A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study

Impacts of Nutlin-3a and exercise on murine double minute 2-enriched glioma treatment

JOURNAL/nrgr/04.03/01300535-202504000-00029/figure1/v/2024-07-06T104127Z/r/image-tiff Recent research has demonstrated the impact of physical activity on the prognosis of glioma patients, with evidence suggesting exercise may reduce mortality risks and aid neural regeneration. The role of the small ubiquitin-like modifier (SUMO) protein, especially post-exercise, in cancer progression, is gaining attention, as are the potential anti-cancer effects of SUMOylation. We used machine learning to create the exercise and SUMO-related gene signature (ESLRS). This signature shows how physical activity might help improve the outlook for low-grade glioma and other cancers. We demonstrated the prognostic and immunotherapeutic significance of ESLRS markers, specifically highlighting how murine double minute 2 (MDM2), a component of the ESLRS, can be targeted by nutlin-3. This underscores the intricate relationship between natural compounds such as nutlin-3 and immune regulation. Using comprehensive CRISPR screening, we validated the effects of specific ESLRS genes on low-grade glioma progression. We also revealed insights into the effectiveness of Nutlin-3a as a potent MDM2 inhibitor through molecular docking and dynamic simulation. Nutlin-3a inhibited glioma cell proliferation and activated the p53 pathway. Its efficacy decreased with MDM2 overexpression, and this was reversed by Nutlin-3a or exercise. Experiments using a low-grade glioma mouse model highlighted the effect of physical activity on oxidative stress and molecular pathway regulation. Notably, both physical exercise and Nutlin-3a administration improved physical function in mice bearing tumors derived from MDM2-overexpressing cells. These results suggest the potential for Nutlin-3a, an MDM2 inhibitor, with physical exercise as a therapeutic approach for glioma management. Our research also supports the use of natural products for therapy and sheds light on the interaction of exercise, natural products, and immune regulation in cancer treatment.

Deciphering KDM8 dysregulation and CpG methylation in hepatocellular carcinoma using multi-omics and machine learning

Aim: This study investigates the altered expression and CpG methylation patterns of histone demethylase KDM8 in hepatocellular carcinoma (HCC), aiming to uncover insights and promising diagnostics biomarkers.Materials & methods: Leveraging TCGA-LIHC multi-omics data, we employed R/Bioconductor libraries and Cytoscape to analyze and construct a gene correlation network, and LASSO regression to develop an HCC-predictive model.Results: In HCC, KDM8 downregulation is correlated with CpGs hypermethylation. Differential gene correlation analysis unveiled a liver carcinoma-associated network marked by increased cell division and compromised liver-specific functions. The LASSO regression identified a highly accurate HCC prediction signature, prominently featuring CpG methylation at cg02871891.Conclusion: Our study uncovers CpG hypermethylation at cg02871891, possibly influencing KDM8 downregulation in HCC, suggesting these as promising biomarkers and targets.

Exploring the prognostic implications of cuproptosis-associated alterations in clear cell renal cell carcinoma via in vitro experiments

This study investigated the impact of novel copper ionophores on the prognosis of clear cell renal cell carcinoma (ccRCC) and the tumor microenvironment (TME). The differential expression of 10 cuproptosis and 40 TME-pathway-related genes were measured in 531 tumor samples and 71 adjacent kidney samples in The Cancer Genome Atlas database. A risk score model was constructed with LASSO cox to predict the prognosis of ccRCC patients. Forest plot and function enrichment were used to study the biological function of the key genes in depth. The study found that the risk score model accurately predicted the prognosis of ccRCC patients. Patients with high scores had higher immune responses with a higher proportion of anti-tumor lymphocytes and a lower proportion of immunosuppressive M2-like macrophages. However, the high-score group also exhibited a higher proportion of T follicular helper cells and regulatory T cells. These results suggest that cuproptosis-based therapy may be worth further investigation for the treatment of ccRCC and TME. Subsequently, by using RNAi, we established the stable depletion models of FDX1 and PDHB in ccRCC cell lines 786-O and ACHN. Through CCK8, colony formation, and Transwell assays, we observed that the knockdown of FDX1 and PDHB could significantly reduce the capabilities of proliferation and migration in ccRCC cells. In conclusion, this study illuminates the potential effectiveness of copper ionophores in the treatment of ccRCC, with higher risk scores correlating with better TME immune responses. It sets the stage for future cuproptosis-based therapy research in ccRCC and other cancers, focusing on copper's role in TME.

A Cuproptosis-Related LncRNA Risk Model for Predicting Prognosis and Immunotherapeutic Efficacy in Patients with Hepatocellular Carcinoma

Cuproptosis is a novel programmed cell death pathway that is initiated by direct binding of copper to lipoylated tricarboxylic acid (TCA) cycle proteins. Recent studies have demonstrated that cuproptosis-related genes regulate tumorigenesis. However, the potential role and clinical significance of cuproptosis-related long noncoding RNAs (lncRNAs) in hepatocellular carcinoma (HCC) have not been established. We performed a bioinformatics analyses of RNA-sequencing data of HCC patients extracted from The Cancer Genome Atlas (TCGA) dataset to identify and validate a cuproptosis-related lncRNA prognostic signature. Furthermore, we analyzed the clinical significance of the prognostic signature of cuproptosis-related lncRNA in predicting the immunotherapeutic efficacy and the status of the tumor immune microenvironment. The RNA-sequencing data, genomic mutations, and clinical information were downloaded for 374 HCC samples and 50 normal liver samples from TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset. Co-expression analysis of Gene-lncRNA pairs with 49 known cuproptosis-related prognostic genes was used to define cuproptosis-related prognostic lncRNAs. We performed the LASSO algorithm and univariate and multivariate Cox regression analysis, respectively, to gradually identify the prognostic risk models of cuproptosis-related lncRNA based on the TCGA-LIHC dataset. Subsequently, the predictive performance of the model was evaluated using receiver operation characteristic (ROC) curves, Kaplan-Meier survival curves, and prognostic nomogram. The analysis of gene-lncRNA co-expression with 49 known cuproptosis-related genes identified 1359 cuproptosis-related lncRNAs in the TCGA-LIHC data set. A prognostic model was constructed with nine cuproptosis-related prognostic lncRNAs (AC007998.3, AC003086.1, AC009974.2, IQCH-AS1, LINC0256 1, AC105345.1, ZFPM2-AS1, AL353708.1 and WAC-AS1) using LASSO regression and Cox regression analyses. Risk scores were calculated for all HCC patient samples based on the four cuproptosis-related lncRNA prognostic models. All HCC patients were divided into high-risk and low-risk subgroups according to a 1:1 ratio column. The Kaplan-Meier survival curve analysis showed that the overall survival rate (OS) of the high-risk group patients was significantly lower than that of the low-risk group. The principal component analysis (PCA) confirmed that the prognostic lncRNA model accurately distinguished between high- and low-risk HCC patients. Furthermore, regression analysis as well as ROC curves confirmed the prognostic value of the risk score. A nomogram with risk scores and other clinicopathological characteristics was constructed. The nomogram accurately predicted the probability of 1-, 3-, and 5-year OS in HCC patients. Tumor mutation burden (TMB) scores were higher for high-risk patients than for low-risk patients. HCC patients in the low-risk group showed lower TIDE scores and greater sensitivity to antitumor drugs than those in the high-risk group. Tumor immune responses and tumor immune cell infiltration were significantly different between the high-risk and low-risk groups of patients with HCC. Our study identified a 9-cuproptosis-related lncRNA signature that accurately predicted prognosis, immunotherapeutic efficacy, and the status of the tumor immune microenvironment in HCC patients. Therefore, this cuproptosis-related lncRNA risk model is a potential prognostic biometric feature in HCC and shows high clinical value in identifying HCC patients who are potentially responsive to immunotherapy.

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

An angiogenesis-associated gene-based signature predicting prognosis and immunotherapy efficacy of head and neck squamous cell carcinoma patients

OBJECTIVES

To develop a model that can assist in the diagnosis and prediction of prognosis for head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

Data from TCGA and GEO databases were used to generate normalized gene expression data. Consensus Cluster Plus was used for cluster analysis and the relationship between angiogenesis-associated gene (AAG) expression patterns, clinical characteristics and survival was examined. Support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) analyzes and multiple logistic regression analyzes were performed to determine the diagnostic model, and a prognostic nomogram was constructed using univariate and multivariate Cox regression analyses. ESTIMATE, XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, CIBERSORT algorithms were used to assess the immune microenvironment of HNSCC patients. In addition, gene set enrichment analysis, treatment sensitivity analysis, and AAGs mutation studies were performed. Finally, we also performed immunohistochemistry (IHC) staining in the tissue samples.

RESULTS

We classified HNSCC patients into subtypes based on differences in AAG expression from TCGA and GEO databases. There are differences in clinical features, TME, and immune-related gene expression between two subgroups. We constructed a HNSCC diagnostic model based on nine AAGs, which has good sensitivity and specificity. After further screening, we constructed a prognostic risk signature for HNSCC based on six AAGs. The constructed risk score had a good independent prognostic significance, and it was further constructed into a prognostic nomogram together with age and stage. Different prognostic risk groups have differences in immune microenvironment, drug sensitivity, gene enrichment and gene mutation.

CONCLUSION

We have constructed a diagnostic and prognostic model for HNSCC based on AAG, which has good performance. The constructed prognostic risk score is closely related to tumor immune microenvironment and immunotherapy response.

Identification of a new gene signature for prognostic evaluation in cervical cancer: based on cuproptosis-associated angiogenesis and multi-omics analysis

Patients with recurrent or metastatic cervical cancer are in urgent need of novel prognosis assessment or treatment approaches. In this study, a novel prognostic gene signature was discovered by utilizing cuproptosis-related angiogenesis (CuRA) gene scores obtained through weighted gene co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. To enhance its reliability, the gene signature was refined by integrating supplementary clinical variables and subjected to cross-validation. Meanwhile, the activation of the VEGF pathway was inferred from an analysis of cell-to-cell communication, based on the expression of ligands and receptors in cell transcriptomic datasets. High-CuRA patients had less infiltration of CD8 + T cells and reduced expression of most of immune checkpoint genes, which indicated greater difficulty in immunotherapy. Lower IC50 values of imatinib, pazopanib, and sorafenib in the high-CuRA group revealed the potential value of these drugs. Finally, we verified an independent prognostic gene SFT2D1 was highly expressed in cervical cancer and positively correlated with the microvascular density. Knockdown of SFT2D1 significantly inhibited ability of the proliferation, migration, and invasive in cervical cancer cells. CuRA gene signature provided valuable insights into the prediction of prognosis and immune microenvironment of cervical cancer, which could help develop new strategies for individualized precision therapy for cervical cancer patients.

A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer. Finding prognostic biomarkers is helpful in stratifying LUAD patients with different prognosis.

METHODS

We explored the correlation of LUAD prognosis and genes associated with chemotherapy in LUAD and obtained data of LUAD patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Drug sensitivity data were acquired from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Differential and enrichment analyses were used to screen the target genes utilizing limma and "clusterProfiler" packages. Then univariate and LASSO Cox analyses were used to select the prognosis-related genes. Survival analysis was used to estimate the overall survival (OS) of different groups.

RESULTS

Twenty-three differentially expressed genes (DEGs) were screened between LUAD samples and healthy samples, and BTK, FGFR2, PIM2, CHEK1, and CDK1 were selected to construct a prognostic signature. The OS of patients in the high-risk group (risk score higher than 0.69) was worse than that in the low-risk group (risk score lower than 0.69).

CONCLUSION

The risk score model constructed by five genes is a potential prognostic biomarker for LUAD patients.

Characterization of neutrophil extracellular traps related gene pair for predicting prognosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a malignant disease with a high incidence rate, high mortality and poor prognosis. Neutrophil extracellular traps (NETs), as an extracellular reticular structure, promote the development and progression of cancer in the tumor microenvironment, and have a promising prospect as a prognostic indicator. In the present study, we elucidated the prognostic value of NET-related genes.

METHODS

The NETs gene pair of The Cancer Genome Atlas cohort was constructed by least absolute shrinkage and selection operator analysis. Samples from the International Cancer Genome Consortium were performed to verify its feasibility. Kaplan-Meier analysis was used to compare the overall survival (OS) rate of the two subgroups. The independent predictors of OS were determined by univariate and multivariate Cox analysis. Furthermore, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway were analyzed by gene set enrichment analysis. The single sample gene set enrichment analysis method was performed to deplore the relationship of risk score with tumor immune microenvironment. The GSE149614 dataset was applied as single cell RNA level validation. PCR was performed to the detect mRNA expression profiles of NETs-related genes.

RESULTS

Our analysis of the NETs-related model provides a promising prospect as a prognostic indicator. The OS of high-risk group patients was significantly reduced. The risk score was an important independent predictor of HCC prognosis. The Nomogram model suggested a favorable classification performance. The drug resistance and sensitivity of tumor cells to chemotherapeutics was significantly correlated with the prognostic gene expression. The immune status of the two risk groups showed a marked difference.

CONCLUSIONS

The novel prognostic gene pair and immune landscape could predict the prognosis of HCC patients and provide a new understanding of immunotherapy in HCC.

Identification of m5C-related lncRNAs signature to predict prognosis and therapeutic responses in esophageal squamous cell carcinoma patients

Esophageal squamous cell carcinoma (ESCC) has a dismal prognosis because of atypical early symptoms and heterogeneous therapeutic responses. 5-methylcytosine (m5C) modification plays an important role in the onset and development of many tumors and is widespread in long non-coding RNA (lncRNA) transcripts. However, the functions of m5C and lncRNAs in ESCC have not been completely elucidated. Herein, this study aimed to explore the role of m5C-related lncRNAs in ESCC. The RNA-seq transcriptome profiles and clinical information were downloaded from the TCGA-ESCC database. Pearson analysis was used to identify m5C-related lncRNAs. Then we established the m5C-related lncRNAs prognostic signature (m5C-LPS) using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Then, the prognostic value of m5C-LPS was evaluated internally and externally using the TCGA-ESCC and GSE53622 databases through multiple methods. We also detected the expression of these lncRNAs in ESCC cell lines and patient tissues. Fluorescence in situ hybridization (FISH) was used to detect the prognostic value of specific lncRNA. In addition, clinical parameters, immune status, genomic variants, oncogenic pathways, enrichment pathways, and therapeutic response features associated with m5C-LPS were explored using bioinformatics methods. We constructed and validated a prognostic signature based on 9 m5C-related lncRNAs (AC002091.2, AC009275.1, CAHM, LINC02057.1, AC0006329.1, AC037459.3, AC064807.1, ATP2B1-AS1, and UBAC2-AS1). The quantitative real-time polymerase chain reaction (qRT-PCR) revealed that most lncRNAs were upregulated in ESCC cell lines and patient tissues. And AC002091.2 was validated to have significant prognostic value in ESCC patients. A composite nomogram was generated to facilitate clinical practice by integrating this signature with the N stage. Besides, patients in the low-risk group were characterized by good clinical outcomes, favorable immune status, and low oncogenic alteration. Function enrichment analysis indicated that the risk score was associated with mRNA splicing, ncRNA processing, and DNA damage repair response. At the same time, we found significant differences in the responses to chemoradiotherapy between the two groups, proving the value of m5C-LPS in treatment decision-making in ESCC. This study established a novel prognostic signature based on 9 m5C-related lncRNAs, which is a promising biomarker for predicting clinical outcomes and therapeutic response in ESCC.

Integrative Evaluation of the Clinical Significance Underlying Protein Arginine Methyltransferases in Hepatocellular Carcinoma

HCC is a major contributor to cancer-related mortality worldwide. Curative treatments are available for a minority of patients diagnosed at early stages; however, only a few multikinase inhibitors are available and are marginally effective in advanced cases, highlighting the need for novel therapeutic targets. One potential target is the protein arginine methyltransferase, which catalyzes various forms of arginine methylation and is often overexpressed in various cancers. However, the diverse expression patterns and clinical values of PRMTs in HCC remain unclear. In the present study, we evaluated the transcriptional expression of PRMTs in HCC cohorts using publicly available datasets. Our results revealed a significant association between PRMTs and prognosis in HCC patients with diverse clinical characteristics and backgrounds. This highlights the promising potential of PRMTs as prognostic biomarkers in patients with HCC. In particular, single-cell RNA (scRNA) sequencing analysis coupled with another human cohort study highlighted the pivotal role of PRMT1 in HCC progression, particularly in the context of Tex. Translating these findings into specific therapeutic decisions may address the unmet therapeutic needs of patients with HCC.

PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

Identification and validation of SOCS1/2/3/4 as potential prognostic biomarkers and correlate with immune infiltration in glioblastoma

Suppressor of cytokine signalling (SOCS) 1/2/3/4 are involved in the occurrence and progression of multiple malignancies; however, their prognostic and developmental value in patients with glioblastoma (GBM) remains unclear. The present study used TCGA, ONCOMINE, SangerBox3.0, UALCAN, TIMER2.0, GENEMANIA, TISDB, The Human Protein Atlas (HPA) and other databases to analyse the expression profile, clinical value and prognosis of SOCS1/2/3/4 in GBM, and to explore the potential development mechanism of action of SOCS1/2/3/4 in GBM. The majority of analyses showed that SOCS1/2/3/4 transcription and translation levels in GBM tissues were significantly higher than those in normal tissues. qRT-PCR, western blotting (WB) and immunohistochemical staining were used to verify that SOCS3 was expressed at higher mRNA and protein levels in GBM than in normal tissues or cells. High SOCS1/2/3/4 mRNA expression was associated with poor prognosis in patients with GBM, especially SOCS3. SOCS1/2/3/4 were highly contraindicated, which had few mutations, and were not associated with clinical prognosis. Furthermore, SOCS1/2/3/4 were associated with the infiltration of specific immune cell types. In addition, SOCS3 may affect the prognosis of patients with GBM through JAK/STAT signalling pathway. Analysis of the GBM-specific protein interaction (PPI) network showed that SOCS1/2/3/4 were involved in multiple potential carcinogenic mechanisms of GBM. In addition, colony formation, Transwell, wound healing and western blotting assays revealed that inhibition of SOCS3 decreased the proliferation, migration and invasion of GBM cells. In conclusion, the present study elucidated the expression profile and prognostic value of SOCS1/2/3/4 in GBM, which may provide potential prognostic biomarkers and therapeutic targets for GBM, especially SOCS3.

Exploring the prognostic function of TMB-related prognostic signature in patients with colon cancer

Tumor mutation burden (TMB) level is identified as a useful predictor in multiple tumors including colon adenocarcinoma (COAD). However, the function of TMB related genes has not been explored previously. In this study, we obtained patients' expression and clinical data from The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI). TMB genes were screened and subjected to differential expression analysis. Univariate Cox and LASSO analyses were utilized to construct the prognostic signature. The efficiency of the signature was tested by using a receiver operating characteristic (ROC) curve. A nomogram was further plotted to assess the overall survival (OS) time of patients with COAD. In addition, we compared the predictive performance of our signature with other four published signatures. Functional analyses indicated that patients in the low-risk group have obviously different enrichment of tumor related pathways and tumor infiltrating immune cells from that of high-risk patients. Our findings suggested that the ten genes' prognostic signature could exert undeniable prognostic functions in patients with COAD, which might provide significant clues for the development of personalized management of these patients.

DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, DRPPM-PATH-SURVEIOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis (GSEA) and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

Identification of an unfolded protein response-related signature for predicting the prognosis of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive lethal malignancy. An effective prognosis prediction model is urgently needed for treatment optimization.

Methods

The differentially expressed unfolded protein response (UPR)‒related genes between pancreatic tumor and normal tissue were analyzed using the TCGA-PDAC dataset, and these genes that overlapped with UPR‒related prognostic genes from the E-MTAB-6134 dataset were further analyzed. Univariate, LASSO and multivariate Cox regression analyses were applied to establish a prognostic gene signature, which was evaluated by Kaplan‒Meier curve and receiver operating characteristic (ROC) analyses. E‒MTAB‒6134 was set as the training dataset, while TCGA-PDAC, GSE21501 and ICGC-PACA-AU were used for external validation. Subsequently, a nomogram integrating risk scores and clinical parameters was established, and gene set enrichment analysis (GSEA), tumor immunity analysis and drug sensitivity analysis were conducted.

Results

A UPR-related signature comprising twelve genes was constructed and divided PDAC patients into high- and low-risk groups based on the median risk score. The UPR-related signature accurately predicted the prognosis and acted as an independent prognostic factor of PDAC patients, and the AUCs of the UPR-related signature in predicting PDAC prognosis at 1, 2 and 3 years were all more than 0.7 in the training and validation datasets. The UPR-related signature showed excellent performance in outcome prediction even in different clinicopathological subgroups, including the female (p<0.0001), male (p<0.0001), grade 1/2 (p<0.0001), grade 3 (p=0.028), N0 (p=0.043), N1 (p<0.001), and R0 (p<0.0001) groups. Furthermore, multiple immune-related pathways were enriched in the low-risk group, and risk scores in the low-risk group were also associated with significantly higher levels of tumor-infiltrating lymphocytes (TILs). In addition, DepMap drug sensitivity analysis and our validation experiment showed that PDAC cell lines with high UPR-related risk scores or UPR activation are more sensitive to floxuridine, which is used as an antineoplastic agent.

Conclusion

Herein, we identified a novel UPR-related prognostic signature that showed high value in predicting survival in patients with PDAC. Targeting these UPR-related genes might be an alternative for PDAC therapy. Further experimental studies are required to reveal how these genes mediate ER stress and PDAC progression.

A metabolism-related gene signature for predicting the prognosis in thyroid carcinoma

Metabolic reprogramming is one of the cancer hallmarks, important for the survival of malignant cells. We investigated the prognostic value of genes associated with metabolism in thyroid carcinoma (THCA). A prognostic risk model of metabolism-related genes (MRGs) was built and tested based on datasets in The Cancer Genome Atlas (TCGA), with univariate Cox regression analysis, LASSO, and multivariate Cox regression analysis. We used Kaplan-Meier (KM) curves, time-dependent receiver operating characteristic curves (ROC), a nomogram, concordance index (C-index) and restricted mean survival (RMS) to assess the performance of the risk model, indicating the splendid predictive performance. We established a three-gene risk model related to metabolism, consisting of PAPSS2, ITPKA, and CYP1A1. The correlation analysis in patients with different risk statuses involved immune infiltration, mutation and therapeutic reaction. We also performed pan-cancer analyses of model genes to predict the mutational value in various cancers. Our metabolism-related risk model had a powerful predictive capability in the prognosis of THCA. This research will provide the fundamental data for further development of prognostic markers and individualized therapy in THCA.

IOFS-SA: An interactive online feature selection tool for survival analysis

BACKGROUND

Survival analysis is a primary problem before clinical treatments to cancer patients after their operations. In order to make this kind of analysis simple, many corresponding tools have been proposed. Though these tools are easy to use, there exist still two fatal flaws. One is that sample grouping is commonly empirical and wrongly based on original gene expressions or survival time. The other is that their feature selection methods mostly depend univariate semi-supervised regression or the multivariate one without considering the small sample size compared with the high dimension.

OBJECTIVE

In order to solve the two problems, we design an automatic feature selection web tool which can also satisfy interactive sample grouping.

METHODS

An automatic feature selection is performed on user-defined data or TCGA data. users can also perform manual feature selection. Then, hierarchical clustering is used and an automatic re-clustering strategy is proposed after interactive risk score split. Kaplan-Meier survival curve and log-rank test are utilized as the measurement.

RESULTS

Experimental results on 53 datasets from TCGA demonstrate the effectiveness of our method. The tree view, heat map and scatter map can intuitively display the result of the selected genes to the doctors for further research.

CONCLUSIONS

This method is suitable for survival analysis of high-dimensional small sample data sets. At the same time, it also provides a platform for researchers to analyze custom data. It solves the problems of the existing web tools and provides an effective feature selection method for survival analysis.

AVAILABILITY

The full code package is freely available and can be downloaded at https://github.com/Yuan-23/IOFS-SA-ecp-data-main, and the online version at https://bioinfor.nefu.edu.cn/IOFS-SA/ is ready for use freely.

Role of reactive oxygen species in regulating 27-hydroxycholesterol-induced apoptosis of hematopoietic progenitor cells and myeloid cell lines

Oxysterols are oxygenated derivatives of cholesterol that contain an additional hydroxy, epoxide, or ketone group in the sterol nucleus and/or a hydroxyl group in the side chain of the cholesterol molecule. 27-Hydroxycholesterol (27HC) is a side-chain oxysterol that is oxygenated at the 27th carbon atom of cholesterol. The oxysterol (27HC) is produced via oxidation by sterol 27-hydroxylase (CYP27A1) and metabolized via oxysterol 7a-hydroxylase (CYP7B1) for bile acid synthesis in the liver. A previous study has demonstrated that treatment with the alternative Estrogen receptor alpha (ERα) ligand 27HC induces ERα-dependent hematopoietic stem cell (HSC) mobilization. In addition, Cyp27a1-deficient mice demonstrate significantly reduced 27HC levels and HSC mobilization. Here, we report that exogenous 27HC treatment leads to a substantial reduction in the hematopoietic stem and progenitor cell (HSPC) population owing to significantly increased reactive oxygen species (ROS) levels and apoptosis in the bone marrow (BM). However, 27HC does not influence the population of mature hematopoietic cells in the BM. Furthermore, exogenous 27HC treatment suppresses cell growth and promotes ROS production and apoptosis in leukemic cells. Moreover, acute myeloid leukemia (AML) patients with high CYP7B1 expression (expected to have inhibition of 27HC) had significantly shorter survival than those with low CYP7B1 expression (expected to have an elevation of 27HC). Single-cell RNA-sequencing (scRNA seq) analysis revealed that the expression of CYP7B1 was significantly increased in AML patients. Thus, our study suggests that 27HC may serve as a potent agent for regulating pools of HSPCs and may have an application as a novel therapeutic target for hematological malignancies. Collectively, pharmacological inhibition of CYP7B1 (expected to have an elevation of 27HC) would potentially have fewer long-term hematological side effects, particularly when used in combination with chemotherapy or radiation for the treatment of leukemia patients.

m6A-Related Genes Contribute to Poor Prognosis of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common and lethal digestive system cancers worldwide. N6-methyladenosine (m6A) modification plays an essential role in diverse critical biological processes and may participate in the development and progression of HCC.

Methods

We downloaded transcriptome data and clinical data from TCGA as the training set. COX and LASSO screened prognostic m6A genes. ROC and Kaplan-Meier curve analysis evaluated the effectiveness of the model. ICGC and our center data were used as verification sets.

Results

We include the “writer (METTL3, METTL14, WTAP, KIAA1429, RBM15, ZC3H13),” the “reader (YTHDC1, YTHDC2, YTHDF1, YTHDF2, HNRNPC),” and the “eraser (FTO, ALKBH5)” in the study. We obtained YTHDF2, YTHDF1, METTL3, and KIAA1429 through differential analysis, survival analysis, and LASSO regression analysis. The prediction model was established based on the expression of these 4 molecules. HCC patients were divided into “high-risk” and “low-risk” groups to compare survival differences. The model suggested a poor prognosis in the validation sets.

Conclusion

The four-m6A-related-gene combination model was an independent prognostic factor of HCC and could improve the prediction of the prognosis of HCC.

Inhibition of USP1 enhances anticancer drugs-induced cancer cell death through downregulation of survivin and miR-216a-5p-mediated upregulation of DR5

Ubiquitin-specific protease 1 (USP1) is a deubiquitinase involved in DNA damage repair by modulating the ubiquitination of major regulators, such as PCNA and FANCD2. Because USP1 is highly expressed in many cancers, dysregulation of USP1 contributes to cancer therapy. However, the role of USP1 and the mechanisms underlying chemotherapy remain unclear. In this study, we found high USP1 expression in tumor tissues and that it correlated with poor prognosis in RCC. Mechanistically, USP1 enhanced survivin stabilization by removing ubiquitin. Pharmacological inhibitors (ML23 and pimozide) and siRNA targeting USP1 induced downregulation of survivin expression. In addition, ML323 upregulated DR5 expression by decreasing miR-216a-5p expression at the post-transcriptional level, and miR-216a-5p mimics suppressed the upregulation of DR5 by ML323. Inhibition of USP1 sensitized cancer cells. Overexpression of survivin or knockdown of DR5 markedly prevented the co-treatment with ML323 and TRAIL-induced apoptosis. These results of in vitro were proved in a mouse xenograft model, in which combined treatment significantly reduced tumor size and induced survivin downregulation and DR5 upregulation. Furthermore, USP1 and survivin protein expression showed a positive correlation, whereas miR-216a-5p and DR5 were inversely correlated in RCC tumor tissues. Taken together, our results suggest two target substrates of USP1 and demonstrate the involvement of survivin and DR5 in USP1-targeted chemotherapy.

Association of Elevated Expression Levels of COL4A1 in Stromal Cells with an Immunosuppressive Tumor Microenvironment in Low-Grade Glioma, Pancreatic Adenocarcinoma, Skin Cutaneous Melanoma, and Stomach Adenocarcinoma

Aberrant expression of collagen type IV alpha chain 1 (COL4A1) can influence tumor cell behavior. To examine the association of COL4A1 expression in the tumor microenvironment (TME) with tumor progression, we performed bioinformatics analyses of The Cancer Genome Atlas RNA sequencing and RNA microarray datasets available in public databases and identified upregulated COL4A1 expression in most examined tumor types compared to their normal counterparts. The elevated expression of COL4A1 was correlated with low survival rates of patients with low-grade glioma, pancreatic adenocarcinoma, skin cutaneous melanoma, and stomach adenocarcinoma, thus suggesting its potential use as a biomarker for the poor prognosis of these tumors. However, COL4A1 was mostly expressed in adjacent stromal cells, such as cancer-associated fibroblasts (CAFs) and endothelial cells. Additionally, COL4A1 expression was highly correlated with the signatures of CAFs and endothelial cells in all four tumor types. The expression of marker genes for the infiltration of pro-tumoral immune cells, such as Treg, M2, and TAM, and those of immunosuppressive cytokines exhibited very strong positive correlations with COL4A1 expression. Collectively, our data suggest that COL4A1 overexpression in stromal cells may be a potential regulator of tumor-supporting TME composition associated with poor prognosis.

Tumor-associated macrophages related signature in glioma

Background: Glioma is the most common malignant primary tumor with a poor prognosis. Infiltration of tumor-associated macrophages (TAMs) is a hallmark of glioma. However, the regulatory mechanism of TAMs and the prognostic value of related signature in glioma remain unclear.

Methods: TAMs were analyzed by EPIC, MCPCOUNTER and XCELL methods in multiple cohorts, including the TCGA merged GBMLGG, CGGA mRNAseq-325, and CGGA mRNAseq-693. Weighted correlation network analysis (WGCNA) were performed to identify candidate hub genes that might be related to TAMs. The prognostic genes were selected by Univariate Cox regression, Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) multivariate Cox regression algorithm, and were used to construct a high efficacy prediction model.

Results: Compared with LGG, TAMs of GBM in the TCGA merged GBMLGG, CGGA mRNAseq-693, and CGGA mRNAseq-325 cohorts were increased, and high TAMs levels predicted poorer overall survival for gliomas. The prediction model constructed by nine prognostic genes was highly efficient. The TAMs related risk-score was an independent risk factor for glioma. Moreover, high risk score was correlated with an increased population of TAMs in glioma, as well as the high immune scores, stromal scores and ESTIMATE scores.

Conclusions: Increased TAMs might be an immune evasion mechanism of glioma. In addition, our findings suggested that TAMs-related signature was a valuable prognostic biomarker in glioma and provided therapeutic targets for glioma.

Identification of IL20RB as a Novel Prognostic and Therapeutic Biomarker in Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a type of life-threatening malignant tumor of the urinary system. IL20RB, interleukin 20 receptor subunit beta, is a cytokine receptor subunit coding gene and was initially found to play a vital role in human cancers, while its role in ccRCC still remains unclear.

Methods

In this work, we explored the prognostic value and therapeutic potential of IL20RB in ccRCC mainly by online tools. Firstly, we used UALCAN and GEPIA to explore the expression profile and prognostic value of IL20RB in various cancers; the expression profile in tumor cell lines was also analysed with CCLE and Expression Atlas. Then, we decided to focus on ccRCC for further analysis; we further demonstrated the significant correlation between expression and clinical features by GEPIA and UALCAN. In order to reveal the potential intrinsic mechanism responsible for the upregulation of IL20RB in ccRCC, we made genetic alternation analysis and methylation analysis. cBioPortal was used for genetic alternation analysis. UALCAN, MethSurv, and Xena were used for methylation analysis. To learn details of how IL20RB might function in ccRCC, we further conducted functional analysis and immune infiltration analysis. STRING and GSEA were used to do functional analysis. TIMER was used for immune infiltration analysis; KM plotter was used for survival analysis.

Results

Results show that IL20RB is upregulated in ccRCC, and low methylation may be responsible for its upregulation. Both high expression and low methylation of IL20RB predict worse survival, and both have a strong positive correlation with clinical characteristics. In addition, results indicate that there exists a crosstalk between IL20RB and neutrophils. Furthermore, the immune microenvironment could influence the prognosis predicting ability of IL20RB.

Conclusions

In conclusion, IL20RB plays an important role in ccRCC and is identified as a novel prognostic and potential therapeutic biomarker in ccRCC.

Survival Genie, a web platform for survival analysis across pediatric and adult cancers

The genomics data-driven identification of gene signatures and pathways has been routinely explored for predicting cancer survival and making decisions related to targeted treatments. A large number of packages and tools have been developed to correlate gene expression/mutations to the clinical outcome but lack the ability to perform such analysis based on pathways, gene sets, and gene ratios. Furthermore, in this single-cell omics era, the cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option. Additionally, no bioinformatics online tool evaluates the associations between the enrichment of canonical cell types and survival across cancers. Here we have developed Survival Genie, a web tool to perform survival analysis on single-cell RNA-seq (scRNA-seq) data and a variety of other molecular inputs such as gene sets, genes ratio, tumor-infiltrating immune cells proportion, gene expression profile scores, and tumor mutation burden. For a comprehensive analysis, Survival Genie contains 53 datasets of 27 distinct malignancies from 11 different cancer programs related to adult and pediatric cancers. Users can upload scRNA-seq data or gene sets and select a gene expression partitioning method (i.e., mean, median, quartile, cutp) to determine the effect of expression levels on survival outcomes. The tool provides comprehensive results including box plots of low and high-risk groups, Kaplan–Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile. The analytical options and comprehensive collection of cancer datasets make Survival Genie a unique resource to correlate gene sets, pathways, cellular enrichment, and single-cell signatures to clinical outcomes to assist in developing next-generation prognostic and therapeutic biomarkers. Survival Genie is open-source and available online at https://bbisr.shinyapps.winship.emory.edu/SurvivalGenie/.

High-Mobility Group AT-Hook 1 Served as a Prognosis Biomarker and Associated with Immune Infiltrate in Hepatocellular Carcinoma

BACKGROUND

The protein high-mobility group AT-hook 1 (HMGA1) has been demonstrated that modulated cellular proliferation, invasion, and apoptosis with a poor prognosis in miscellaneous carcinomas. However, the mechanism of circumstantial carcinogenesis and association with the immune microenvironment of HMGA1 in hepatocellular carcinoma (HCC) had not been extensively explored.

METHODS

The gene expression, clinicopathological correlation, and prognosis analysis were performed in the data obtained from TCGA. The results were further validated by ICGC and GEO database and external validation cohort from Guangxi. The HMGA1 protein expression was further examined in the HPA database. Biological function analyses were conducted by GSEA, STRING database, and Coexpedia online tool. Using TIMER and CIBERSORT method, the relationship between immune infiltrate and HMGA1 was investigated.

RESULTS

In HCC, HMGA1 had much higher transcriptional and proteomic expression than in corresponding paraneoplastic tissue. Patients with high HMGA1 expression had a poor prognosis and unpromising clinicopathological features. High HMGA1 expression was closely related to the cell cycle, tumorigenesis, substance metabolism, and immune processes by regulating complex signaling pathways. Notably, HMGA1 may be associated with TP53 mutational carcinogenesis. Moreover, increased HMGA1 expression may lead to an increase in immune infiltration and a decrease in tumor purity in HCC. CIBERSORT analysis elucidated that the amount of B cell naive, B cell memory, T cells gamma delta, macrophages M2, and mast cell resting decreased when HMGA1 expression was high, whereas T cells follicular helper, macrophages M0, and Dendritic cells resting increased.

CONCLUSION

In conclusions, HMGA1 is a potent prognostic biomarker and a sign of immune infiltration in HCC, which may be a potential immunotherapy target for HCC.

SOCS3 is Related to Cell Proliferation in Neuronal Tissue: An Integrated Analysis of Bioinformatics and Experiments

Glioma is the most common primary malignant tumor that occurs in the central nervous system. Gliomas are subdivided according to a combination of microscopic morphological, molecular, and genetic factors. Glioblastoma (GBM) is the most aggressive malignant tumor; however, efficient therapies or specific target molecules for GBM have not been developed. We accessed RNA-seq and clinical data from The Cancer Genome Atlas, the Chinese Glioma Genome Atlas, and the GSE16011 dataset, and identified differentially expressed genes (DEGs) that were common to both GBM and lower-grade glioma (LGG) in three independent cohorts. The biological functions of common DEGs were examined using NetworkAnalyst. To evaluate the prognostic performance of common DEGs, we performed Kaplan-Meier and Cox regression analyses. We investigated the function of SOCS3 in the central nervous system using three GBM cell lines as well as zebrafish embryos. There were 168 upregulated genes and 50 downregulated genes that were commom to both GBM and LGG. Through survival analyses, we found that SOCS3 was the only prognostic gene in all cohorts. Inhibition of SOCS3 using siRNA decreased the proliferation of GBM cell lines. We also found that the zebrafish ortholog, socs3b, was associated with brain development through the regulation of cell proliferation in neuronal tissue. While additional mechanistic studies are necessary, our results suggest that SOCS3 is an important biomarker for glioma and that SOCS3 is related to the proliferation of neuronal tissue.

A New Survival Model Based on ADAMTSs for Prognostic Prediction in Clear Cell Renal Cell Carcinoma

The main purpose of this study was to explore the genetic variation, gene expression, and clinical significance of ADAMTSs (a disintegrin and metalloprotease domains with thrombospondin motifs) across cancer types. Analysis of data from the TCGA (The Cancer Genome Atlas) database showed that the ADAMTSs have extensive CNV (copy number variation) and SNV (single nucleotide variation) across cancer types. Compared with normal tissues, the methylation of ADAMTSs in cancer tissues is also significantly different, which affects the expression of ADAMTS gene and the prognosis of cancer patients. Through gene expression analysis, we found that ADAMTS family has significant changes in gene expression across cancer types and is closely related to the prognosis of carcinoma, especially in ccRCC (clear cell renal cell carcinoma). LASSO regression analysis was used to establish a prognostic model based on the ADAMTSs to judge the prognosis of patients with ccRCC. Multiple Cox regression analysis suggested that age, grade, stage, and risk score of the prognostic model of ccRCC were independent prognostic factors in patients with renal clear cell carcinoma. These findings indicate that the ADAMTSs-based survival model can accurately predict the prognosis of patients with ccRCC and suggest that ADAMTSs are a potential prognostic biomarker and therapeutic target in ccRCC.

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

BACKGROUND

Survival analysis is a cornerstone of medical research, enabling the assessment of clinical outcomes for disease progression and treatment efficiency. Despite its central importance, no commonly used spreadsheet software can handle survival analysis and there is no web server available for its computation.

OBJECTIVE

Here, we introduce a web-based tool capable of performing univariate and multivariate Cox proportional hazards survival analysis using data generated by genomic, transcriptomic, proteomic, or metabolomic studies.

METHODS

We implemented different methods to establish cut-off values for the trichotomization or dichotomization of continuous data. The false discovery rate is computed to correct for multiple hypothesis testing. A multivariate analysis option enables comparing omics data with clinical variables.

RESULTS

We established a registration-free web-based survival analysis tool capable of performing univariate and multivariate survival analysis using any custom-generated data.

CONCLUSIONS

This tool fills a gap and will be an invaluable contribution to basic medical and clinical research.

Identification and Verification of a 17 Immune-Related Gene Pair Prognostic Signature for Colon Cancer

Background

Colon cancer (CC) is a malignant tumor with a high incidence and poor prognosis. Accumulating evidence shows that the immune signature plays an important role in the tumorigenesis, progression, and prognosis of CC. Our study is aimed at establishing a novel robust immune-related gene pair signature for predicting the prognosis of CC.

Methods

Gene expression profiles and corresponding clinical information are obtained from two public data sets: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO, GSE39582). We screened out immune-related gene pairs (IRGPs) associated with prognosis in the discovery cohort. Lasso-Cox proportional hazard regression was used to develop the best prognostic signature model. According to this, the patients in the validation cohort were divided into high immune-risk group and low immune-risk group, and the prediction ability of the signature model was verified by survival analysis and independent prognostic analysis.

Results

A total of 17 IRGPs composed of 26 IRGs were used to construct a prognostic-related risk scoring model. This model accurately predicted the prognosis of CC patients, and the patients in the high immune-risk group indicated poor prognosis in the discovery cohort and validation cohort. Besides, whether in univariate or multivariate analysis, the IRGP signature was an independent prognostic factor. T cell CD4 memory resting in the low-risk group was significantly higher than that in the high-risk group. Functional analysis showed that the biological processes of the low-risk group included "TCA cycle" and "RNA degradation," while the high-risk group was enriched in the "CAMs" and "focal adhesion" pathways.

Conclusion

We have successfully established a signature model composed of 17 IRGPs, which provides a novel idea to predict the prognosis of CC patients.

ITGA3 Is Associated With Immune Cell Infiltration and Serves as a Favorable Prognostic Biomarker for Breast Cancer

Background

ITGA3 is a member of the integrin family, a cell surface adhesion molecule that can interact with extracellular matrix (ECM) proteins. The purpose of this study was to explore the significance of ITGA3 expression in the prognosis and clinical diagnosis of breast cancer patients.

Methods

Oncomine, the Human Protein Atlas (HPA) and UALCAN were used to analyze the expression of ITGA3 in various cancers. PrognoScan, GEPIA, Kaplan–Meier plotter and Easysurv were utilized to analyze the prognosis of ITGA3 in certain cancers. Based on TCGA data, a receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of ITGA3 expression. cBio-Portal and MethSurv were used to evaluate the genomic mechanism. LinkedOmics, NetworkAnalyst and Metascape were used to build the signaling network. TIMER is a web server for comprehensive analysis of tumor infiltrating immune cells and tumor infiltrating lymphocytes (TILs).

Results

The expression of ITGA3 in normal breast tissues was greater than that in breast cancer tissues at both the mRNA and protein levels. High expression of ITGA3 was associated with better prognosis of breast cancer patients. ROC analysis indicated that ITGA3 had significant diagnostic value. Genomic analysis revealed that promoter methylation of ITGA3 leads to transcriptional silencing, which may be one of the mechanisms underlying ITGA3 downregulation in BRCA. Immune infiltration analysis showed that ITGA3 may be involved in the recruitment of immune cells.

Conclusions

This study identified ITGA3 as a novel biomarker to estimate the diagnosis and prognosis of breast cancer. In addition, ITGA3 is involved in ECM regulation and immune cell infiltration.

Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT

The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.

Identification of a Potential PPAR-Related Multigene Signature Predicting Prognosis of Patients with Hepatocellular Carcinoma

Peroxisome proliferator-activated receptors (PPARs) and part of their target genes have been reported to be related to the progression of hepatocellular carcinoma (HCC). The prognosis of HCC is not optimistic, and more accurate prognostic markers are needed. This study focused on discovering potential prognostic markers from the PPAR-related gene set. The mRNA data and clinical information of HCC were collected from TCGA and GEO platforms. Univariate Cox and lasso Cox regression analyses were used to screen prognostic genes of HCC. Three genes (MMP1, HMGCS2, and SLC27A5) involved in the PPAR signaling pathway were selected as the prognostic signature of HCC. A formula was established based on the expression values and multivariate Cox regression coefficients of selected genes, that was, risk score = 0.1488∗expression value of MMP1 + (−0.0393)∗expression value of HMGCS2 + (−0.0479)∗expression value of SLC27A5. The prognostic ability of the three-gene signature was assessed in the TCGA HCC dataset and verified in three GEO sets (GSE14520, GSE36376, and GSE76427). The results showed that the risk score based on our signature was a risk factor with a HR (hazard ratio) of 2.72 (95%CI (Confidence Interval) = 1.87 ~ 3.95, p < 0.001) for HCC survival. The signature could significantly (p < 0.0001) distinguish high-risk and low-risk patients with poor prognosis for HCC. In addition, we further explored the independence and applicability of the signature with other clinical indicators through multivariate Cox analysis (p < 0.001) and nomogram analysis (C‐index = 0.709). The above results indicate that the combination of MMP1, HMGCS2, and SLC27A5 selected from the PPAR signaling pathway could effectively, independently, and applicatively predict the prognosis of HCC. Our research provided new insights to the prognosis of HCC.

Identification and Complete Validation of Prognostic Gene Signatures for Human Papillomavirus-Associated Cancers: Integrated Approach Covering Different Anatomical Locations

Human papillomavirus (HPV) infects squamous epithelium and is a major cause of cervical cancer (CC) and a subset of head and neck cancers (HNC). Virus-induced tumorigenesis, molecular alterations, and related prognostic markers are expected to be similar between the two cancers, but they remain poorly understood. We present integrated molecular analysis of HPV-associated tumors from TCGA and GEO databases and identify prognostic biomarkers. Analysis of gene expression profiles identified common upregulated genes and pathways of DNA replication and repair in the HPV-associated tumors. We established 34 prognostic gene signatures with a universal cutoff value in TCGA-CC using Elastic Net Cox regression analysis. We were able to externally validate our results in the TCGA-HNC and several GEO data sets, and demonstrated prognostic power in HPV-associated HNC, but not in HPV-negative cancers. The HPV-related prognostic and predictive indicator did not discriminate other cancers, except bladder urothelial carcinoma. These results identify and completely validate a highly selective prognostic system and its cross-usefulness in HPV-associated cancers, regardless of the tumor's anatomical subsite.IMPORTANCE Persistent infection with high-risk HPV interferes with cell function regulation and causes cell mutations, which accumulate over the long term and eventually develop into cancer. Results of pathway enrichment analysis presumably showed this accumulation of intracellular damage during the chronic HPV-infected state. We used highly advanced statistical methods to identify the most appropriate genes and coefficients and developed the HPV-related prognostic and predictive indicator (HPPI) risk scoring system. We applied the same cutoff value to training and validation sets and demonstrated good prognostic performance in both data sets, and confirmed a consistent trend in external validation. Moreover, HPPI presented significant validation results for bladder cancer suspected to be related to HPV. This suggested that our risk scoring system based on the prognostic gene signature could play an important role in the development of treatment strategies for patients with HPV-related cancer.

Downregulated mRNA Expression of ZNF385B Is an Independent Predictor of Breast Cancer

Background

ZNF385B, a zinc finger protein, has been known as a potential biomarker in some neurological and hematological studies recently. Although numerous studies have demonstrated the potential function of zinc finger proteins in tumor progression, the effects of ZNF385B in breast cancer (BC) are less studied.

Methods

The Oncomine database and “ESurv” tool were used to explore the differential expression of ZNF385B in pan-cancer. Furthermore, data of patients with BC were downloaded from The Cancer Genome Atlas (TCGA). The receiver operating characteristic (ROC) curve of ZNF385B expression was established to explore the diagnostic value of ZNF385B and to obtain the cut-off value of high or low ZNF385B expression in BC. The chi-square test as well as Fisher exact test was used for identification of the relationships between clinical features and ZNF385B expression. Furthermore, the effects of ZNF385B on BC patients' survival were evaluated by the Kaplan-Meier and Cox regression. Data from the Gene Expression Omnibus (GEO) database were employed to validate the results of TCGA. Protein expression of ZNF385B in BC patient specimens was detected by immunohistochemistry (IHC) staining.

Results

ZNF385B expression was downregulated in most types of cancer including BC. Low ZNF385B expression was related with survival status, overall survival (OS), and recurrence of BC. ZNF385B had modest diagnostic value, which is indicated by the area under the ROC curve (AUC = 0.671). Patients with lower ZNF385B expression had shorter OS and RFS (relapse-free survival). It had been demonstrated that low ZNF385B expression represented independent prognostic value for OS and RFS by multivariate survival analysis. The similar results were verified by datasets from the GEO database as well. The protein expression of ZNF385B was decreased in patients' samples compared with adjacent tissues by IHC.

Conclusions

Low ZNF385B expression was an independent predictor for worse prognosis of BC patients.

A Novel Prognostic Model of Endometrial Carcinoma Based on Clinical Variables and Oncogenomic Gene Signature

Due to the difficulty in predicting the prognosis of endometrial carcinoma (EC) patients by clinical variables alone, this study aims to build a new EC prognosis model integrating clinical and molecular information, so as to improve the accuracy of predicting the prognosis of EC. The clinical and gene expression data of 496 EC patients in the TCGA database were used to establish and validate this model. General Cox regression was applied to analyze clinical variables and RNAs. Elastic net-penalized Cox proportional hazard regression was employed to select the best EC prognosis-related RNAs, and ridge regression was used to construct the EC prognostic model. The predictive ability of the prognostic model was evaluated by the Kaplan-Meier curve and the area under the receiver operating characteristic curve (AUC-ROC). A clinical-RNA prognostic model integrating two clinical variables and 28 RNAs was established. The 5-year AUC of the clinical-RNA prognostic model was 0.932, which is higher than that of the clinical-alone (0.897) or RNA-alone prognostic model (0.836). This clinical-RNA prognostic model can better classify the prognosis risk of EC patients. In the training group (396 patients), the overall survival of EC patients was lower in the high-risk group than in the low-risk group [HR = 32.263, (95% CI, 7.707-135.058), P = 8e-14]. The same comparison result was also observed for the validation group. A novel EC prognosis model integrating clinical variables and RNAs was established, which can better predict the prognosis and help to improve the clinical management of EC patients.

Prognostic Significance of COVID-19 Receptor ACE2 and Recommendation for Antihypertensive Drug in Renal Cell Carcinoma

PURPOSE

Owing to its worldwide spread, the coronavirus disease (COVID-19) epidemic was declared a pandemic by the World Health Organization on March 11, 2020. Angiotensin-converting enzyme 2 (ACE2) is the outer surface protein of the cell membrane that is abundantly distributed in the heart, lungs, and kidneys and plays an important role in molecular docking of the severe acute respiratory syndrome coronavirus 2. In this study, we aimed to analyze the difference in the survival rate according to ACE2 expressions in pan-cancer.

MATERIALS AND METHODS

We downloaded clinical and genomic data from The Cancer Genome Atlas. We used Kaplan-Meier with a log-rank test, and the Cox proportional hazards regression to analyze prognostic significance.

RESULTS

In the Kaplan-Meier curve, clear cell renal cell carcinoma (ccRCC), uveal melanoma, and prostate adenocarcinoma showed statistical significance. In the Cox regression, thyroid carcinoma and glioblastoma multiforme and ccRCC showed significant results. Only ccRCC had statistical significance, and high ACE2 expression is related to good prognosis. It is known that the ACE inhibitor, a primary antihypertensive agent, increases ACE2 expression.

CONCLUSION

Based on these results, we believe that the ACE inhibitor will be important to increase the lifespan of ccRCC patients. This study is the first research to offer a recommendation on the use of anti-hypertensive drugs to ccRCC patients.

A Three-Metabolic-Genes Risk Score Model Predicts Overall Survival in Clear Cell Renal Cell Carcinoma Patients

Metabolic alterations play crucial roles in carcinogenesis, tumor progression, and prognosis in clear cell renal cell carcinoma (ccRCC). A risk score (RS) model for ccRCC consisting of disease-associated metabolic genes remains unidentified. Here, we utilized gene set enrichment analysis to analyze expression data from normal and tumor groups from the cancer genome atlas. Out of 70 KEGG metabolic pathways, we found seven and two pathways to be significantly enriched in our normal and tumor groups, respectively. We identified 113 genes enriched in these nine pathways. We further filtered 47 prognostic-related metabolic genes and used Least absolute shrinkage and selection operator (LASSO) analysis to construct a three-metabolic-genes RS model composed of ALDH3A2, B3GAT3, and CPT2. We further tested the RS by mapping Kaplan-Meier plots and receiver operating characteristic curves, the results were promising. Additionally, multivariate Cox analysis revealed the RS to be an independent prognostic factor. Thereafter, we considered all the independent factors and constructed a nomogram model, which manifested in better prediction capability. We validated our results using a dataset from ArrayExpress and through qRT-PCR. In summary, our study provided a metabolic gene-based RS model that can be used as a prognostic predictor for patients with ccRCC.