A five-gene signature may predict sunitinib sensitivity and serve as prognostic biomarkers for renal cell carcinoma

Network analysis of histopathological image features and genomics data improving prognosis performance in clear cell renal cell carcinoma

INTRODUCTION

Clear cell renal cell carcinoma is the most common type of kidney cancer, but the prediction of prognosis remains a challenge.

METHODS

We collected whole-slide histopathological images, corresponding clinical and genetic information from the The Cancer Imaging Archive and The Cancer Genome Atlas databases and randomly divided patients into training (n = 197) and validation (n = 84) cohorts. After feature extraction by CellProfiler, we used 2 different machine learning techniques (Least Absolute Shrinkage and Selector Operation-regularized Cox and Support Vector Machine-Recursive Feature Elimination) and weighted gene co-expression network analysis to select prognosis-related image features and genes, respectively. These features and genes were integrated into a joint model using random forest and used to create a nomogram that combines other predictive indicators.

RESULTS

A total of 4 overlapped features were identified, represented by the computed histopathological risk score in the random forest model, and showed predictive value for overall survival (test set: 1-year area under the curves (AUC) = 0.726, 3-year AUC = 0.727, and 5-year AUC = 0.764). The histopathological-genetic risk score (HGRS) integrating the genetic information computed performed better than the model that used image features only (test set: 1-year AUC = 0.682, 3-year AUC = 0.734, and 5-year AUC = 0.78). The nomogram (gender, stage, and HGRS) achieved the highest net benefit according to decision curve analysis compared to HGRS or clinical model.

CONCLUSION

This study developed a histopathological-genetic-related nomogram by combining histopathological features and clinical predictors, providing a more comprehensive prognostic assessment for clear cell renal cell carcinoma patients.

Identification of miRNAs and Their Target Genes Associated with Sunitinib Resistance in Clear Cell Renal Cell Carcinoma Patients

Sunitinib has greatly improved the survival of clear cell renal cell carcinoma (ccRCC) patients in recent years. However, 20-30% of treated patients do not respond. To identify miRNAs and genes associated with a response, comparisons were made between biopsies from responder and non-responder ccRCC patients. Using integrated transcriptomic analyses, we identified 37 miRNAs and 60 respective target genes, which were significantly associated with the NF-kappa B, PI3K-Akt and MAPK pathways. We validated expression of the miRNAs (miR-223, miR-155, miR-200b, miR-130b) and target genes (FLT1, PRDM1 and SAV1) in 35 ccRCC patients. High levels of miR-223 and low levels of FLT1, SAV1 and PRDM1 were associated with worse overall survival (OS), and combined miR-223 + SAV1 levels distinguished responders from non-responders (AUC = 0.92). Using immunohistochemical staining of 170 ccRCC patients, VEGFR1 (FLT1) expression was associated with treatment response, histological grade and RECIST (Response Evaluation Criteria in Solid Tumors) score, whereas SAV1 and BLIMP1 (PRDM1) were associated with metachronous metastatic disease. Using in situ hybridisation (ISH) to detect miR-155 we observed higher tumoural cell expression in non-responders, and non-tumoural cell expression with increased histological grade. In summary, our preliminary analysis using integrated miRNA-target gene analyses identified several novel biomarkers in ccRCC patients that surely warrant further investigation.

CircRNA_001895 promotes sunitinib resistance of renal cell carcinoma through regulation of apoptosis and DNA damage repair

Sunitinib, an inhibitor of receptor tyrosine kinase, possesses anti-tumor activity in renal cell carcinoma (RCC) through its anti-angiogenic effects. However, patients with advanced RCC are resistant to sunitinib. Dysregulated circRNAs has been shown to be associated with drug resistance in various tumors. However, little is known about the effect of circRNA_001895 on sunitinib resistance of RCC. First, the expression of circRNA_001895 was found to be higher in sunitinib-resistant RCC tissues than chemosensitive tumor tissues. Half maximal inhibitory concentration of sunitinib-resistant RCC cells (786-O/R and ACHN/R) was higher than sunitinib-sensitive 786-O and ACHN cells. CircRNA_001895 was also upregulated in 786-O/R and ACHN/R cells. Second, data from colony formation and flow cytometry analysis showed that knockdown of circRNA_001895 suppressed cell proliferation and promoted cell apoptosis in 786-O/R and ACHN/R cells. Moreover, the protein expression of phosphorylated histone H2AX (γH2AX) was enhanced, while phosphorylated DNA-dependent protein kinase (p-DNA-PK) and Rad51 were reduced in 786-/R and ACHN/R by knockdown of circRNA_001895. Lastly, knockdown of circRNA_001895 conferred sensitivity of in vivo tumor growth to sunitinib. In conclusion, circRNA_001895 was implicated in the sunitinib resistance in RCC through regulation of apoptosis and DNA damage repair, suggesting that circRNA_001895 might be a potential therapeutic target for advanced RCC.

Establishing a metastasis-related diagnosis and prognosis model for lung adenocarcinoma through CRISPR library and TCGA database

PURPOSE

Existing biomarkers for diagnosing and predicting metastasis of lung adenocarcinoma (LUAD) may not meet the demands of clinical practice. Risk prediction models with multiple markers may provide better prognostic factors for accurate diagnosis and prediction of metastatic LUAD.

METHODS

An animal model of LUAD metastasis was constructed using CRISPR technology, and genes related to LUAD metastasis were screened by mRNA sequencing of normal and metastatic tissues. The immune characteristics of different subtypes were analyzed, and differentially expressed genes were subjected to survival and Cox regression analyses to identify the specific genes involved in metastasis for constructing a prediction model. The biological function of RFLNA was verified by analyzing CCK-8, migration, invasion, and apoptosis in LUAD cell lines.

RESULTS

We identified 108 differentially expressed genes related to metastasis and classified LUAD samples into two subtypes according to gene expression. Subsequently, a prediction model composed of eight metastasis-related genes (RHOBTB2, KIAA1524, CENPW, DEPDC1, RFLNA, COL7A1, MMP12, and HOXB9) was constructed. The areas under the curves of the logistic regression and neural network were 0.946 and 0.856, respectively. The model effectively classified patients into low- and high-risk groups. The low-risk group had a better prognosis in both the training and test cohorts, indicating that the prediction model had good diagnostic and predictive power. Upregulation of RFLNA successfully promoted cell proliferation, migration, invasion, and attenuated apoptosis, suggesting that RFLNA plays a role in promoting LUAD development and metastasis.

CONCLUSION

The model has important diagnostic and prognostic value for metastatic LUAD and may be useful in clinical applications.

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.

Characterization of glucose metabolism in breast cancer to guide clinical therapy

Background

Breast cancer (BRCA) ranks as a leading cause of cancer death in women worldwide. Glucose metabolism is a noticeable characteristic of the occurrence of malignant tumors. In this study, we aimed to construct a novel glycometabolism-related gene (GRG) signature to predict overall survival (OS), immune infiltration and therapeutic response in BRCA patients.

Materials and methods

The mRNA sequencing and corresponding clinical data of BRCA patients were obtained from public cohorts. Lasso regression was applied to establish a GRG signature. The immune infiltration was evaluated with the ESTIMATE and CIBERSORT algorithms. The drug sensitivity was estimated using the value of IC50, and further forecasted the therapeutic response of each patient. The candidate target was selected in Cytoscape. A nomogram was constructed via the R package of “rms”.

Results

We constructed a six-GRG signature based on CACNA1H, CHPF, IRS2, NT5E, SDC1 and ATP6AP1, and the high-risk patients were correlated with poorer OS (P = 2.515 × 10⁻⁷). M2 macrophage infiltration was considerably superior in high-risk patients, and CD8⁺ T cell infiltration was significantly higher in low-risk patients. Additionally, the high-risk group was more sensitive to Lapatinib. Fortunately, SDC1 was recognized as candidate target and patients had a better OS in the low-SDC1 group. A nomogram integrating the GRG signature was developed, and calibration curves were consistent between the actual and predicted OS.

Conclusions

We identified a novel GRG signature complementing the present understanding of the targeted therapy and immune biomarker in breast cancer. The GRGs may provide fresh insights for individualized management of BRCA patients.

Construction of Lymph Node Metastasis-Related Prognostic Model and Analysis of Immune Infiltration Mode in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a major cause for global cancer-related deaths. Research reports demonstrate that lymph node metastasis (LNM) is pertinent to the survival rate of LUAD patients, and crux lies in the lack of biomarkers that could distinguish patients with LNM. We aimed to verify the LNM-related prognostic biomarkers in LUAD.

Methods

We firstly accessed the expression data of mRNA from The Cancer Genome Atlas (TCGA) database and then obtained samples with LNM (N+) and without LNM (N-). Differential expression analysis was conducted to acquire differentially expressed genes (DEGs). Univariate-LASSO-multivariate Cox regression analyses were performed on DEGs to build a risk model and obtain optimal genes. Afterwards, effectiveness and independence of risk model were assessed based on TCGA-LUAD and GSE31210 datasets. Moreover, a nomogram was established combining clinical factors and riskscores. Nomogram performance was measured by calibration curves. The infiltration abundance of immune cells was scored with CIBERSORT to explore the differences between high- and low-risk groups. Lastly, gene set enrichment analysis (GSEA) was used to investigate differences in immune features between the two risk groups.

Results

Nine optimal feature genes closely related to LNM in LUAD were identified to construct a risk model. Prognostic ability of the risk model was verified in independent databases. Patients were classified into high- and low-risk groups in accordance with their median riskscores. CIBERSORT score displayed differences in immune cell infiltration like T cells CD4 memory resting between high/low-risk groups. LNM-related genes may also be closely relevant to immune features. Additionally, GSEA indicated that differential genes in the two risk groups were enriched in genes related to immune cells.

Conclusion

This research built a risk model including nine optimal feature genes, which may be potential biomarkers for LUAD.

Uncovering of potential molecular markers for cervical squamous cell carcinoma (CESC) based on analysis of methylated-differentially expressed genes

OBJECTIVE

Cervical squamous cell carcinoma (CESC) is a cancer with high mortality caused by human papillomavirus. The aim of this study was to uncover potential CESC biomarkers to help early diagnosis and treatment.

MATERIALS AND METHODS

The mRNA transcriptome data and DNA methylation data were downloaded from database for the identification of differentially expressed mRNAs (DEmRNAs) and DNA methylation analysis. Functional analysis was used to reveal the molecular functions. Then, the association between differential methylation and DEmRNA was analyzed. Protein-protein interaction (PPI) network analysis was performed on the selected differentially methylated genes (DEGs). Subsequently, we analyzed the prognosis and constructed a prognostic risk model. We also performed diagnostic analyses of risk model genes. In addition, we verified the protein expression level of identified DEGs.

RESULTS

1438 DEmRNAs, 1669 differentially methylated sites (DMSs), 46 differentially methylated CpG islands and 53 differential methylation genes (DMGs) were obtained. In PPI, the highest interaction scores were MX2 and IRF8, and their interaction score was 0.928. Interestingly, 5 DMGs were found to be associated with CESC prognosis. In addition, our results demonstrated that high risk score was associated with poor prognosis of CESC. Furthermore, it was found that ZIK1, ZNRF2, HHEX, VCAM1 could be diagnostic molecular markers for CESC.

CONCLUSION

Analysis of methylated-differentially expressed genes may contribute to the identification of early diagnosis and therapeutic targets of CESC. In addition, a prognostic model based on 5 DMGs can be used to predict the prognosis of CESC.

A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients

Simple Summary

Clear cell renal cell carcinoma is a type of kidney cancer which comprises the majority of all renal cell carcinomas. Many efforts have been made to identify biomarkers which could help healthcare professionals better treat this kind of cancer. With extensive public data available, we conducted a machine learning study to determine a gene signature that could indicate patient survival with high accuracy. Through the min-Redundancy and Max-Relevance algorithm we generated a signature of 13 genes highly correlated with patient outcomes. These findings reveal potential strategies for personalized medicine in the clinical practice.

Abstract

Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.

Potential Use of CTCs as Biomarkers in Renal Cancer Patients

We demonstrated that the CellCollector is an appropriate tool for detecting CTCs in RCC patients. We examined EpCAM and MUC1 expression levels in RCC tissues and cell lines and analyzed the detection rate of CTCs in blood samples ex vivo using an anti-EpCAM antibody-covered straight or spiraled CellCollector. Eight matched samples were examined for affinity to the anti-EpCAM vs. anti-EpCAM/anti-MUC1 antibody-covered wire. The use of this combination of antibodies allowed us to classify patients with lung metastasis. Finally, four patients were analyzed in vivo. In conclusion, both straight (ex vivo, in vivo) and spiraled (ex vivo) wires detected CTCs.

Prognostic value and immunological role of AXL gene in clear cell renal cell carcinoma associated with identifying LncRNA/RBP/AXL mRNA networks

Backgrounds

This article aimed to explore the prognostic and immunological roles of AXL gene in clear cell renal cell carcinoma (ccRCC) for overall survival (OS) and to identify the LncRNA/RBP/AXL mRNA networks.

Methods

AXL-related gene expression matrix and clinical data were obtained from The Cancer Genome Atlas (TCGA) dataset and AXL-related pathways were identified by gene set enrichment analysis (GSEA). We performed univariate/multivariate Cox regression analysis to evaluate independent prognostic factors and the relationships between AXL and immunity were also investigated.

Results

The outcomes of us indicated that the AXL mRNA expression was up-regulated in ccRCC samples and high expression of AXL was associated with worse OS in TCGA dataset (P < 0.01). Further external verification results from HPA, UALCAN, ICGC dataset, GSE6344, GSE14994, and qRT-PCR remained consistent (all P < 0.05). AXL was also identified as an independent prognostic factor for ccRCC by univariate/multivariate Cox regression analysis (both P < 0.05). A nomogram including AXL expression and clinicopathological factors was established by us and GSEA results found that elevated AXL expression was associated with the JAK-STAT, P53, WNT, VEGF and MAPK signaling pathways. In terms of immunity, AXL was dramatically linked to tumor microenvironment, immune cells, immune infiltration, immune checkpoint molecules and tumor mutational burden (TMB). As for its potential mechanisms, we also identified several LncRNA/RBP/AXL mRNA axes.

Conclusions

AXL was revealed to play prognostic and immunological roles in ccRCC and LncRNA/RBP/AXL mRNA axes were also identified by us for its potential mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02322-y.

Identification of C3 and FN1 as potential biomarkers associated with progression and prognosis for clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most lethal urological malignancies, but the pathogenesis and prognosis of ccRCC remain obscure, which need to be better understand.

Methods

Differentially expressed genes were identified and function enrichment analyses were performed using three publicly available ccRCC gene expression profiles downloaded from the Gene Expression Omnibus database. The protein-protein interaction and the competing endogenous RNA (ceRNA) networks were visualized by Cytoscape. Multivariate Cox analysis was used to predict an optimal risk mode, and the survival analysis was performed with the Kaplan-Meier curve and log-rank test. Protein expression data were downloaded from Clinical Proteomic Tumor Analysis Consortium database and Human Protein Atlas database, and the clinical information as well as the corresponding lncRNA and miRNA expression data were obtained via The Cancer Genome Atlas database. The co-expressed genes and potential function of candidate genes were explored using data exacted from the Cancer Cell Line Encyclopedia database.

Results

Of the 1044 differentially expressed genes shared across the three datasets, 461 were upregulated, and 583 were downregulated, which significantly enriched in multiple immunoregulatory-related biological process and tumor-associated pathways, such as HIF-1, PI3K-AKT, P53 and Rap1 signaling pathways. In the most significant module, 36 hub genes were identified and were predominantly enriched in inflammatory response and immune and biotic stimulus pathways. Survival analysis and validation of the hub genes at the mRNA and protein expression levels suggested that these genes, particularly complement component 3 (C3) and fibronectin 1 (FN1), were primarily responsible for ccRCC tumorigenesis and progression. Increased expression of C3 or FN1 was also associated with advanced clinical stage, high pathological grade, and poor survival in patients with ccRCC. Univariate and multivariate Cox regression analysis qualified the expression levels of the two genes as candidate biomarkers for predicting poor survival. FN1 was potentially regulated by miR-429, miR-216b and miR-217, and constructed a bridge to C3 and C3AR1 in the ceRNA network, indicating a critical position of FN1.

Conclusions

The biomarkers C3 and FN1 could provide theoretical support for the development of a novel prognostic tool to advance ccRCC diagnosis and targeted therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08818-0.

Integrated mRNA and miRNA Transcriptomic Analyses Reveals Divergent Mechanisms of Sunitinib Resistance in Clear Cell Renal Cell Carcinoma (ccRCC)

Simple Summary

Clear cell renal cell carcinoma (ccRCC) is a frequent cancer that causes more than 100,000 deaths every year. Treatment with drugs that target enzymes that help tumours grow such as sunitinib have greatly improved the prospects for ccRCC patients, however a large proportion of patients become resistant. We created sunitinib resistant cell lines and identified consequent changes in gene (and miRNA) expression by microarray analyses. Using this approach, we identified different pathways of resistance suggesting that tumour cells have many ways to overcome sunitinib treatment. We were able to overcome resistance in cells by inhibiting a protein, PD-L1, that is targeted by many immunotherapeutics currently in use for ccRCC patients suggesting a combination of immunotherapy and sunitinib may benefit patients. In addition, we identified miRNAs that are common to multiple resistance mechanisms suggesting they may be useful targets for future studies.

Abstract

The anti-angiogenic therapy sunitinib remains the standard first-line treatment for meta static clear cell renal cell carcinoma (ccRCC). However, acquired resistance develops in nearly all responsive patients and represents a major source of treatment failure. We used an integrated miRNA and mRNA transcriptomic approach to identify miRNA:target gene interactions involved in sunitinib resistance. Through the generation of stably resistant clones in three ccRCC cell lines (786-O, A498 and Caki-1), we identified non-overlapping miRNA:target gene networks, suggesting divergent mechanisms of sunitinib resistance. Surprisingly, even though the genes involved in these networks were different, they shared targeting by multiple members of the miR-17~92 cluster. In 786-O cells, targeted genes were related to hypoxia/angiogenic pathways, whereas, in Caki-1 cells, they were related to inflammatory/proliferation pathways. The immunotherapy target PD-L1 was consistently up-regulated in resistant cells, and we demonstrated that the silencing of this gene resulted in an increase in sensitivity to sunitinib treatment only in 786-O-resistant cells, suggesting that some ccRCC patients might benefit from combination therapy with PD-L1 checkpoint inhibitors. In summary, we demonstrate that, although there are clearly divergent mechanisms of sunitinib resistance in ccRCC subtypes, the commonality of miRNAs in multiple pathways could be targeted to overcome sunitinib resistance.

Loss of Setd2 associates with aberrant microRNA expression and contributes to inflammatory bowel disease progression in mice

Both SETD2-mediated H3K36me3 and miRNAs play critical epigenetic roles in inflammatory bowel disease (IBD) and involve in the dysfunctional intestinal barrier. However, little is known about cross-talk between these two types of regulators in IBD progression. We performed small RNA sequencing of Setd2 epithelium-specific knockout mice (Setd2^Vil-KO) and wild-type controls, both with DSS-induced colitis, and designed a framework for integrative analysis. Firstly, we integrated the downloaded ChIP-seq data with miRNA expression profiles and identified a significant intersection of pre-miRNA expression and H3K36me3 modification. A significant inverse correlation was detected between changes of H3K36me3 modification and expression of the 171 peak-covered miRNAs. We further integrated RNA-seq data with predicted miRNA targets to screen negatively regulated miRNA-mRNA pairs and found the H3K36me3-associated differentially expressed microRNAs significantly enriched in cell-cell junction and signaling pathways. Using network analysis, we identified ten hub miRNAs, among which six are H3K36me3-associated, suggesting therapeutic targets for IBD patients with SETD2-deficiency.

Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC. Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis. Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC. Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Predicting Survival in Patients With HNSCC

Head and neck squamous cell cancer (HNSCC) is one of the most common types of cancer worldwide. There have been many reports suggesting that biomarkers explored via database mining plays a critical role in predicting HNSCC prognosis. However, a single biomarker for prognostic analysis is not adequate. Additionally, there is growing evidence indicating that gene signature could be a better choice for HNSCC prognosis. We performed a comprehensive analysis of mRNA expression profiles using clinical information of HNSCC patients from The Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis (GSEA) was performed, and we found that a set of genes involved in epithelial mesenchymal transition (EMT) contributed to HNSCC. Cox proportional regression model was used to identify a four-gene (WIPF1, PPIB, BASP1, PLOD2) signature that were significantly associated with overall survival (OS), and all the four genes were significantly upregulated in tumor tissues. We successfully classified the patients with HNSCC into high-risk and low-risk groups, where in high-risk indicated poorer patient prognosis, indicating that this gene signature might be a novel potential biomarker for the prognosis of HNSCC. The prognostic ability of the gene signature was further validated in an independent cohort from the Gene Expression Omnibus (GEO) database. In conclusion, we identified a four-EMT-based gene signature which provides the potentiality to serve as novel independent biomarkers for predicting survival in HNSCC patients, as well as a new possibility for individualized treatment of HNSCC.

Real-world outcomes in patients with metastatic renal cell carcinoma according to risk factors: the STAR-TOR registry

Aim: Examine outcomes in sunitinib-treated patients by International Metastatic RCC Database Consortium (IMDC) or Memorial Sloan-Kettering Cancer Center (MSKCC) risk factors. Patients & methods: Patients enrolled in STAR-TOR registry (n = 327). End points included overall survival, progression-free survival and objective response rate. Results: Overall survival was similar for IMDC 0 versus 1 (p = 0.238) or 2 versus ≥3 (p = 0.156), but different for MSKCC (0 vs 1, p = 0.037; 2 vs ≥3, p = 0.001). Progression-free survival was similar for IMDC 2 versus 3 (p = 0.306), but different for MSKCC (p = 0.009). Objective response rate was different for IMDC 1 (41.9%) and 2 (29.5%) and similar for MSKCC 1 (34.4%) and 2 (31.0%). Conclusion: Outcome data varied according to IMDC or MSKCC. MSKCC model accurately stratify patients into risk groups. Clinical trial registration: NCT00700258 (ClinicalTrials.gov).

Sirtuin 6 regulates the proliferation and survival of clear cell renal cell carcinoma cells via B-cell lymphoma 2

Sirtuin 6 (SIRT6) is a member of the third family of longevity proteins (SIRTs) that is involved in the development of different types of cancer. However, the potential role of SIRT6 in clear cell renal cell carcinoma (ccRCC) and its molecular mechanism have not yet been fully elucidated. Therefore, the present study aimed to investigate the association between SIRT6 and ccRCC, and to further examine the underlying mechanism of its effect on ccRCC proliferation, using bioinformatics analysis, and in vitro and in vivo experiments. The results of the present study demonstrated that SIRT6 was upregulated in ccRCC tissues. In addition, bioinformatics analysis revealed that high SIRT6 expression was closely associated with poor prognosis of patients with ccRCC. In vitro experiments demonstrated that silencing SIRT6 expression in ccRCC-derived 769-P and 786-O cells significantly inhibited their proliferation, migration and invasion. Consistent with these results, in vivo assays demonstrated that SIRT6 knockdown markedly attenuated tumor growth arising from 769-P cells. Furthermore, depletion of SIRT6 enhanced the sensitivity of ccRCC cells to cisplatin. Notably, silencing SIRT6 expression decreased B-cell lymphoma 2 (Bcl-2) expression and increased Bax expression, respectively. Taken together, these results suggest that SIRT6 acts as a proto-oncogene in ccRCC through the augmentation of the Bcl-2-dependent pro-survival pathway, and may be used as a therapeutic target for patients with ccRCC.

A Novel Six-Gene Signature for Prognosis Prediction in Ovarian Cancer

Ovarian cancer (OC) is the most malignant tumor in the female reproductive tract. Although abundant molecular biomarkers have been identified, a robust and accurate gene expression signature is still essential to assist oncologists in evaluating the prognosis of OC patients. In this study, samples from 367 patients in The Cancer Genome Atlas (TCGA) database were subjected to mRNA expression profiling. Then, we used a gene set enrichment analysis (GSEA) to screen genes correlated with epithelial–mesenchymal transition (EMT) and assess their prognostic power with a Cox proportional regression model. Six genes (TGFBI, SFRP1, COL16A1, THY1, PPIB, BGN) associated with overall survival (OS) were used to construct a risk assessment model, after which the patients were divided into high-risk and low-risk groups. The six-gene signature was an independent prognostic biomarker of OS for OC patients based on the multivariate Cox regression analysis. In addition, the six-gene model was validated with samples from the Gene Expression Omnibus (GEO) database. In summary, we established a six-gene signature relevant to the prognosis of OC, which might become a therapeutic tool with clinical applications in the future.

A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most universal malignant liver tumors worldwide. However, there were no systematic studies to establish glycolysis‑related gene pairs (GRGPs) signatures for the patients with HCC. Therefore, the study aimed to establish novel GRGPs signatures to better predict the prognosis of HCC.

Methods

Based on the data from Gene Expression Omnibus, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium databases, glycolysis-related mRNAs were used to construct GRGPs. Cox regression was applied to establish a seventeen GRGPs signature in TCGA dataset, which was verified in two validation (European and American, and Asian) datasets.

Results

Seventeen prognostic GRGPs (HMMR_PFKFB1, CHST1_GYS2, MERTK_GYS2, GPC1_GYS2, LDHA_GOT2, IDUA_GNPDA1, IDUA_ME2, IDUA_G6PD, IDUA_GPC1, MPI_GPC1, SDC2_LDHA, PRPS1_PLOD2, GALK1_IER3, MET_PLOD2, GUSB_IGFBP3, IL13RA1_IGFBP3 and CYB5A_IGFBP3) were identified to be significantly progressive factors for the patients with HCC in the TCGA dataset, which constituted a GRGPs signature. The patients with HCC were classified into low-risk group and high-risk group based on the GRGPs signature. The GRGPs signature was a significantly independent prognostic indicator for the patients with HCC in TCGA (log-rank P = 2.898e−14). Consistent with the TCGA dataset, the patients in low-risk group had a longer OS in two validation datasets (European and American: P = 1.143e−02, and Asian: P = 6.342e−08). Additionally, the GRGPs signature was also validated as a significantly independent prognostic indicator in two validation datasets.

Conclusion

The seventeen GRGPs and their signature might be molecular biomarkers and therapeutic targets for the patients with HCC.

Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer

Background

Although genes have been previously detected in pancreatic cancer (PC), aberrant genes that play roles in resectable pancreatic cancer should be further assessed.

Methods

Messenger RNA samples and clinicopathological data corrected with PC were downloaded from The Cancer Genome Atlas (TCGA). Resectable PC patients were randomly divided into a primary set and a validation set. Univariable Cox regression analysis, lasso-penalized Cox regression analysis, and multivariable Cox analysis were implemented to distinguish survival-related genes (SRGs). A risk score based on the SRGs was calculated by univariable Cox regression analysis. A genomic-clinical nomogram was established by integrating the risk score and clinicopathological data to predict overall survival (OS) in resectable PC.

Results

Five survival-related genes (AADAC, DEF8, HIST1H1C, MET, and CHFR) were significantly correlated with OS in resectable PC. The resectable PC patients, based on risk score, were sorted into a high-risk group that showed considerably unfavorable OS (p < 0.001) than the low-risk group, in both the primary set and the validation set. The concordance index (C-index) was calculated to evaluate the predictive performance of the nomogram were respectively in the primary set [0.696 (0.608–0.784)] and the validation set [0.682 (0.606–0.758)]. Additionally, gene set enrichment Analysis discovered several meaningful enriched pathways.

Conclusion

Our study identified five prognostic gene biomarkers for OS prediction and which facilitate postoperative molecular target therapy for the resectable PC, especially the nomic-clinical nomogram which may be used as an effective model for the postoperative OS evaluation and also an optimal therapeutic tool for the resectable PC.

Computational Prediction of Drug Responses in Cancer Cell Lines From Cancer Omics and Detection of Drug Effectiveness Related Methylation Sites

Accurately predicting the response of a cancer patient to a therapeutic agent remains an important challenge in precision medicine. With the rise of data science, researchers have applied computational models to study the drug inhibition effects on cancers based on cancer genomics and transcriptomics. Moreover, a common epigenetic modification, DNA methylation, has been related to the occurrence and development of cancer, as well as drug effectiveness. Therefore, it is helpful for improvement of drug response prediction through exploring the relationship between DNA methylation and drug effectiveness. Here, we proposed a computational model to predict drug responses in cancers through integration of cancer genomics, transcriptomics, epigenomics, and compound chemical properties. Meanwhile, we applied a regularized regression model (Least Absolute Shrinkage and Selection Operator, lasso) to detect the methylation sites that were closely related to drug effectiveness. The prediction models were trained on a well-known pharmacogenomics data resource, Genomics of Drug Sensitivity in Cancer (GDSC). The cross-validation indicates that the performance of the prediction model using DNA methylation is comparable to that of using other cancer omics, including oncogene mutation and gene expression data. It indicates the important role of DNA methylation in prediction of drug responses. Encyclopedia of DNA Elements (ENCODE) and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST2) database analyses suggest that the methylation sites associated with drug effectiveness are mainly located in the transcription factor (TF) binding region. Therefore, we hypothesized that the sensitivity of cancer cells to drugs could be regulated by changing the methylation modification of TF binding region. In conclusion, we confirmed the important role of DNA methylation in prediction of drug responses, and provided some methylation sites that closely related to the drug effectiveness, which may be a great regulatory target for improvement of drug treatment effects on cancer patients.

Identification of lapatinib sensitivity-related genes by integrative functional module analysis

Background

Globally, gastric carcinoma (GC) is one of the most commonly encountered malignancies and is the second highest contributor to cancer mortality. Lapatinib is a potent, orally-bioavailable small-molecule inhibitor of both epidermal growth factor receptor and human epidermal growth factor receptor-2 tyrosine kinases, and is administered to treat GC. However, a large proportion of patients either develop resistance to or do not respond to lapatinib, often because the treatment activates alternative signaling pathways. It is, therefore, vital to identify the key pathways which mediate resistance to lapatinib treatment.

Methods

The lapatinib sensitivity-related genes were extracted from the CellMiner database (version 2.2) using “NCI-60 Analysis Tools”. The differentially expressed genes (DEGs) in gastric cancer were derived from The Cancer Genome Atlas (TCGA) database, the protein-protein interaction (PPI) network was derived from the Human Protein Reference Database (HPRD), and the Database for Annotation, Visualization and Integrated Discovery (DAVID) facilitated the functional analysis. The cell function was tested by CCK-8 cell viability assay, colony formation assay, acridine orange/ethidium bromide (AO/EB) staining, and Transwell assay.

Results

The functional linkage networks of lapatinib sensitivity were constructed. Two modules were identified, and pathway analysis indicated that these modules were involved in several pathways, including the neuroactive ligand-receptor interaction network and the Rap1 signaling pathway. Finally, the breast cancer anti-estrogen resistance 1 (BCAR1) gene was selected for further study with lapatinib-resistant SUN216 cells (SUN216/LR). We found the expression of BCAR1 was upregulated in SUN216/LR cells compared to SUN216 cells. The IC50 of lapatinib in SUN216/LR cells was reduced upon BCAR1 knockdown, as measured by a CCK-8 assay. A clonogenic assay showed fewer SUN216/LR colonies with BCAR1 knockdown and lapatinib treatment.

Conclusions

In brief, we efficiently identified those crucial modules highly related to lapatinib sensitivity in GC by using a topological network method. BCAR1 was identified as a potentially critical gene that plays a role in lapatinib sensitivity, and experiments confirmed that BCAR1 might contribute to lapatinib resistance in GC. These results provide further insight into the molecular basis of lapatinib sensitivity and may offer novel strategies for the future treatment of GC.

The prognostic value of miRNA-18a-5p in clear cell renal cell carcinoma and its function via the miRNA-18a-5p/HIF1A/PVT1 pathway

Purpose Clear cell renal cell carcinoma(ccRCC) is the most common type of renal cell carcinoma. While it is curable when detected at an early stage, some patients presented with advanced disease have poor prognosis. We aimed to identify key genes and miRNAs associated with clinical prognosis in ccRCC.

Methods The microarray datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were analyzed by using GEO2R. Then, Functional enrichment analysis was performed using the DAVID. A retrospective series of 254 ccRCC patients with complete clinical information was included in this study. Kaplan-Meier analysis and multivariate cox regression analysis were used for prognostic analysis. Wound healing assay and transwell assay were designed to evaluate the migration and invasion ability of ccRCC cell lines.

Results miRNA-18a was identified to be related with prognosis of ccRCC by using Kaplan-Meier analysis and multivariate cox regression analysis demonstrated that the prognostic value of miRNA-18a was independent of clinical features. Further studies showed that up-regulation of miRNA-18a had a positive effect on migration and invasion of ccRCC cells. The target gene (HIF1A) of the miRNA-18a was predicted by using the miRPathDB database. The transcription factors of DEGs were identified by using the i-cisTarget. Luckily, HIF1A was found to be one of the transcription factors of DEGs. Among these DEGs, PVT1 may be regulated by HIF1A and be related with prognosis of ccRCC. Finally, validation of miRNA18a/HIF1A/PVT1 pathway was checked via reverse transcription-polymerase chain reaction (RT-PCR) assay in both cell lines and clinical tumor samples.

Conclusion Our research revealed that miRNA18a/HIF1A/PVT1 pathway might play a crucial role in ccRCC progression, providing novel insights into understanding of ccRCC molecular mechanisms. Importantly, miRNA-18a could serve as a potential diagnostic biomarker and therapeutic targets for ccRCC patients.

Comprehensive Analysis of Copy Number Variations in Kidney Cancer by Single-Cell Exome Sequencing

Clear-cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. VHL and PBRM1 are the top two significantly mutated genes in ccRCC specimens, while the genetic mechanism of the VHL/PBRM1-negative ccRCC remains to be elucidated. Here we carried out a comprehensive analysis of single-cell genomic copy number variations (CNVs) in VHL/PBRM1-negative ccRCC. Genomic CNVs were identified at the single-cell level, and the tumor cells showed widespread amplification and deletion across the whole genome. Functional enrichment analysis indicated that the amplified genes are significantly enriched in cancer-related signaling transduction pathways. Besides, receptor protein tyrosine kinase (RTK) genes also showed widespread copy number variations in cancer cells. Our studies indicated that the genomic CNVs in RTK genes and downstream signaling transduction pathways may be involved in VHL/PBRM1-negative ccRCC pathogenesis and progression, and highlighted the role of the comprehensive investigation of genomic CNVs at the single-cell level in both clarifying pathogenic mechanism and identifying potential therapeutic targets in cancers.

Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma

Background

Lung cancer (LC) is one of the most lethal and most prevalent malignant tumors, and its incidence and mortality are increasing annually. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. Several biomarkers have been confirmed by data excavation to be related to metastasis, prognosis and survival. However, the moderate predictive effect of a single gene biomarker is not sufficient. Thus, we aimed to identify new gene signatures to better predict the possibility of LUAD.

Methods

Using an mRNA-mining approach, we performed mRNA expression profiling in large LUAD cohorts (n = 522) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and connections between genes and glycolysis were found in the Cox proportional regression model.

Results

We confirmed a set of nine genes (HMMR, B4GALT1, SLC16A3, ANGPTL4, EXT1, GPC1, RBCK1, SOD1, and AGRN) that were significantly associated with metastasis and overall survival (OS) in the test series. Based on this nine-gene signature, the patients in the test series could be divided into high-risk and low-risk groups. Additionally, multivariate Cox regression analysis revealed that the prognostic power of the nine-gene signature is independent of clinical factors.

Conclusion

Our study reveals a connection between the nine-gene signature and glycolysis. This research also provides novel insights into the mechanisms underlying glycolysis and offers a novel biomarker of a poor prognosis and metastasis for LUAD patients.

Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer

BACKGROUND

Endometrial cancer (EC) is one of the three major gynecological malignancies. Numerous biomarkers that may be associated with survival and prognosis have been identified through database mining in previous studies. However, the predictive ability of single-gene biomarkers is not sufficiently specific. Genetic signatures may be an improved option for prediction. This study aimed to explore data from The Cancer Genome Atlas (TCGA) to identify a new genetic signature for predicting the prognosis of EC.

METHODS

mRNA expression profiling was performed in a group of patients with EC (n = 548) from TCGA. Gene set enrichment analysis was performed to identify gene sets that were significantly different between EC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Quantitative real-time-PCR was used to verify the reliability of the expression of selected mRNAs. Subsequent multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan-Meier survival estimates and the log-rank test were used to validate the significance of risk parameters for prognosis prediction.

RESULT

Nine genes associated with glycolysis (CLDN9, B4GALT1, GMPPB, B4GALT4, AK4, CHST6, PC, GPC1, and SRD5A3) were found to be significantly related to overall survival. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. Based on the nine-gene signature, the 548 patients with EC were divided into high/low-risk subgroups. The prognostic ability of the nine-gene signature was not affected by other factors.

CONCLUSION

A nine-gene signature associated with cellular glycolysis for predicting the survival of patients with EC was developed. The findings provide insight into the mechanisms of cellular glycolysis and identification of patients with poor prognosis in EC.

Cell death-related molecules and biomarkers for renal cell carcinoma targeted therapy

Renal cell carcinoma (RCC) is not sensitive to conventional radio- and chemotherapies and is at least partially resistant to impairments in cell death-related signaling pathways. The hallmarks of RCC formation include diverse signaling pathways, such as maintenance of proliferation, cell death resistance, angiogenesis induction, immune destruction avoidance, and DNA repair. RCC diagnosed during the early stage has the possibility of cure with surgery. For metastatic RCC (mRCC), molecular targeted therapy, especially antiangiogenic therapy (e.g., tyrosine kinase inhibitors, TKIs, such as sunitinib), is one of the main partially effective therapeutics. Various forms of cell death that may be associated with the resistance to targeted therapy because of the crosstalk between targeted therapy and cell death resistance pathways were originally defined and differentiated into apoptosis, necroptosis, pyroptosis, ferroptosis and autophagic cell death based on cellular morphology. Particularly, as a new form of cell death, T cell-induced cell death by immune checkpoint inhibitors expands the treatment options beyond the current targeted therapy. Here, we provide an overview of cell death-related molecules and biomarkers for the progression, prognosis and treatment of mRCC by targeted therapy, with a focus on apoptosis and T cell-induced cell death, as well as other forms of cell death.

Prognostic significance of VHL, HIF1A, HIF2A, VEGFA and p53 expression in patients with clear‑cell renal cell carcinoma treated with sunitinib as first‑line treatment

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell cancer, characterized by the highest mortality rate among other RCC subtypes due to the occurrence of metastasis and drug resistance following surgery. The Von Hippel-Lindau tumor suppressor (VHL)-hypoxia-inducible factor 1 subunit α (HIF1A)/hypoxia-inducible factor 2α (HIF2A)-vascular endothelial growth factor A (VEGFA) protein axis is involved in the development and progression of ccRCC, whereas sunitinib, a tyrosine kinase inhibitor, blocks the binding of VEGFA to its receptor. The aim of the present study was to examine the possible association of the gene expression of VHL, HIF1A, HIF2A, VEGFA and tumor protein P53 (P53) in cancer tissue with the outcome of ccRCC patients who were treated with sunitinib as first-line therapy following nephrec-tomy. A total of 36 ccRCC patients were enrolled, 11 of whom were administered sunitinib post-operatively. Tumor and control samples were collected, and mRNA and protein levels were assessed by reverse transcription-quantitative polymerase chain reaction and western blot analysis, respectively. High mRNA and protein expression levels of HIF2A and VEGFA were found to be associated with shorter overall survival (OS) and progression-free survival (PFS) rates, as well as with unfavorable risk factors of cancer recurrence and mortality. Resistance to sunitinib was also observed; the OS and PFS rates were shorter (median OS and PFS: 12 and 6 months, respectively, vs. undetermined). Sunitinib resistance was associated with high HIF2A and VEGFA protein levels (b=0.57 and b=0.69 for OS and PFS, respectively; P<0.001). Taken together, the findings of this study suggest that the protein levels of HIF2A and VEGFA in tumor tissue may serve as independent prognostic factors in ccRCC. ccRCC patients with increased intratumoral HIF2A and VEGFA protein levels, and unaltered VHL protein levels, are not likely to benefit from sunitinib treatment following nephrectomy; however, this hypothesis requires verification by large-scale replication studies.

Effect of the Nrf2-ARE signaling pathway on biological characteristics and sensitivity to sunitinib in renal cell carcinoma

The aim of the present study was to examine the effects of the nuclear factor erythroid-2 related factor 2-antioxidant-responsive element (Nrf2-ARE) signaling pathway on the biological characteristics and sensitivity to targeted therapy in human renal cell carcinoma (RCC) cells. RCC tissues and adjacent tissues were collected and assessed by immunohistochemistry to determine the expression of Nrf2, NAD(P)H dehydrogenase [quinone] 1 (NQO1) and heme oxygenase-1 (HO-1) to analyze the clinicopathological features of RCC. A series of in vitro experiments were conducted to analyze the biological characteristics of Nrf2-ARE signaling in RCC. The renal cancer cell line, 786-0 was used, and cells was divided into a mock group, negative control group and small hairpin (sh)RNA-Nrf2 group. A Cell Counting Kit-8 assay was performed alongside flow cytometry to detect cell viability, cell cycle stage and apoptosis following treatment with sunitinib. The results demonstrated that Nrf2, NQO1 and HO-1 were significantly upregulated in RCC tissues compared with adjacent tissues and were associated with tumor node metastasis stage, Fuhrman classification and lymph node metastasis. Following shRNA-Nrf2 transfection, the 786-0 cells demonstrated a significant decrease in viability, cell invasion and scratch healing rate, and the mRNA and protein expression levels of Nrf2, NQO1, HO-1 and glutathione transferase were significantly decreased, which enhanced the sensitivity to sunitinib, arrested cells in the G0/G1 phase and increased apoptosis. In conclusion, Nrf2-ARE signaling is important for RCC progression, and its inhibition may increase sensitivity to targeted drugs to provide novel developments for RCC treatment.

Pancreatic cancer biomarker detection by two support vector strategies for recursive feature elimination

AIM

Pancreatic cancer is one of the worst malignant tumors in prognosis. Therefore, to reduce the mortality rate of pancreatic cancer, early diagnosis and prompt treatment are particularly important.

RESULTS

We put forward a new feature-selection method that was used to find clinical markers for pancreatic cancer by combination of Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE) algorithms. As a result, seven differentially expressed genes were predicted as specific biomarkers for pancreatic cancer because of their highest accuracy of classification on cancer and normal samples.

CONCLUSION

Three (MMP7, FOS and A2M) out of the seven predicted gene markers were found to encode proteins secreted into urine, providing potential diagnostic evidences for pancreatic cancer.

Immune associated LncRNAs identify novel prognostic subtypes of renal clear cell carcinoma

Kidney Renal Clear Cell Carcinoma (KIRC) is a significant cause of cancer-related deaths. Here, we aim to identify the LncRNAs associated with the immune system and characterise their clinical utility in KIRC. A total of 504 patients' data was used from TCGA-GDC. In silico correlation analysis identified 143 LncRNAs associated with immune-related genes (r > 0.7, P < 0.05). K-means consensus method clustered KIRC samples in three immune clusters, namely cluster C1, C2, and C3 based on the expression of 143 immune-related LncRNAs. Kaplan-Meier analysis showed that C3 patients survived significantly worse than the other two clusters (P < 0.0001). A comparison of TCGA miRNA, mRNA cluster with immune cluster showed the independence and robustness of immune clusters (HR = 2.02 and P = 2.12 × 10^-8 ). The GSEA and CIBERSORT analysis showed high enrichment of poorly activated T-cells in C3 patients. To define LncRNA immune prognostic signature, we randomly divided the TCGA sample into discovery and validation sets. By utilising multivariate Cox regression analysis, we identified and validated a seven LncRNA immune prognostic signature score (LIPS score) (HR = 1.43 and P = 2.73 × 10^-6 ) in KIRC. Comparison of LIPS score with all the clinical factors validated its independence and superiority in KIRC prognosis. In summary, we identified LncRNAs associated with the immune system and showed the presence of prognostic subtypes of KIRC patients based on immune-related LncRNA expression. We also identified a novel immune LncRNA based gene-signature for KIRC patients' prognostication.

Identification of a novel cell cycle-related gene signature predicting survival in patients with gastric cancer

Gastric cancer (GC) is one of the most fatal cancers in the world. Thousands of biomarkers have been explored that might be related to survival and prognosis via database mining. However, the prediction effect of single gene biomarkers is not specific enough. Increasing evidence suggests that gene signatures are emerging as a possible better alternative. We aimed to develop a novel gene signature to improve the prognosis prediction of GC. Using the messenger RNA (mRNA)-mining approach, we performed mRNA expression profiling in a large GC cohort (n = 375) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and we recovered genes related to the G2/M checkpoint, which we identified with a Cox proportional regression model. We identified a set of five genes (MARCKS, CCNF, MAPK14, INCENP, and CHAF1A), which were significantly associated with overall survival (OS) in the test series. Based on this five-gene signature, the test series patients could be classified into high-risk or low-risk subgroups. Multivariate Cox regression analysis indicated that the prognostic power of this five-gene signature was independent of clinical features. In conclusion, we developed a five-gene signature related to the cell cycle that can predict survival for GC. Our findings provide novel insight that is useful for understanding cell cycle mechanisms and for identifying patients with GC with poor prognoses.