Meier plotter" database was used for direct in silico validation in clinically relevant 876 gastric cancer patients with >15 years of follow-up information. After obtaining KM survival plots, hazard ratio and log-rank P value were calculated. RESULTS: Increased expression of NIS and DIO 1-3 is significantly associated with worsen overall survival of gastric cancer patients followed for 20 years. Prognostic roles of NIS and individual DIOs were assessed in different types of gastric cancer classified based on morphologies, human epidermal growth factor receptor-2 receptor status, treatment choices, and different clinicopathological features. CONCLUSIONS: Based on these analyses, the present study found the indication of prognostic values of these genes. This information will contribute to better understanding of managing complex and heterogeneous gastric cancer. Further, these findings may be beneficial as a companion diagnostic tool predicting more accurate gastric cancer prognosis.

COL4A family: potential prognostic biomarkers and therapeutic targets for gastric cancer.

BACKGROUND: The type IV collagen alpha chain (COL4A) family is a major component of the basement membrane (BM) that has recently been found to be involved in tumor angiogenesis and progression. However, the expression levels and the exact roles of distinct COL4A family members in gastric cancer (GC) have not been completely understood. METHODS: Here, the expression levels of COL4As in GC and normal gastric tissues were calculated by using TCGA datasets and the predicted prognostic values by the GEPIA tool. Furthermore, the cBioPortal and Metascape tools were integrated to analyze the genetic alterations, correlations and potential functions of COL4As, and their frequently altered neighboring genes in GC. RESULTS: Notably, the expression levels of COL4A1/2/4 in GC were higher to those in normal gastric tissues, while the expression levels of COL4A3/5/6 were lower in GC than normal. Survival analysis revealed that lower expression levels of COL4A1/5 led to higher overall survival (OS) rate. Multivariate analysis using the Cox proportional-hazards model indicated that age, gender, pathological grade, metastasis and COL4A5 expression, are independent prognostic factors for OS. However, TNM stage, lymph node metastasis, Lauren's classification, COL4A1-4 and COL4A6 were associated with poor OS but not independent prognostic factors. Function-enriched analysis of COL4As and their frequently altered neighboring genes was involved in tumor proliferation and metastasis in GC. CONCLUSIONS: These results implied that COL4A1/2 were potential therapeutic targets for GC. COL4A3/4/6 might have an impact on gastric carcinogenesis and subsequent progression, whereas COL4A5 was an independent prognostic marker for GC.

Prognostic significance of autophagy related genes in estrogen receptor positive tamoxifen treated breast cancer.

Resistance to Tamoxifen constitutes a major therapeutic challenge in treating hormone sensitive breast cancer. The induction of autophagy has been shown to be involved as one of the mechanism responsible for Tamoxifen resistance. Autophagy related gene (ATG) members are the regulators and effectors of Macroautophagy process in the cellular systems. In this study, we evaluated the prognostic significance of ATGs in Tamoxifen treated breast cancer. The "Kaplan- Meier plotter" database was utilized to analyze the relevance and significance of ATGs mRNA expression to Relapse Free Survival in breast cancer patients. We used the data of patients who are Estrogen receptor positive and are treated with Tamoxifen. Hazard ratio and log-rank p-value were calculated using KM survival plots for various ATGs. Overexpressed ATG3, ATG 5, ATG 8B and PIK3R4 resulted in a poor prognosis. A gene signature of these ATGs predicts deteriorated RFS (p-value=8.3e-05 and HR=1.84 (1.35-2.51) and Distant Metastasis Free Survival (p value = 0.0027 and HR=2.03 (1.27-3.26). We report the distinct prognostic values of ATGs in patients of breast cancer treated with Tamoxifen. Thus, better understandings of the induction of autophagy pathway may potentially form the basis for use of autophagy inhibitors in the Tamoxifen treated breast cancer.

The Prognosis Of Peroxiredoxin Family In Breast Cancer.

PURPOSE: PRDX (Peroxiredoxin) family has involved in breast cancer tumorigenesis from the evidence obtained from cell lines, human tissues and mouse models. Nonetheless, the diversified expression patterns, coupled with the prognostic values of PRDX family, still require explanation. This study aimed at investigating the clinical importance and biological of PRDXs in breast cancer. PATIENTS AND METHODS: Specimens of paraffin sections used for immunohistochemistry were collected from the hospital and the remaining patient information was retrieved from online databases. The expression and survival data of PRDXs in patients with breast cancer were from ONCOMINE, GEPIA, Kaplan-Meier Plotter. cBioPortal, Metascape, String, Cytoscape and DAVID were used to predict functions and pathways of the changes in PRDXs and their frequently altered neighbor genes. Immunohistochemistry was used to detect the expression of PRDXs in breast cancer. RESULTS: We discovered the expression levels of PRDX1-5 were higher in breast cancer tissues than in normal tissues, whereas the expression level of PRDX6 was observed as lower in the former one in comparison with that of the latter one. There existed a correlation between the expression levels of PRDX4, 5 and the advanced tumor stage. Survival analysis revealed that the expression of PRDXs were all associated with relapse-free survival (RFS) in all of the patients with breast cancer. Eventually, we discovered significant regulation of the cellular oxidant detoxification and detoxification of ROS by the PRDX changes, together with obtaining the core modules of genes (TXN, TXN2, TXNRD1, TXNRD2, GPX1 and GPX2) linked to the PRDX family of genes in breast cancer. CONCLUSION: The PRDX family is widely involved in the development of breast cancer and affects the prognosis of patients. The functions and pathways of the changes in PRDXs and their frequently altered neighbor genes can be further verified by wet experiments.

KCNK levels are prognostic and diagnostic markers for hepatocellular carcinoma.

Two-pore-domain (KCNK, K2P) K+ channels are transmembrane protein complexes that control the flow of ions across biofilms, which underlie many essential cellular functions. Because KCNK family members are known to contribute to tumorigenesis in various types of cancer, we hypothesized that they might be differentially expressed in hepatocellular carcinoma (HCC) cells as compared to healthy tissue and serve as diagnostic or prognostic biomarkers. We tested this hypothesis through bioinformatic analyses of publicly available data for the expression of various KCNK subunits in HCC. We observed reduced expression of KCNK2, KCNK15, and KCNK17 in liver cancer, as well as overexpression of KCNK9, all of which correlated with a better prognosis for HCC patients per survival analyses. Moreover, ROC curves indicated that KCNK2, KCNK9, KCNK15, and KCNK17 levels could be used as a diagnostic biomarker for HCC. Finally, our western blot and qRT-PCR results were consistent with those obtained from bioinformatic analyses. Taken together, these results suggest that KCNK2, KCNK9, KCNK15, and KCNK17 could serve as potential diagnostic and prognostic biomarkers of HCC.

Expressions and prognostic values of the E2F transcription factors in human breast carcinoma.

E2F transcription factors (E2Fs) are a family of transcription factors involved in cell proliferation, differentiation, and apoptosis. Their important roles in the development and metastasis of breast carcinoma (BC) have been discovered by previous in vitro and in vivo studies. Yet, expressions and distinct prognostic values of these eight E2Fs in human BC remain unclear in many respects. In this study, we aimed to reveal their roles in BC through analyzing the transcription and survival data of the E2Fs in BC patients from four online databases including ONCOMINE, Breast Cancer Gene-Expression Miner v4.1, cBioPortal for Cancer Genomics, and Kaplan-Meier Plotter. We found the overexpression of E2Fs in BC tissues compared with normal breast tissues, except for E2F4. Higher expression levels of E2Fs, except for E2F4 and E2F6, were associated with higher levels of Scarff-Bloom-Richardson grade of BC. Alterations of E2Fs were found to be significantly correlated with poorer overall survival of BC patients. Through plotting the survival curve in the Kaplan-Meier Plotter, it was found that higher mRNA levels of E2F1, E2F3, E2F7, and E2F8 were associated with poorer relapse-free survival in all BC patients, indicating that they are potential targets for individualized treatments of BC patients. Conversely, higher mRNA expression level of E2F4 predicted better RFS in BC patients, suggesting E2F4 as a new biomarker for BC prognosis. Considering currently available limited evidence, further studies need to be performed to investigate the roles of E2Fs in BC.

Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma.

The present study aimed to identify new key genes as potential biomarkers for the diagnosis, prognosis or targeted therapy of clear cell renal cell carcinoma (ccRCC). Three expression profiles (GSE36895, GSE46699 and GSE71963) were collected from Gene Expression Omnibus. GEO2R was used to identify differentially expressed genes (DEGs) in ccRCC tissues and normal samples. The Database for Annotation, Visualization and Integrated Discovery was utilized for functional and pathway enrichment analysis. STRING v10.5 and Molecular Complex Detection were used for protein-protein interaction (PPI) network construction and module analysis, respectively. Regulation network analyses were performed with the WebGestal tool. UALCAN web-portal was used for expression validation and survival analysis of hub genes in ccRCC patients from The Cancer Genome Atlas (TCGA). A total of 65 up- and 164 downregulated genes were identified as DEGs. DEGs were enriched with functional terms and pathways compactly related to ccRCC pathogenesis. Seventeen hub genes and one significant module were filtered out and selected from the PPI network. The differential expression of hub genes was verified in TCGA patients. Kaplan-Meier plot showed that high mRNA expression of enolase 2 (ENO2) was associated with short overall survival in ccRCC patients (P=0.023). High mRNA expression of cyclin D1 (CCND1) (P<0.001), fms related tyrosine kinase 1 (FLT1) (P=0.004), plasminogen (PLG) (P<0.001) and von Willebrand factor (VWF) (P=0.008) appeared to serve as favorable factors in survival. These findings indicate that the DEGs may be key genes in ccRCC pathogenesis and five genes, including ENO2, CCND1, PLT1, PLG and VWF, may serve as potential prognostic biomarkers in ccRCC.

Transcriptional E2F1/2/5/8 as potential targets and transcriptional E2F3/6/7 as new biomarkers for the prognosis of human lung carcinoma.

E2F is a group of genes that encode a family of transcription factors (TFs) in higher eukaryotes and participate in cell cycle regulation and DNA synthesis in mammalian cells. Evidence from cell lines, mouse models, and human tissues indicates that TFs are implicated in lung cancer (LC) tumorigenesis. However, the diverse expression patterns and prognostic values of eight E2Fs have yet to be elucidated. In the current study, we examined the transcriptional and survival data of E2Fs in patients with LC from ONCOMINE, GEPIA, Kaplan-Meier Plotter, and cBioPortal databases. We found that the expression levels of E2F1/2/3/5/6/7/8 were higher in lung adenocarcinoma and squamous cell lung carcinoma tissues than in lung tissues, whereas the expression level of E2F4 was lower in the former than in the latter. The expression levels of E2F2/4/5/7/8 were correlated with advanced tumor stage. Survival analysis using the Kaplan-Meier Plotter database revealed that the high transcription levels of E2F1/2/4/5/7/8 were associated with low relapse-free survival (RFS) in all of the patients with LC. Conversely, high E2F3/6 levels predicted high RFS in these patients. This study implied that E2F3/6/7 are potential targets of precision therapy for patients with LC and that E2F1/2/4/5/8 are new biomarkers for the prognosis of LC.