Prognostic value of a nine-gene signature in glioma patients based on mRNA expression profiling

Gene features of tumor-specific T cells relevant to immunotherapy, targeted therapy and chemotherapy in lung cancer

1 Background

In lung cancer, the use of small-molecule inhibitors, chemotherapy and immunotherapy has led to unprecedented survival benefits in selected patients. Considering most patients will experience a relapse within a short period of time due to single drug resistance, combination therapy is also particularly important to improve patient prognosis. Therefore, more robust biomarkers to predict responses to immunotherapy, targeted therapy, chemotherapy and rationally drug combination therapies may be helpful in clinical treatment choices.

2 Methods

We defined tumor-specific T cells (TSTs) and their features (TSTGs) by single-cell RNA sequencing. We applied LASSO regression to filter out the most survival-relevant TSTGs to form the Tumor-specific T cell score (TSTS). Immunological characteristics, enriched pathways, and mutation were evaluated in high- and low TSTS groups.

3 Results

We identified six clusters of T cells as TSTs in lung cancer, and four most robust genes from 9 feature genes expressed only on tumor-specific T cells were screened to construct a tumor-specific T cells score (TSTS). TSTS was positively correlated with immune infiltration and angiogenesis and negatively correlated with malignant cell proliferation. Moreover, potential vascular-immune crosstalk in lung cancer provides the theoretical basis for combined anti-angiogenic and immunotherapy. Noticeable, patients in high TSTS had better response to ICB and targeted therapy and patients in the low TSTS group often benefit from chemotherapy.

4 Conclusion

The proposed TSTS is a promising indicator to predict immunotherapy, targeted therapy and chemotherapy responses in lung cancer patients for helping clinical treatment choices.

Tubulin alpha-1b chain was identified as a prognosis and immune biomarker in pan-cancer combing with experimental validation in breast cancer

The α-tubulin subtype, Tubulin α-1b chain (TUBA1B), has been shown to influence immune cell infiltration, cancer growth, and survival across various malignancies. However, a comprehensive study has not yet been undertaken examining the immunological and predictive effects of TUBA1B in a pan-carcinoma context. Using data from TCGA, GEO, and other databases, we analyzed TUBA1B expression across various carcinoma types using transcriptional profiling, prognostic implications, genetic and epigenetic alterations, methylation patterns, and immunological significance. To validate our findings, we conducted Western blot analysis to assess TUBA1B protein levels in matched breast cancer tissue samples and performed CCK-8 proliferation assay, flow cytometry, transwell invasion, and migration assays to comprehensively examine the functional impact of TUBA1B on breast cancer cells. Our pan-cancer analysis found TUBA1B upregulation across most tumor types, with varying expression patterns in distinct immune and molecular subtypes. High TUBA1B expression was an independent risk factor and associated with poor prognoses in several cancers, including BRCA, KICH, LGG, LUAD, and MESO. TUBA1B also demonstrates moderate to high diagnostic accuracy in most tumor types. Increased m6A methylation levels were observed in the TUBA1B gene, while its promoter region displayed low methylation levels. TUBA1B's expression impacted some cancers by elevating tumor mutation burden, microsatellite instability, neoantigen formation, immune cell infiltration, and the modulation of immune checkpoints. Functional enrichment analysis highlights TUBA1B's involvement in important cellular processes such as the cell cycle, p53 signaling, cell senescence, programmed cell death, and the regulation of immune-related pathways. Moreover, our study reveals higher TUBA1B protein expression in breast cancer tissues compared to adjacent tissues. In vitro experiments confirm that TUBA1B deletion reduces breast cancer cell proliferation, invasion, and migration while increasing apoptosis. In conclusion, our study suggests that TUBA1B could potentially serve as a diagnostic marker for predicting cancer immunological profiles and survival outcomes and shed light on the expression and role of TUBA1B in breast cancer, providing a solid foundation for considering it as a promising therapeutic target for breast cancer patient treatment.

A guide to selecting high-performing antibodies for Rab1A and Rab1B for use in Western Blot, immunoprecipitation and immunofluorescence

Rab1 is a highly conserved small GTPase that exists in humans as two isoforms: Rab1A and Rab1B, sharing 92% sequence identity. These proteins regulate vesicle trafficking between the endoplasmic reticulum (ER) and Golgi and within the Golgi stacks. Rab1A and Rab1B may be oncogenes, as they are frequently dysregulated in various human cancers. Moreover, they contribute to the progression of Parkinson's disease. The availability of high-quality antibodies specific for Rab1A or Rab1B is essential to understand the distinct functions of these Rab1 proteins in both health and diseaseand to enhance the reproducibility of research involving these proteins. In this study, we characterized seven antibodies targeting Rab1A and five antibodies targeting Rab1B for Western Blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. These studies are part of a much larger, collaborative initiative seeking to address the antibody reproducibility issue by characterizing commercially available antibodies for human proteins and publishing the results openly as a valuable resource for the scientific community. While uses of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies for their specific needs.

Identification of DNA Damage Repair Gene Signature as a Novel Prognostic Marker in Glioblastoma Multiforme

BACKGROUND

The efficacy of treatment of glioblastoma multiforme (GBM) is limited. The effect of DNA damage repair is an important factor.

METHODS

Expression data were downloaded from The Cancer Genome Atlas (training dataset) and the Gene Expression Omnibus (validation dataset) databases. Univariate Cox regression analysis and the least absolute shrinkage and selection operator were used to construct a DNA damage response (DDR) gene signature. Receiver operating characteristic curve analysis and Kaplan-Meier curve analysis were used to estimate the prognostic value of the risk signature. Moreover, consensus clustering analysis was used to investigate the potential subtypes of GBM according to DDR expression.

RESULTS

We constructed a 3-DDR-related gene signature through the survival analysis. The Kaplan-Meier curve analysis suggested that patients in the low-risk group have significantly better survival outcomes compared with the high-risk group in the training and external validation datasets. The results from the receiver operating characteristic curve analysis indicated that the risk model has high prognostic value in the training and external validation datasets. Moreover, 3 stable molecular subtypes were identified and validated in the Gene Expression Omnibus and The Cancer Genome Atlas databases according to the expression of the DNA repair gene. The microenvironment and immunity of GBM were further investigated and showed that cluster 2 had higher immunity and a higher immune score compared with clusters 1 and 3.

CONCLUSIONS

The DNA damage repair-related gene signature was an independent and powerful prognostic biomarker in GBM. Knowledge of the GBM subtypes could have important implications in the subclassification of GBM.

Deep learning algorithm reveals two prognostic subtypes in patients with gliomas

Background

Gliomas are highly complex and heterogeneous tumors, rendering prognosis prediction challenging. The advent of deep learning algorithms and the accessibility of multi-omic data represent a new approach for the identification of survival-sensitive subtypes. Herein, an autoencoder-based approach was used to identify two survival-sensitive subtypes using RNA sequencing (RNA-seq) and DNA methylation (DNAm) data from The Cancer Genome Atlas (TCGA) dataset. The subtypes were used as labels to build a support vector machine model with cross-validation. We validated the robustness of the model on Chinese Glioma Genome Atlas (CGGA) dataset. DNAm-driven genes were identified by integrating DNAm and gene expression profiling analyses using the R MethylMix package and carried out for further enrichment analysis.

Results

For TCGA dataset, the model produced a high C-index (0.92 ± 0.02), low brier score (0.16 ± 0.02), and significant log-rank p value (p < 0.0001). The model also had a decent performance for CGGA dataset (CGGA DNAm: C-index of 0.70, brier score of 0.21; CGGA RNA-seq: C-index of 0.79, brier score of 0.18). Moreover, we identified 389 DNAm-driven genes of survival-sensitive subtypes, which were significantly enriched in the glutathione metabolism pathway.

Conclusions

Our study identified two survival-sensitive subtypes of glioma and provided insights into the molecular mechanisms underlying glioma development; thus, potentially providing a new target for the prognostic prediction of gliomas and supporting personalized treatment strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04970-x.

Identification of Four Hub Genes as Promising Biomarkers for the Evaluation of Ovarian Cancer Prognosis

Ovarian malignant growth is perhaps the most lethal disease in females. There are no exact biomarkers for the early determination of ovarian disease. We obtained a total of 12 gene clusters through WGCNA and studied the azure gene modules related to the lymphatic infiltration of ovarian cancer further. What’s more, endurance investigation was utilized to decide three qualities connected with the by and large and infection-free endurance in ovarian disease patients, including GOGA8B [Hazard Ratio (HR)=1.53, p=0.037, 95% CI: 1.05–2.24], LRRC26 (HR=0.7, p =0.045, 95% CI: 0.48–1.01), and CCDC114 (HR = 0.72, p = 0.042, 95% CI: 0.53–0.98). A prognostic risk score model was built to anticipate the endurance pace of patients at 1, 3, and 5 years, individually. The area under the receiver operating characteristic (ROC) curve (AUC) of the training set was 0.749, 0.764, and 0.784, respectively; the test AUC was 0.601, 0.623, and 0.709. Our review gives a point of view on significant possible biomarkers for the determination, anticipation, and therapy of ovarian malignant growth.

Identification of an immune‐related gene signature as a prognostic target and the immune microenvironment for adrenocortical carcinoma

Background

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy. Even with complete tumor resection and adjuvant therapies, the prognosis of patients with ACC remains unsatisfactory. In the microtumor environment, the impact of a disordered immune system and abnormal immune responses is enormous. To improve treatment, novel prognostic predictors and treatment targets for ACC need to be identified. Hence, credible prognostic biomarkers of immune‐associated genes (IRGs) should be explored and developed.

Material and methods

We downloaded RNA‐sequencing data and clinical data from The Cancer Genome Atlas (TCGA) data set, Genotype‐Tissue Expression data set, and Gene Expression Omnibus data set. Gene set enrichment analysis (GSEA) was applied to reveal the potential functions of differentially expressed genes.

Results

GSEA indicated an association between ACC and immune‐related functions. We obtained 332 IRGs and constructed a prognostic signature on the strength of 3 IRGs (INHBA, HELLS, and HDAC4) in the training cohort. The high‐risk group had significantly poorer overall survival than the low‐risk group (p < .001). Multivariate Cox regression was performed with the signature as an independent prognostic indicator for ACC. The testing cohort and the entire TCGA ACC cohort were utilized to validate these findings. Moreover, external validation was conducted in the GSE10927 and GSE19750 cohorts. The tumor‐infiltrating immune cells analysis indicated that the quantity of T cells, natural killer cells, macrophage cells, myeloid dendritic cells, and mast cells in the immune microenvironment differed between the low‐risk and high‐risk groups.

Conclusion

Our three‐IRG prognostic signature and the three IRGs can be used as prognostic indicators and potential immunotherapeutic targets for ACC. Inhibitors of the three novel IRGs might activate immune cells and play a synergistic role in combination therapy with immunotherapy for ACC in the future.

Rab1A promotes IL-4R/JAK1/STAT6-dependent metastasis and determines JAK1 inhibitor sensitivity in non-small cell lung cancer

Rab1A overexpression has been observed in several cancer types, however, its significance and the underlying mechanisms in non-small cell lung cancer (NSCLC) remain largely unexplored. This study demonstrated that Rab1A overexpression in NSCLC was significantly correlated to short survival and metastasis. Rab1A overexpression promoted cancer cell migration, invasion, and metastasis both in vitro and in vivo, by activating JAK1/STAT6 signaling through stabilizing IL-4Rα protein. Strikingly, high Rab1A level was associated with sensitivity to JAK1 inhibitor, and Rab1A overexpression rendered cancer cells vulnerable to JAK1-targeted agents. JAK1 inhibitor, Itacitinib adipate, dramatically inhibited high Rab1A NSCLC metastasis, in both cell line and patient derived xenograft models. Collectively, these findings demonstrated that Rab1A plays a critical role in the aggressive properties of NSCLC, revealing a unique mechanism by which it promotes metastasis. In addition, we found that Rab1A is a determinant of JAK1 inhibitor sensitivity, which could be explored for improving JAK1-targeted cancer therapy.

An immune-associated ten-long noncoding RNA signature for predicting overall survival in cervical cancer

BACKGROUND

Several immune-associated long non-coding RNA (lncRNA) signatures have been reported as prognostic models in different types of cancers; however, the immune-associated lncRNA signature for predicting overall survival (OS) in cervical cancer is unknown.

METHODS

The lncRNA expression profiles and clinical data of cervical cancer were acquired from The Cancer Genome Atlas (TCGA) dataset. Immune-associated genes were extracted from the Molecular Signatures Database (MSigDB), and the immune-associated lncRNAs were extracted for Cox regression analysis. Principal component analysis (PCA) was used to distinguish the high and low risk status of cervical cancer patients. Gene Set Enrichment Analysis (GSEA) was used for functional analyses.

RESULTS

Cox regression analyses and the least absolute shrinkage and selection operator (LASSO) Cox regression model were used to construct an immune-associated ten-lncRNA signature (containing AL021807.1, AL109976.1, LINC02446, MIR4458HG, AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) for predicting OS in cervical cancer. The signature segregated the cervical cancer patients into 2 groups (high-risk group and low-risk group). The Kaplan-Meier survival curves of AL021807.1, AL109976.1, LINC02446, and MIR4458HG were statistically significant (P<0.05) and the others (including AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) were not statistically significant (P>0.05). The Kaplan-Meier survival curves of the signature were statistically significant (P=1.134e-10), and the 5-year survival rate was 0.444 in the high-risk group [95% confidence interval (CI): 0.334 to 0.590] and 0.884 in the low-risk group (95% CI: 0.807 to 0.969). The area under curve (AUC) of the receiver operating characteristic (ROC) curve of the signature was 0.833. The concordance index (C-index) of the signature was 0.788 (95% CI: 0.730 to 0.846, P=1.884778e-22). The PCA successfully distinguished the high-risk group and low-risk group based on the signature. The GSEA showed that the signature-related protein coding genes (PCGs) may participate in immunologic biological processes and pathways.

CONCLUSIONS

This study revealed that the immune-associated ten-lncRNA signature is an independent factor for cervical cancer prognosis prediction, providing a bright future for immunotherapy of cervical cancer patients.

Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer

Background

The effective treatment and prognosis prediction of bladder cancer (BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis is closely related to tumour occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified an FRG signature with potential prognostic value for patients with BLCA.

Methods

The corresponding clinical data and mRNA expression profiles of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, and a Cox regression model was used to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation.

Results

Clinical traits were combined with FRGs, and 15 prognosis-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD was related to poor survival in BLCA patients. Multivariate Cox regression was used to construct a prognostic model with 7 FRGs that divided patients into two risk groups. Compared with that in the low-risk group, the overall survival (OS) of patients in the high-risk group was significantly lower (P < 0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR = 1.772, P < 0.01). Receiver operating characteristic (ROC) curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in ssGSEA and different distributed patterns in PCA.

Conclusion

Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08687-7.

Multi-Omics Profiling Identifies Risk Hypoxia-Related Signatures for Ovarian Cancer Prognosis

Background

Ovarian cancer (OC) has the highest mortality rate among gynecologic malignancy. Hypoxia is a driver of the malignant progression in OC, which results in poor prognosis. We herein aimed to develop a validated model that was based on the hypoxia genes to systematically evaluate its prognosis in tumor immune microenvironment (TIM).

Results

We identified 395 hypoxia-immune genes using weighted gene co-expression network analysis (WGCNA). We then established a nine hypoxia-related genes risk model using least absolute shrinkage and selection operator (LASSO) Cox regression, which efficiently distinguished high-risk patients from low-risk ones. We found that high-risk patients were significantly related to poor prognosis. The high-risk group showed unique immunosuppressive microenvironment, lower antigen presentation, and higher levels of inhibitory cytokines. There were also significant differences in somatic copy number alterations (SCNAs) and mutations between the high- and low-risk groups, indicating immune escape in the high-risk group. Tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms showed that low-risk patients are significantly responsive to programmed cell death protein-1 (PD-1) inhibitors.

Conclusions

In this study, we highlighted the clinical significance of hypoxia in OC and established a hypoxia-related model for predicting prognosis and providing potential immunotherapy strategies.

A review of gliomas-related proteins. Characteristics of potential biomarkers

Brain tumors are one of the most commonly diagnosed cancers of the central nervous system. Of all diagnosed malignant tumors, 80% are gliomas. An unequivocal diagnosis of gliomas is not always simple, and there is a great need for research to find new treatment options and diagnostic approaches. This paper is focused on the glioma-related protein profiles as compared to healthy brain tissue, which is reflected in multiple correlations between biological aspects that influence proliferation, apoptosis evasion and the invasiveness of neoplastic cells. The work presents the possibilities of facilitating clinical practice with proteomic biomarkers, which offer a wider diagnostic spectrum and reduce the margin of mistake in histopathological or imaging diagnostic methods. In fact, many changes in the body's homeostasis can be overlooked due to the lack of symptoms or their non-specificity. Nevertheless, a single marker has limited reliability in distinguishing a particular tumor subtype, since the increased or decreased level of the protein of interest may differ between the stages or locations of the tumor. Moreover, the correlations between proposed proteins - presented in this paper - may help clinicians to choose the most optimal therapy, and estimate its effectiveness, or indicate new therapeutic targets affecting disrupted biochemical pathways.

Bioinformatics Profiling of Five Immune-Related lncRNAs for a Prognostic Model of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), one of the most common tumors worldwide, has the fifth highest mortality rate, which is increasing every year. At present, many studies have revealed that immunotherapy has an important effect on many malignant tumors. The main purpose of our research was to verify and establish a new immune-related lncRNA model and to explore the potential immune mechanisms. We analysed the pathways and mechanisms of immune-related lncRNAs by bioinformatics analysis, screened key lncRNAs based on Cox regression analysis, and determined the characteristics of the immune-related lncRNAs. On this basis, a predictive model was established. Through a comparison of specificity and sensitivity, we found that the constructed model was superior to the known markers of HCC. Then, the cell types were identified by the relative subgroup (CIBERSORT) algorithm for RNA transcripts. A signature model was eventually constructed, and we proved that it was a survival factor for HCC. Moreover, five kinds of immune cells were significantly positively correlated with the signature. The results indicated that these five kinds of lncRNAs may be related to the immune infiltration of hepatocellular carcinoma. To verify these findings, we selected the top coexpressed lncRNA, AC099850.3, for further study. We found that AC099850.3 could promote the migration and proliferation of hepatocellular carcinoma cells in vitro. RT-PCR experiments found that AC099850.3 could promote the expression of the cell cycle molecules BUB1, CDK1, PLK1, and TTK, and western blotting to prove that the expression of the molecules CD155 and PD-L1 was inhibited in the interference group. In conclusion, we used five kinds of immune-related lncRNAs to construct prognostic signatures to explore the mechanism, which provides a new way to study therapies for HCC.

Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer

Ovarian cancer (OC) is one of the most lethal gynecologic malignant tumors. The interaction between autophagy and the tumor immune microenvironment has clinical importance. Hence, it is necessary to explore reliable biomarkers associated with autophagy-related genes (ARGs) for risk stratification in OC. Here, we obtained ARGs from the MSigDB database and downloaded the expression profile of OC from TCGA database. The k-means unsupervised clustering method was used for clustering, and two subclasses of OC (cluster A and cluster B) were identified. SsGSEA method was used to quantify the levels of infiltration of 24 subtypes of immune cells. Metascape and GSEA were performed to reveal the differential gene enrichment in signaling pathways and cellular processes of the subtypes. We found that patients in cluster A were significantly associated with higher immune infiltration and immune-associated signaling pathways. Then, we established a risk model by LASSO Cox regression. ROC analysis and Kaplan-Meier analysis were applied for evaluating the efficiency of the risk signature, patients with low-risk got better outcomes than those with high-risk in overall survival. Finally, ULK2 and GABARAPL1 expression was further validated in clinical samples. In conclusion, Our study constructed an autophagy-related prognostic indicator, and identified two promising targets in OC.

Identification of Novel Gene Signature Associated with Cell Glycolysis to Predict Survival in Hepatocellular Carcinoma Patients

Purpose

As hepatocellular carcinoma (HCC) is a complex disease, it is hard to classify HCC with a specific biomarker. This study used data from TCGA to create a genetic signature for predicting the prognosis of HCC patients.

Methods

In a group of HCC patients (n = 424) from TCGA, mRNA profiling was carried out. To recognize gene sets that differed significantly between HCC and normal tissues, an enrichment study of genes was carried out. Cox relative hazard regression models have been used to identify genes that are significantly associated with overall survival. To test the function of a prognostic risk parameter, the following multivariate Cox regression analysis was used. The log-rank test and Kaplan–Meier survival estimates were used to test the significance of risk parameters for predictive prognoses.

Results

Eight genes have been identified as having a significant link to overall survival (PAM, NUP155, GOT2, KDELR3, PKM, NSDHL, ENO1, and SRD5A3). The 377 HCC patients were divided into eight-gene signature-based high/low-risk subgroups. The eight-gene signature's prognostic ability was unaffected by a number of factors.

Conclusion

To predict the survival of patients with HCC, an eight-gene signature associated with cellular glycolysis was then identified. The findings shed light on cellular glycolysis processes and the diagnosis of patients with low HCC prognoses.

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.

Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

Prognostic value of eight immune gene signatures in pancreatic cancer patients

BACKGROUND

Pancreatic cancer is one of the most common malignant tumors of the digestive tract, and it has a poor prognosis. Traditional methods are not effective to accurately assess the prognosis of patients with pancreatic cancer. Immunotherapy is a new promising approach for the treatment of pancreatic cancer; however, some patients do not respond well to immunotherapy, which may be related to tumor microenvironment regulation. In this study, we use gene expression database to mine important immune genes and establish a prognostic prediction model for pancreatic cancer patients. We hope to provide a feasible method to evaluate the prognosis of pancreatic cancer and provide valuable targets for pancreatic cancer immunotherapy.

RESULTS

We used univariate COX proportional hazard regression analysis, the least absolute shrinkage and selection operator, and multivariate COX regression analysis to screen 8 genes related to prognosis from the 314 immune-related genes, and used them to construct a new clinical prediction model in the TCGA pancreatic cancer cohort. Subsequently, we evaluated the prognostic value of the model. The Kaplan-Meier cumulative curve showed that patients with low risk scores survived significantly longer than patients with high risk scores. The area under the ROC curve (AUC value) of the risk score was 0.755. The univariate COX analysis showed that the risk score was significantly related to overall survival (HR 1.406, 95% CI 1.237-1.598, P < 0.001), and multivariate analysis showed that the risk score was an independent prognostic factor (HR 1.400, 95% CI 1.287-1.522, P < 0.001). Correlation analysis found that immune genes are closely related to tumor immune microenvironment.

CONCLUSIONS

Based on the TCGA-PAAD cohort, we identified immune-related markers with independent prognostic significance, validated, and analyzed their biological functions, to provide a feasible method for the prognosis of pancreatic cancer and provide potentially valuable targets for pancreatic cancer immunotherapy.

Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma

Glioblastoma (GBM) is the most aggressive malignant brain tumour, with high morbidity and mortality rates. Currently, there is a lack of systematic and comprehensive analysis on the prognostic significance of alternative splicing (AS) profiling for GBM. The GBM data, including RNA-sequencing, corresponding clinical information and the expression levels of splicing factor genes, were downloaded from The Cancer Genome Atlas and the SpliceAid2 database. The prognostic models were assessed by the least absolute shrinkage and selection operator Cox regression analysis. The correlation network between survival-associated AS events and splicing factors was plotted. Prognostic models were built for every AS event type and performed well for risk stratification in patients with GBM. The final prognostic signature served as an independent prognostic factor [hazard ratio (HR), 4.61; 95% confidence interval (CI), 2.97-7.16; P=9.66×10-12] for several clinical parameters, including age, sex, isocitrate dehydrogenase mutation, O6-methylguanine-DNA methyltransferase promoter methylation and risk score. The HR for risk score with GBM was 1.0063 (95% CI, 1.0024-1.0103). The splicing regulatory network indicated that heat shock protein b-1, protein arginine N-methyltransferase 5, protein FAM50B and endoplasmic reticulum chaperone BiP genes were independent prognostic factors for GBM. The results of the present study support the ongoing effort in developing novel genomic models and providing potentially more effective treatment options for patients with GBM.

Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility

Genome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n = 1194 and whole blood, n = 31,684) and glioma subtype (all glioma (7400 cases, 8257 controls) glioblastoma (GBM, 3112 cases) and non-GBM gliomas (2411 cases)). We also leveraged tissue-specific eQTLs collected from 13 brain tissues (n = 114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk.

An mRNA characterization model predicting survival in patients with invasive breast cancer based on The Cancer Genome Atlas database

BACKGROUND

Invasive breast cancer is a highly heterogeneous tumor, although there have been many prediction methods for invasive breast cancer risk prediction, the prediction effect is not satisfactory. There is an urgent need to develop a more accurate method to predict the prognosis of patients with invasive breast cancer.

OBJECTIVE

To identify potential mRNAs and construct risk prediction models for invasive breast cancer based on bioinformaticsMETHODS: In this study, we investigated the differences in mRNA expression profiles between invasive breast cancer and normal breast samples, and constructed a risk model for the prediction of prognosis of invasive breast cancer with univariate and multivariate Cox analyses.

RESULTS

We constructed a risk model comprising 8 mRNAs (PAX7, ZIC2, APOA5, TP53AIP1,MYBPH, USP41, DACT2, and POU3F2) for the prediction of invasive breast cancer prognosis. We used the 8-mRNA risk prediction model to divide 1076 samples into high-risk groups and low-risk groups, the Kaplan-Meier curve showed that the high-risk group was closely related to the poor prognosis of overall survival in patients with invasive breast cancer. The receiver operating characteristic curve revealed an area under the curve of 0.773 for the 8 mRNA model at 3-year overall survival, indicating that this model showed good specificity and sensitivity for prediction of prognosis of invasive breast cancer.

CONCLUSIONS

The study provides an effective bioinformatic analysis for the better understanding of the molecular pathogenesis and prognosis risk assessment of invasive breast cancer.

A glycolysis-related gene signature predicts prognosis of patients with esophageal adenocarcinoma

Background: Esophageal adenocarcinoma (EAC) is a growing problem with a rapidly rising incidence and carries a poor prognosis. We aimed to develop a glycolysis-related gene signature to predict the prognostic outcome of patients with EAC.

Results: Five genes (CLDN9, GFPT1, HMMR, RARS and STMN1) were correlated with prognosis of EAC patients. Patients were classified into high-risk and low-risk groups calculated by Cox regression analysis, based on the five gene signature risk score. The five-gene signature was an independent biomarker for prognosis and patients with low risk scores showed better prognosis. Nomogram incorporating the gene signature and clinical prognostic factors was effective in predicting the overall survival.

Conclusion: An innovative identified glycolysis-related gene signature and an effective nomogram reliably predicted the prognosis of EAC patients.

Methods: The Cancer Genome Atlas database was investigated for the gene expression profile of EAC patients. Glycolytic gene sets difference between EAC and normal tissues were identified via Gene set enrichment analysis (GSEA). Univariate and multivariate Cox analysis were utilized to construct a prognostic gene signature. The signature was evaluated by receiver operating characteristic curves and Kaplan–Meier curves. A prognosis model integrating clinical parameters with the gene signature was established with nomogram.

Identification and verification of an immune-related lncRNA signature for predicting the prognosis of patients with bladder cancer

BACKGROUND

Recent studies have revealed the significant roles of immune-related long noncoding RNAs (lncRNAs) in cancer development and progression. The identification of biomarkers that contribute to early detection and risk stratification provides significant benefits for bladder cancer (BC) patients. The current study aimed to determine an immune-related lncRNA signature for predicting the prognosis of BC patients.

METHODS

Based on The Cancer Genome Atlas (TCGA) database, we identified seven immune-related lncRNAs with prognostic value. The predictive value of the prognostic signature developed from immune-related lncRNAs was assessed by survival and nomogram analyses. Principal component analysis (PCA) was performed to visualize gene expression patterns in the groups defined by the risk score, and the immune composition and purity of the tumor were evaluated by the ESTIMATE algorithm.

RESULTS

Based on the Pearson correlation analysis results, 765 immune-related lncRNAs were filtered (|R| > 0.4, P < 0.001), and seven immune-related lncRNAs (Z84484.1, AC009120.2, AL450384.2, AC024060.1, TNFRSF14-AS1, AL354919.2, OCIAD1-AS1) with prognostic value were finally identified. Patients in the low-risk group had a better prognosis than those in the high-risk group. Multivariate Cox regression analysis showed that the signature was an independent prognostic factor. A prognostic nomogram with clinical features and the signature of seven immune-related lncRNAs was also constructed. According to the PCA and ESTIMATE algorithm results, we found different immune statuses in the low-and high-risk groups.

CONCLUSIONS

Our study shows that the signature of seven immune-related lncRNAs can be used as a prognostic marker for BC.

Comprehensive analysis of prognostic immune-related genes associated with the tumor microenvironment of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a specific tumor immune microenvironment (TIME). Therefore, investigating prognostic immune-related genes (IRGs) that are closely associated with TIME to predict PDAC clinical outcomes is necessary. In the present study, 459 samples of PDAC from the Genotype-Tissue Expression database, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) were included and a survival-associated module was identified using weighted gene co-expression network analysis. Based on the Cox regression analysis and least absolute shrinkage and selection operator analysis, four IRGs (2′-5′-oligoadenylate synthetase 1, MET proto-oncogene, receptor tyrosine kinase, interleukin 1 receptor type 2 and interleukin 20 receptor subunit β) were included in the prognostic model to calculate the risk score (RS), and patients with PDAC were divided into high- and low-RS groups. Kaplan-Meier survival and receiver operating characteristic curve analyses demonstrated that the low-RS group had significantly improved survival conditions compared with the high-RS group in TCGA training set. The prognostic function of the model was also validated using ICGC and GEO cohorts. To investigate the mechanism of different overall survival between the high- and low-RS groups, the present study included Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data and Cell Type Identification by Estimating Relative Subset of Known RNA Transcripts algorithms to investigate the state of the tumor microenvironment and immune infiltration inpatients in the cohort from TCGA. In summary, four genes associated with the TIME of PDAC were identified, which may provide a reference for clinical treatment.

Molecular subtyping of glioblastoma based on immune-related genes for prognosis

Glioblastoma (GBM) is associated with an increasing mortality and morbidity and is considered as an aggressive brain tumor. Recently, extensive studies have been carried out to examine the molecular biology of GBM, and the progression of GBM has been suggested to be correlated with the tumor immunophenotype in a variety of studies. Samples in the current study were extracted from the ImmPort and TCGA databases to identify immune-related genes affecting GBM prognosis. A total of 92 immune-related genes displaying a significant correlation with prognosis were mined, and a shrinkage estimate was conducted on them. Among them, the 14 most representative genes showed a marked correlation with patient prognosis, and LASSO and stepwise regression analysis was carried out to further identify the genes for the construction of a predictive GBM prognosis model. Then, samples in training and test cohorts were incorporated into the model and divided to evaluate the efficiency, stability, and accuracy of the model to predict and classify the prognosis of patients and to identify the relevant immune features according to the median value of RiskScore (namely, Risk-H and Risk-L). In addition, the constructed model was able to instruct clinicians in diagnosis and prognosis prediction for various immunophenotypes.

Evidence for Enhanced Exosome Production in Aromatase Inhibitor-Resistant Breast Cancer Cells

Aromatase inhibitors (AIs) represent the standard anti-hormonal therapy for post-menopausal estrogen receptor-positive breast cancer, but their efficacy is limited by the emergence of AI resistance (AI^R). Exosomes act as vehicles to engender cancer progression and drug resistance. The goal of this work was to study exosome contribution in AI^R mechanisms, using estrogen-dependent MCF-7 breast cancer cells as models and MCF-7 LTED (Long-Term Estrogen Deprived) subline, modeling AI^R. We found that exosome secretion was significantly increased in MCF-7 LTED cells compared to MCF-7 cells. MCF-7 LTED cells also exhibited a higher amount of exosomal RNA and proteins than MCF-7 cells. Proteomic analysis revealed significant alterations in the cellular proteome. Indeed, we showed an enrichment of proteins frequently identified in exosomes in MCF-7 LTED cells. The most up-regulated proteins in MCF-7 LTED cells were represented by Rab GTPases, important vesicle transport-regulators in cancer, that are significantly mapped in “small GTPase-mediated signal transduction”, “protein transport” and “vesicle-mediated transport” Gene Ontology categories. Expression of selected Rab GTPases was validated by immunoblotting. Collectively, we evidence, for the first time, that AI^R breast cancer cells display an increased capability to release exosomes, which may be associated with an enhanced Rab GTPase expression. These data provide the rationale for further studies directed at clarifying exosome’s role on endocrine therapy, with the aim to offer relevant markers and druggable therapeutic targets for the management of hormone-resistant breast cancers.

Potential five-mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma

Although the mortality rate of papillary thyroid carcinoma (PTC) is relatively low, the recurrence rates of PTC remain high. The high recurrence rates are related to the difficulties in treatment. Gene expression profiles has provided novel insights into potential therapeutic targets and molecular biomarkers of PTC. The aim of the present study was to identify mRNA signatures which may categorize PTCs into high-and low-risk subgroups and aid with the predictions for prognoses. The mRNA expression profiles of PTC and normal thyroid tissue samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs were identified using the ‘EdgeR’ software package. Gene signatures associated with the overall survival of PTC were selected, and enrichment analysis was performed to explore the biological pathways and functions of the prognostic mRNAs using the Database for Visualization, Annotation and Integration Discovery. A signature model was established to investigate a specific and robust risk stratification for PTC. A total of 1,085 differentially expressed mRNAs were identified between the PTC and normal thyroid tissue samples. Among them, 361 mRNAs were associated with overall survival (P<0.05). A 5-mRNA prognostic signature for PTC (ADRA1B, RIPPLY3, PCOLCE, TEKT1 and SALL3) was identified to classify the patients into high-and low-risk subgroups. These prognostic mRNAs were enriched in Gene Ontology terms such as ‘calcium ion binding’, ‘enzyme inhibitor activity’, ‘carbohydrate binding’, ‘transcriptional activator activity’, ‘RNA polymerase II core promoter proximal region sequence-specific binding’ and ‘glutathione transferase activity’, and Kyoto Encyclopedia of Genes and Genomes signaling pathways such as ‘pertussis’, ‘ascorbate and aldarate metabolism’, ‘systemic lupus erythematosus’, ‘drug metabolism-cytochrome P450 and ‘complement and coagulation cascades’. The 5-mRNA signature model may be useful during consultations with patients with PTC to improve the prediction of their prognosis. In addition, the prognostic signature identified in the present study may reveal novel therapeutic targets for patients with PTC.

Personalized and translational approach for malignant brain tumors in the era of precision medicine: the strategic contribution of an experienced neurosurgery laboratory in a modern neurosurgery and neuro-oncology department

Personalized medicine (PM) aims to optimize patient management, taking into account the individual traits of each patient. The main purpose of PM is to obtain the best response, improving health care and lowering costs. Extending traditional approaches, PM introduces novel patient-specific paradigms from diagnosis to treatment, with greater precision. In neuro-oncology, the concept of PM is well established. Indeed, every neurosurgical intervention for brain tumors has always been highly personalized. In recent years, PM has been introduced in neuro-oncology also to design and prescribe specific therapies for the patient and the patient's tumor. The huge advances in basic and translational research in the fields of genetics, molecular and cellular biology, transcriptomics, proteomics, and metabolomics have led to the introduction of PM into clinical practice. The identification of a patient's individual variation map may allow to design selected therapeutic protocols that ensure successful outcomes and minimize harmful side effects. Thus, clinicians can switch from the "one-size-fits-all" approach to PM, ensuring better patient care and high safety margin. Here, we review emerging trends and the current literature about the development of PM in neuro-oncology, considering the positive impact of innovative advanced researches conducted by a neurosurgical laboratory.

Identification of an immune-related long non-coding RNA signature and nomogram as prognostic target for muscle-invasive bladder cancer

To identify an immune-related prognostic signature based on long non-coding RNAs (lncRNAs) and find immunotherapeutic targets for bladder urothelial carcinoma, we downloaded RNA-sequencing data from The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis demonstrated bladder urothelial carcinoma was related to immune-related functions. We obtained 332 immune-related genes and 262 lncRNAs targeting immune-related genes. We constructed a signature based on eight lncRNAs in training cohort. Patients were classified as high-risk and low-risk according to signature risk score. High-risk patients had poor overall survival compared with low-risk patients (P < 0.001). Multivariate Cox regression suggested the signature was an independent prognostic indicator. The findings were further validated in testing, entire TCGA and external validation cohorts. Gene set enrichment analysis indicated significant enrichment of immune-related phenotype in high-risk group. Immunohistochemistry and online analyses validated the functions of 4 key immune-related genes (LIG1, TBX1, CTSG and CXCL12) in bladder urothelial carcinoma. Nomogram proved to be a good classifier for muscle-invasive bladder cancer through combining the signature. In conclusion, our immune-related prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for muscle-invasive bladder cancer.

Bioinformatics Analysis Finds Immune Gene Markers Related to the Prognosis of Bladder Cancer

Bladder cancer is one of the most common malignant tumors of the urinary system that seriously threatens the health of a population. In recent years, the application of immunotherapy has significantly changed the treatment of bladder cancer, but only some patients can benefit from the treatment with immune-checkpoint inhibitors. Many problems are unsolved in the field of bladder cancer immunotherapy, especially in the search for genes that are critical to the level of immune cell infiltration and new effective therapeutic targets. We attempted to use bioinformatics analysis to identify immune gene markers related to the prognosis of bladder cancer and to establish a prognostic signature for bladder cancer patients based on their immune gene expression profiles. We used univariate Cox proportional hazards regression analysis, the least absolute shrinkage and selection operator (LASSO) Cox regression, and multivariate Cox proportional hazards regression analysis from The Cancer Genome Atlas bladder cancer cohort (TCGA-BLCA). Fifteen genes related to prognosis were screened using the survival analysis, correlation analysis, cancer and adjacent cancer differential expression analysis, and mutation analysis. The potential biological role of these genes was determined using survival analysis and principal component analysis (PCA). The receiver operating characteristic (ROC) curve assesses the prognostic value of the predictive signature. The gene ontology (GO), Kyoto Encyclopedia of Gene and Genome (KEGG), Gene set enrichment analysis (GSEA), and other methods were used to reveal the differential gene enrichment in the signaling pathways and cellular processes of high- and low-risk groups. The single-sample GSEA (ssGSEA) method was used to quantify the infiltration levels of 24 immune cells in the tumor immune microenvironment and these immune genes were found to be closely related to the tumor immune microenvironment. In summary, we screened 15 immune genes that were closely related to bladder cancer overall survival (OS) and may be potential prognostic indicators of bladder cancer. They may have research and clinical application value in bladder cancer immunotherapy. We used 15 immune genes to construct a new immune-related gene signature that was verified and could be helpful in improving individualized prognosis of patients with bladder cancer.

[Effects of RAB1A on the proliferation, invasion, and metastasis of tongue squamous cell carcinoma cells]

OBJECTIVE

This study aimed to investigate the molecular mechanism of RAB1A in the proliferation, invasion, and metastasis of human tongue squamous cell carcinoma.

METHODS

Western blot was used to detect the expression of RAB1A protein in human normal tongue epithelial cells (Hacat) and tongue squamous cell carcinoma Tca8113. The changes in RAB1A after plasmid transfection were also studied. The Tca8113 cells were named SiRAB1A/Tca8113 after RAB1A plasmid transfection. The expression of the epithelial-mesenchymal transition (EMT)-related markers of SiRAB1A/Tca8113 cells was also detected. CCK-8 assay was used to detect the proliferation of SiRAB1A/Tca8113 cells. Transwell and wound healing assays were used to detect the invasive and metastatic abilities of SiRAB1A/Tca8113 cells, respectively.

RESULTS

Western blot results showed that the expression of RAB1A in tongue squamous cell carcinoma cells was significantly higher than that in Hacat. RAB1A decreased significantly after SiRAB1A plasmid transfection. CCK-8 proliferation assay showed that the proliferation of SiRAB1A/Tca8113 cells also decreased significantly. Transwell and wound healing assays demonstrated that the invasive and metastatic abilities of SiRAB1A/Tca8113 cells decreased significantly, respectively. In addition, Western blot results demonstrated that RAB1A deletion significantly increased the expression of E-cadherin and inhibited the expression of Vimentin.

CONCLUSIONS

RAB1A could promote the proliferation, invasion, and metastasis of tongue squamous cell carcinoma cells.

Identification of a Seven-lncRNA Immune Risk Signature and Construction of a Predictive Nomogram for Lung Adenocarcinoma

Background

The incidence of lung cancer is the highest of all cancers, and it has the highest death rate. Lung adenocarcinoma (LUAD) is a major type of lung cancer. This study is aimed at identifying the prognostic value of immune-related long noncoding RNAs (lncRNAs) in LUAD.

Materials and Methods

Gene expression profiles and the corresponding clinicopathological features of LUAD patients were obtained from The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was performed on the prognostic immune-related lncRNAs to calculate the risk scores, and a risk signature was constructed. Survival analysis was performed to assess the prognostic value of the risk signature. A nomogram was also constructed based on the clinicopathological features and risk signature.

Results

A total of 437 LUAD patients with gene expression data and clinicopathological features were obtained in this study, which was considered the combination set. They were randomly and equally divided into a training set and a validation set. Seven immune-related lncRNAs (AC092794.1, AL034397.3, AC069023.1, AP000695.1, AC091057.1, HLA-DQB1-AS1, and HSPC324) were identified and used to construct a risk signature. The patients were divided into the low- and high-risk groups based on the median risk score of -0.04074. Survival analysis suggested that patients in the low-risk group had a longer overall survival (OS) than those in the high-risk group (p = 1.478e − 02). A nomogram was built that could predict the 1-, 3-, and 5-year survival rates of LUAD patients (C-index of the nomogram was 0.755, and the AUCs for the 1-, 3-, and 5-year survivals were 0.826, 0.719, and 0.724, respectively). The validation and combination sets confirmed these results.

Conclusion

Our study identified seven novel immune-related lncRNAs and generated a risk signature, as well as a nomogram, that could predict the prognosis of LUAD patients.

Expression and prognostic value of mRNAs in lower grade glioma with MGMT promoter methylated

Due to the varied overall survival (OS), limited studies focus on the factors that affect the prognosis for lower grade glioma patients (LGGs) with MGMT promoter methylated. A total of 579 samples (TCGA LGGs 456; CGGA LGGs 123) were included to identify potential genes for LGGs with MGMT promoter methylated. All bioinformatics analyses were conducted using SPSS software and GraphPad Prism 6. Based on COX regression analysis, we established a four-gene signature (ALDOC, APOBEC3C, ANXA1 and ARPP21) and divided LGGs into two groups based on median risk score. The OS of LGGs in high risk group was shorter than low risk group (P < 0.0001). Furthermore, the OS in high risk group were shorter than low risk group in Grade II and III, respectively (P = 0.0003; P = 0.0104). It showed that the signature was an independent prognosis factor on multivariate Cox regression analysis (P = 0.033). Patients in high group tended to displayed high grade (GIII), IDH1 wild type and mesenchymal subtype preference. Four-gene signature was discovered for LGGs with MGMT promoter methylated. Our findings suggested that the four genes could serve as prognostic biomarkers.

Identification of N6-methyladenosine-related lncRNAs for patients with primary glioblastoma

To investigate the m⁶a-related long non-coding RNAs (lncRNAs) that may be exploited as potential biomarkers in primary glioblastoma (pGBM), a cohort of 268 glioma samples from GSE16011 dataset was included for discovery. The Chinese Glioma Genome Atlas (CGGA) microarray and RNA sequencing databases were used for validation. Bioinformatic analyses were performed using the R software. The m⁶a-lncRNA co-expression networks were constructed, and four m⁶a-related lncRNAs (MIR9-3HG, LINC00900, MIR155HG, and LINC00515) were identified in pGBM patients on the univariate Cox regression analysis. Patients in the low-risk group had longer overall survival (OS) and progression-free survival (PFS) than those in the high-risk group (P = 0.0025, P = 0.0070). Moreover, the high-risk group displayed older age, isocitrate dehydrogenase (IDH) wild-type, classical and mesenchymal TCGA subtype, and G3 CGGA subtype. Distinct m⁶a status was identified according to histologic grade and two groups (low-risk and high-risk). Functional annotation showed that differentially expressed genes between the two groups were enriched in immune response, apoptosis, cell adhesion, negative regulation of transcription, negative regulation of RNA metabolic process, and regulation of RNA metabolic process. We profiled the m⁶a status in glioma and identified four m⁶a-related prognostic lncRNAs for pGBMs.

Glycolysis gene expression profilings screen for prognostic risk signature of hepatocellular carcinoma

Metabolic changes are the markers of cancer and have attracted wide attention in recent years. One of the main metabolic features of tumor cells is the high level of glycolysis, even if there is oxygen. The transformation and preference of metabolic pathways is usually regulated by specific gene expression. The aim of this study is to develop a glycolysis-related risk signature as a biomarker via four common cancer types. Only hepatocellular carcinoma was shown the strong relationship with glycolysis. The mRNA sequencing and chip data of hepatocellular carcinoma, breast invasive carcinoma, renal clear cell carcinoma, colorectal adenocarcinoma were included in the study. Gene set enrichment analysis was performed, profiling three glycolysis-related gene sets, it revealed genes associated with the biological process. Univariate and multivariate Cox proportional regression models were used to screen out prognostic-related gene signature. We identified six mRNAs (DPYSL4, HOMER1, ABCB6, CENPA, CDK1, STMN1) significantly associated with overall survival in the Cox proportional regression model for hepatocellular carcinoma. Based on this gene signature, we were able to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis showed that prognostic power of this six gene signature is independent of clinical variables. Further, we validated this data in our own 55 paired hepatocellular carcinoma and adjacent tissues. The results showed that these proteins were highly expressed in hepatocellular carcinoma tissues compared with adjacent tissue. The survival time of high-risk group was significantly shorter than that of low-risk group, indicating that high-risk group had poor prognosis. We calculated the correlation coefficients between six proteins and found that these six proteins were independent of each other. In conclusions, we developed a glycolysis-related gene signature that could predict survival in hepatocellular carcinoma patients. Our findings provide novel insight to the mechanisms of glycolysis and it is useful for identifying patients with hepatocellular carcinoma with poor prognoses.

Systematic analysis of differentially methylated expressed genes and site-specific methylation as potential prognostic markers in head and neck cancer

Head and neck cancer (HNC) remains one of the most malignant tumors with a significantly high mortality. DNA methylation exerts a vital role in the prognosis of HNC. In this study, we try to screen abnormal differential methylation genes (DMGs) and pathways in Head-Neck Squamous Cell Carcinoma via integral bioinformatics analysis. Data of gene expression microarrays and gene methylation microarrays were obtained from the Cancer Genome Atlas database. Aberrant DMGs were identified by the R Limma package. We conducted the Cox regression analysis to select the prognostic aberrant DMGs and site-specific methylation. Five aberrant DMGs were recognized that significantly correlated with overall survival. The prognostic model was constructed based on five DMGs (PAX9, STK33, GPR150, INSM1, and EPHX3). The five DMG models acted as prognostic biomarkers for HNC. The area under the curve based on the five DMGs predicting 5-year survival is 0.665. Moreover, the correlation between the DMGs/site-specific methylation and gene expression was also explored. The findings demonstrated that the five DMGs can be used as independent prognostic biomarkers for predicting the prognosis of patients with HNC. Our study might lay the groundwork for further mechanism exploration in HNC and may help identify diagnostic biomarkers for early stage HNC.

Pooled analysis of prognostic value and clinical significance of Rab1A expression in human solid tumors

BACKGROUND

This study aims to assess the relationship between Rab1A expression and clinicopathological parameters and prognosis of patients with human solid cancer by summarizing the studies included.

METHODS

PubMed, EMBASE, The Cochrane Library, and other sources were searched for relative studies. The risk ratios (RRs) and confidence interval (CI) were used to assess association between Rab1A expression and clinical parameters and prognosis in solid cancer patients.

RESULTS

Eight studies were included in the final analysis with 800 patients. The results revealed that expression of Rab1A was significantly related with differentiation (RR = 0.883, 95%CI = 0.782-0.997, P = .044), lymph node metastasis (RR = 0.835, 95%CI = 0.753-0.926, P = .001), tumor-lymph node-metastasis (TNM) stage (RR = 1.190, 95%CI = 1.071-1.322, P < .001) and tumor size (RR = 0.818, 95%CI = 0.730-0.915, P < .001). What is more, no significant difference was seen in 1-year survival between high and low expression of Rab1A in multiple malignancies (RR = 0.855, 95%CI = 0.697-1.050, P = .136). However, increased Rab1A revealed poorer prognosis with 2-year survival (RR = 0.760, 95%CI = 0.701-0.824, P < .001), 3-year survival (RR = 0.669, 95%CI = 0.604-0.742, P < .001), 4-year survival (RR = 0.622, 95%CI = 0.554-0.698, P < .001) and 5-year survival (RR = 0.525, 95%CI = 0.458-0.698, P < .001). Expression of Rab1A was increased obviously in solid cancer tissues compared with the adjacent normal tissue (RR = 4.78, 95%CI 4.05-5.63, P = .015).

CONCLUSION

This study revealed Rab1A expression links closely with tumor size, differentiation, lymph node metastasis, TNM stage and poor prognosis of human solid cancer patients. It may act as a biomarker of prognosis and a novel therapeutic target in solid cancer.

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.

Combined Therapy Sensitivity Index Based on a 13-Gene Signature Predicts Prognosis for IDH Wild-type and MGMT Promoter Unmethylated Glioblastoma Patients

Glioblastoma (GBM) is one of the lethal tumors with poor prognosis. However, prognostic prediction approaches need to be further explored. Therefore, we developed an evaluation system that could be used for prognostic prediction of GBM patients. Published mRNA expression datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) were analyzed. Quantitative Realtime-PCR of signature genes and molecular aberrations of 178 Xiangya GBM patients were used for confirmation. Gene set enrichment analysis (GSEA) was performed for functional annotation. As a result, we established a 13-gene signature which named Combined Therapy Sensitivity Index (CTSI). Based on a cutoff point, we divided patients into high-risk group and low-risk group. Based on Kaplan-Meier analysis and multivariate Cox regression analysis, we found that patients in the high-risk group had a shorter overall survival time than patients in the low-risk group (p<0.001 in TCGA and CGGA datasets, p=0.047 in GSE4271 dataset, p=0.008 in Xiangya GBM cohort, HR: 1.65-3.42). By comparing the status of IDH mutation, TERT promoter mutation (TERTp-mut) and MGMT promoter methylation, CTSI was predictable in IDH wild-type (IDH-wt)/MGMT promoter unmethylated (MGMTp-unmeth) patients (p=0.037 in IDH-wt/TERTp-mut/MGMTp-unmeth subgroup, HR: 1.98; p=0.032 in IDH-wt/TERTp-wt/MGMTp-unmeth subgroup, HR: 2.09). Based on GESA, the Gene Ontology (GO) gene sets were enriched differently between CTSI high-risk and low-risk groups. Our results showed CTSI risk score can predict the prognosis of IDH-wt/MGMTp-unmeth GBM patients. Based on CTSI, combined with the status of IDH mutation, TERT promoter mutation and MGMT promoter methylation, a stepwise prognosis evaluation system which can provide precise prognosis prediction for GBM patients was established.

Identification of a novel DNA repair-related prognostic signature predicting survival of patients with hepatocellular carcinoma

Purpose

Hepatocellular carcinoma (HCC) is the sixth most lethal neoplasm worldwide. Traditional biomarkers often exploit the relationship between a certain gene and cancer progression, but they cannot predict patient survival or prognosis accurately. We aim to construct a new DNA repair-related gene signature that combines several genes to improve prognosis prediction in HCC.

Methods

We selected an HCC mRNA sequencing (mRNA-seq) dataset (n=365) from The Cancer Genome Atlas (TCGA), and gene set enrichment analysis (GSEA) was used to explore bioinformatics information and further screen genes. We then built a gene signature based on the Cox proportional hazards regression model.

Results

GSEA revealed that the hallmark DNA repair gene set was significantly upregulated in the tumor phenotype. A set of seven genes, namely, ADA, FEN1, POLR2G, SAC3D1, SEC61A1, SF3A3, and UPF3B, were significantly a ssociated with overall survival (OS) and used to form a gene signature. The signature risk score was calculated and used to divide patients into high‐ and low‐risk groups. The high-risk group showed worse prognosis (log-rank test p<0.0001). Univariate and multivariate Cox regression analysis showed that the prognostic performance of this risk score signature was robust in different subgroups based on clinicopathological features, with p-values <0.05 (HR=2.38, 95% CI (confidence interval) =1.355–4.184), indicating that it can serve as an independent prognostic indicator.

Conclusion

We developed and identified a seven‐gene signature related to the DNA repair process that can predict survival in HCC. It can be used as an effective classification tool and to guide clinical treatment.

Discovery and validation of methylated-differentially expressed genes in Helicobacter pylori-induced gastric cancer

DNA methylation has an important role in Helicobacter pylori (H. pylori)-induced gastric cancer (GC) processes and development. The aim of this study was to search genome-scale epigenetic modifications for studying pathogenesis of H. pylori-induced GC, and to find factors and powerful signature related to survival and prognosis. In this study, we conducted a comprehensive analysis of DNA methylation and gene expression profiles in the Gene Expression Omnibus (GEO), to identified differentially expressed genes (DEGs) and differentially methylated genes (DMGs). Functional enrichment analysis of the screened genes was performed, and a protein-protein interaction network was constructed. The TCGA DNA methylation databases and 55 H. pylori-infected GC cases of GEO RNA sequencing (GSE62254) were utilized for prognostic value validation of hub genes. Finally, a prognosis-related risk signature was identified by a series of bioinformatics analysis for H. pylori-induced GC patients. Totally, 161 DMGs were identified. Pathway analysis showed that all MDEGs mainly associated with Ras signaling pathway, renal cell carcinoma, mitogen-activated protein kinase signaling pathway. Five hub genes including CACNB2, GNB4, GRIN2A, MEF2C, and PREX1 were screened as independent prognostic factors in H. pylori-induced GC patients. Two-gene (CACNB2 and MEF2C) risk signature was constructed for predicting the overall survival of H. pylori-induced GC patients. Our study indicated possible MDEGs and pathways in H. pylori-induced GC by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of H. pylori-induced GC. Hub genes might serve as aberrantly methylation-based biomarkers for clinical diagnostic and prognostic evaluation of H. pylori-induced GC.

Comprehensive analysis of genes based on chr1p/19q co-deletion reveals a robust 4-gene prognostic signature for lower grade glioma

Purpose: The chr1p/19q co-deletion is a favorable prognostic factor in patients with lower grade glioma. The aim of this study was to reveal key genes for prognosis and establish prognostic gene signatures based on genes encoded by chr1p/19q. Materials and methods: The data was downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between lower grade glioma tissue and normal brain were identified. The univariate COX regression, robust likelihood-base survival analysis (rbsurv) and multivariate COX regression analysis were used to establish the 4-gene-signature based on the DEGs. The receiver operating characteristic (ROC) curve and the Kaplan-Mere curve were used to verify the prediction accuracy of the signature. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were also performed to explore the reasons for good prognosis in patients with chr1p/19q deletion. Results: A total of 1346 DEGs were identified between lower grade glioma samples and normal brain samples in GSE16011, including 56 up-regulated mRNAs located on chr1p and 20 up-regulated mRNAs located on chr19q. We established a 4-gene-signature that was significantly associated with survival based on the 76 gene. The AUC of the 4-gene-signature for 5-year OS in TCGA and CGGA was 0.837 and 0.876, respectively, which was superior compared to other parameters such as chr1p/19q co-deletion, IDH mutant, WHO grade and histology type, especially in chr1p/19q non-co-deletion patients. GSEA and KEGG analysis suggested that the prolongation of chr1p/19q in patients could be associated with cell cycle and DNA mismatch repairing. Conclusions: We established a robust 4-gene-signature based on the chr1p/19q and we explored the potential function of these newly identified survival-associated genes by bioinformatics analysis. The 4-gene from the signature are promising molecular targets to be used in the future.

Survivin is a prognostic indicator in glioblastoma and may be a target of microRNA-218

Accumulating evidence has revealed that survivin expression is associated with a malignant phenotype and poor prognosis in glioma. Survivin is also a potential target of microRNA (miRNA/miR)-218. The aim of the present study was to investigate the expression and function of survivin in glioblastoma, and to examine the association between survivin and miR-218. For that purpose, survivin mRNA levels were analyzed in 144 frozen samples of glioblastoma using whole-genome RNA sequencing. In vitro cell proliferation, migration, invasion and apoptosis assays were performed, and survivin expression was detected by western blotting. The results revealed that the mRNA expression levels of survivin were negatively and significantly associated with overall survival in glioblastoma. Further in vitro analyses suggested that miR-218 may inhibit the expression of survivin. Expression of miR-218 in the LN229 cell line was significantly lower than that in the immortalized human gliocyte HEB cell line. miR-218 markedly inhibited tumor cell proliferation, migration and invasion capacities, and decreased apoptosis. miR-218 also inhibited the expression of survivin. These results indicated that survivin may be a target of miR-218 and could serve as a predictive biomarker.

Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma

Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor. The median survival of GBM patients is 12–15 months, and the 5 years survival rate is less than 5%. More novel molecular biomarkers are still urgently required to elucidate the mechanisms or improve the prognosis of GBM. This study aimed to explore novel biomarkers for GBM prognosis prediction. The gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets of GBM were downloaded. A total of 2241 overlapping differentially expressed genes (DEGs) were identified from TCGA and GSE7696 datasets. By univariate COX regression survival analysis, 292 survival-related genes were found among these DEGs (p < 0.05). Functional enrichment analysis was performed based on these survival-related genes. A five-gene signature (PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4) was further selected by multivariable Cox regression analysis and a prognostic model of this five-gene signature was constructed. Based on this risk score system, patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Moreover, with the assistance of GEPIA http://gepia.cancer-pku.cn/, all five genes were found to be differentially expressed in GBM tissues compared with normal brain tissues. Furthermore, the co-expression network of the five genes was constructed based on weighted gene co-expression network analysis (WGCNA). Finally, this five-gene signature was further validated in other datasets. In conclusion, our study identified five novel biomarkers that have potential in the prognosis prediction of GBM.

I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment

Crohn’s disease and ulcerative colitis are driven by both common and distinct underlying mechanisms of pathobiology. Both diseases, exhibit heterogeneity underscored by the variable clinical responses to therapeutic interventions.

We aimed to identify disease-driving pathways and classify individuals into subpopulations that differ in their pathobiology and response to treatment.

We applied hierarchical clustering of enrichment scores derived from gene set variation analysis of signatures representative of various immunological processes and activated cell types, to a colonic biopsy dataset that included healthy volunteers, Crohn’s disease and ulcerative colitis patients. Patient stratification at baseline or after anti-TNF treatment in clinical responders and non-responders was queried. Signatures with significantly different enrichment scores were identified using a general linear model. Comparisons to healthy controls were made at baseline in all participants and then separately in responders and non-responders. Fifty-nine percent of the signatures were commonly enriched in both conditions at baseline, supporting the notion of a disease continuum within ulcerative colitis and Crohn’s disease. Signatures included T cells, macrophages, neutrophil activation and poly:IC signatures, representing acute inflammation and a complex mix of potential disease-driving biology. Collectively, identification of significantly enriched signatures allowed establishment of an inflammatory bowel disease molecular activity score which uses biopsy transcriptomics as a surrogate marker to accurately track disease severity. This score separated diseased from healthy samples, enabled discrimination of clinical responders and non-responders at baseline with 100% specificity and 78.8% sensitivity, and was validated in an independent data set that showed comparable classification. Comparing responders and non-responders separately at baseline to controls, 43% and 70% of signatures were enriched, respectively, suggesting greater molecular dysregulation in TNF non-responders at baseline. This methodological approach could facilitate better targeted design of clinical studies to test therapeutics, concentrating on patient subsets sharing similar underlying pathobiology, therefore increasing the likelihood of clinical response.

Patients exhibiting similar phenotypical characteristics, diagnosed with the same disease, exhibit variable response to therapeutics. This is a major health care issue, due to the increased patient suffering and the socioeconomical burden that occurs. Crohn’s disease and ulcerative colitis constitute good examples of inflammatory conditions, with sufferers responding differentially to existent therapeutics. Here, we identified disease-driving pathways and classified individuals into subpopulations that differ in their pathobiology and response to treatment. We utilized gene set variation analysis and transcriptomic data from inflammatory bowel disease sufferers to stratify patients at baseline or after anti-TNF treatment in clinical responders and non-responders. We explored gene signatures obtained from the literature, relevant to immune processes, which were significantly enriched in disease compared to healthy controls, as well as before and after treatment. Using these signatures, we established an inflammatory bowel disease molecular activity score, which allowed us to separate clinical responders and non-responders at baseline with high specificity and sensitivity. We validated the proposed approach in an independent data set, demonstrating comparable classification. This methodological approach may lead to better targeted design of clinical studies, allowing the selection of patient sharing similar underlying pathobiology, thus increasing the likelihood of clinical response to treatment.

Bioinformatics profiling utilized a nine immune-related long noncoding RNA signature as a prognostic target for pancreatic cancer

PURPOSE

To identify an immune-related long noncoding RNA (lncRNA) signature with potential prognostic value for patients with pancreatic cancer.

METHODS

Pancreatic cancer samples with available clinical information and whole genomic mRNA expression data obtained from The Cancer Genome Atlas (TCGA) were enrolled in our research. The immune score of each sample was calculated according to the expression level of immune-related genes and used to identify the most promising immune-related lncRNAs. According to the risk score developed from screened immune-related lncRNAs, the high- and low-risk groups were separated on the basis of the median risk score. The prediction reliability was further evaluated in the validation set and combination set. Both gene set enrichment analysis (GSEA) and principal component analysis (PCA) were performed for functional annotation, and the microenvironment cell population record was applied to evaluate the immune composition and purity of the tumor.

RESULTS

A cohort of 176 samples was included in this study. A total of 163 immune-related lncRNAs were collected according to Pearson correlation analyses between immune score and lncRNA expression |R| > 0.5, P < 0.01). Nine immune-related lncRNAs (AL138966.2, AL133520.1, AC142472.1, AC127024.5, AC116913.1, AC083880.1, AC124016.1, AC008443.5, and AC092171.5) with the most significant prognostic values (P < 0.01) were identified. In the training set, it was observed that patients in the low-risk group showed longer overall survival (OS) than those in the high-risk group (P < 0.001); meanwhile, similar results were found in the validation set, combination set and various stratified sets (P < 0.05, P < 0.001, P < 0.05, respectively). Moreover, the signature was identified as an independent prognostic factor and significantly associated with the OS of pancreatic cancer. The area under curve (AUC) of the receiver operating characteristic curve (ROC curve) for the nine lncRNA signature in predicting the 2-year survival rate was 0.703. In addition, the low-risk and high-risk groups displayed different distributed patterns in PCA and different immune statuses in the GSEA. The signature indicated decreased purity of the tumor by implying a lower proportion of cancer cells along with an increasing enrichment of fibroblasts, myeloid dendritic cells, and monocytic lineage cells.

CONCLUSIONS

Our research suggests that the immune-related lncRNA signature possesses latent prognostic value for patients with pancreatic cancer and may provide new information for immunological research and treatment in pancreatic cancer.

Prognostic value of differentially methylated gene profiles in bladder cancer

DNA methylation can regulate gene expression and is pivotal in the occurrence and development of bladder cancer. In this study, we analyzed whole-genome DNA methylation on the basis of data from The Cancer Genome Atlas to select epigenetic biomarkers predictive of survival and further understand the molecular mechanisms underlying methylation patterns in bladder cancer. We identified 540 differentially methylated genes between tumor and normal tissues, including a number of independent prognostic factors based on univariate analysis. Genes (MIR6732, SOWAHC, SERPINI1, OR10W1, OR7G3, AIM1, and ZFAND5) were integrated to establish a risk model for prognostic assessment based on multivariate Cox analysis. The methylation of SOWAHC was negatively correlated with its messenger RNA expression, and together these were significantly correlated with prognosis. This study took advantage of high-throughput data mining to provide new bioinformatics evidence and ideas for further study into the pathogenesis and prognosis of bladder cancer.