HPAanalyze: an R package that facilitates the retrieval and analysis of the Human Protein Atlas data

Calix[6]arene dismantles extracellular vesicle biogenesis and metalloproteinases that support pancreatic cancer hallmarks

Many challenges are faced in pancreatic cancer treatment due to late diagnosis and poor prognosis because of high recurrence and metastasis. Extracellular vesicles (EVs) and matrix metalloproteinases (MMPs), besides acting in intercellular communication, are key players in the cancer cell plasticity responsible for initiating metastasis. Therefore, these entities provide valuable targets for the development of better treatments. In this context, this study aimed to evaluate the potential of calix[6]arene to disturb the release of EVs and the activity of MMPs in pancreatic cancer cells. We found a correlation between the endocytic-associated mediators and the prognosis of pancreatic cancer patients. We observed a more active EV machinery in the pancreatic cancer cell line PANC-1, which was reduced three-fold by treatment with calix[6]arene at subtoxic concentration (5 μM; p 〈0,001). We observed the modulation of 186 microRNAs (164 miRNAs upregulated and 22 miRNAs downregulated) upon calix[6]arene treatment. Interestingly, some of them as miR-4443 and miR-3909, regulates genes HIF1A e KIF13A that are well known to play a role in transport of vesicles. Furthermore, Calix[6]arene downmodulated matrix metalloproteinases (MMPs) -2 and - 9 and disturbed the viability of pancreatic organoids which recapitulate the cellular heterogeneity, structure, and functions of primary tissues. Our findings shed new insights on calix[6]arene's antitumor mechanism, including its intracellular effects on vesicle production and trafficking, as well as MMP activity, which may harm the tumor microenvironment and contribute to a reduction in cancer cell dissemination, which is one of the challenges associated with high mortality in pancreatic cancer.

A novel prognostic signature of coagulation-related genes leveraged by machine learning algorithms for lung squamous cell carcinoma

Coagulation-related genes (CRGs) have been demonstrated to be essential for the development of certain tumors; however, little is known about CRGs in lung squamous cell carcinoma (LUSC). In this study, we adopted CRGs to construct a coagulation-related gene prognostic signature (CRGPS) using machine learning algorithms. Using a set of 92 machine learning integrated algorithms, the CRGPS was determined to be the optimal prognostic signature (median C-index = 0.600) for predicting the prognosis of an LUSC patient. The CRGPS was not only superior to traditional clinical parameters (e.g., T stage, age, and gender) and its commutative genes but also outperformed 19 preexisting prognostic signatures for LUSC on predictive accuracy. The CRGPS score was positively correlated with poor prognoses in patients with LUSC (hazard ratio > 1, p < 0.05), indicating its suitability as a prognostic marker for this disease. The CRGPS was observed to be inversely correlated with the degree of infiltration of natural killer cells. For some tumors, patients with lower CRGPS scores are more likely to experience enhanced immunotherapy effects (area under the curve = 0.70), which implies that the CRGPS can potentially predict immunotherapy efficacy. A high CRGPS score is predictive of an LUSC patient being sensitive to several drugs. Collectively, these findings indicate that the CRGPS may be a reliable indicator of the prognoses of patients with LUSC and may be useful for the clinical management of such patients.

Protective effect and mechanism of Qingfei Paidu decoction on myocardial damage mediated by influenza viruses

Introduction: Significant attention has been paid to myocardial damage mediated by the single-stranded RNA virus. Qingfei Paidu decoction (QFPDD) has been proved to protect the damage caused by the influenza virus A/PR/8/1934 (PR8), but its specific mechanism is unclear. Methods: Molecular biological methods, together with network pharmacology, were used to analyze the effects and underlying mechanism of QFPDD treatment on PR8-induced myocardial damage to obtain insights into the treatment of COVID-19-mediated myocardial damage. Results: Increased apoptosis and subcellular damage were observed in myocardial cells of mice infected by PR8. QFPDD treatment significantly inhibited the apoptosis and subcellular damage induced by the PR8 virus. The inflammatory factors IFN-β, TNF-α, and IL-18 were statistically increased in the myocardia of the mice infected by PR8, and the increase in inflammatory factors was prevented by QFPDD treatment. Furthermore, the expression levels or phosphorylation of necroptosis-related proteins RIPK1, RIPK3, and MLKL were abnormally elevated in the group of infected mice, while QFPDD restored the levels or phosphorylation of these proteins. Our study demonstrated that HIF-1α is a key target of QFPDD in the treatment of influenza virus-mediated injury. The HIF-α level was significantly increased by PR8 infection. Both the knockdown of HIF-1α and treatment of the myocardial cell with QFPDD significantly reversed the increased inflammatory factors during infection. Overexpression of HIF-1α reversed the inhibition effects of QFPDD on cytokine expression. Meanwhile, seven compounds from QFPDD may target HIF-1α. Conclusion: QFPDD can ameliorate influenza virus-mediated myocardial damage by reducing the degree of cell necroptosis and apoptosis, inhibiting inflammatory response and the expression of HIF-1α. Thus, our results provide new insights into the treatment of respiratory virus-mediated myocardial damage.

Single-cell data revealed CD14-type and FCGR3A-type macrophages and relevant prognostic factors for predicting immunotherapy and prognosis in stomach adenocarcinoma

Background

Stomach adenocarcinoma (STAD) exhibits profound tumor heterogeneity and represents a great therapeutic challenge. Single-cell sequencing technology is a powerful tool to identify characteristic cell types.

Methods

Single-cell sequencing data (scRNA-seq) GSE167297 and bulk RNA-seq data from TCGA, GTEx, GSE26901 and GSE15459 database were included in this study. By downscaling and annotating the cellular data in scRNA-seq, critical cell types in tumor progression were identified by AUCell score. Relevant gene modules were then identified by weighted gene co-expression network analysis (WGCNA). A prognostic scoring system was constructed by identifying prognostic factors in STAD by Least absolute shrinkage and selection operator (LASSO) COX model. The prognosis and model performance in the RiskScore groups were measured by Kaplan-Meier (K-M) curves and Receiver operating characteristic (ROC) curves. Nomogram was drawn based on RiskScore and prognosis-related clinical factors. In addition, we evaluated patient's feedback on immunotherapy in the RiskScore groups by TIMER, ESTIMATE and TIDE analysis. Finally, the expression levels of prognostic factors were verified in gastric cancer cell lines (MKN7 and MKN28) and human normal gastric mucosal epithelial cells (GES-1), and the effects of prognostic factors on the viability of gastric cancer cells were examined by the CCK8 assay and cell cycle.

Results

scRNA-seq analysis revealed that 11 cell types were identified, and macrophages exhibited relatively higher AUCell scores and specifically expressed CD14 and FCGR3A. High macrophage scores worsened the prognosis of STAD patients. We intersected the specifically expressed genes in macrophages subgroups (670) and macrophage module genes (2,360) obtained from WGCNA analysis. Among 86 common genes, seven prognostic factors (RGS2, GNAI2, ANXA5, MARCKS, CD36, NRP1 and PDE4A) were identified and composed a RiskScore model. Patients in low Risk group showed a better survival advantage. Nomogram also provided a favorable prediction for survival at 1, 3 and 5 years in STAD patients. Besides, we found positive feedback to immunotherapy in patients with low RiskScore. The expression tendency of the seven prognostic factors in MKN7 and MKN28 was consistent with that in the RNA-seq data in addition to comparison of protein expression levels in the public HPA (The Human Protein Atlas) database. Further functional exploration disclosed that MARCKS was an important prognostic factor in regulating cell viability in STAD.

Conclusion

This study preliminary uncovered a single cell atlas for STAD patients, and Macrophages relevant gene signature and nomogram displayed favorable immunotherapy and prognostic prediction ability. Collectively, our work provides a new insight into the molecular mechanisms and therapeutic approach for LUAD patients.

CAP-RNAseq: an integrated pipeline for functional annotation and prioritization of co-expression clusters

Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq.

Single-cell Sequencing Data Reveals Aggressive CD68-type Macrophages and Prognostic Models in Bladder Cancer

BACKGROUND

The highly heterogeneous, complex pathological histology, and clinical phenotype in bladder cancer (BC) plague the prognostic management of BC to the present day.

METHODS

This study was conducted using single-cell sequencing data from the gene expression omnibus (GEO) database (GSE135337). A descending, annotated analysis was performed to identify the cell types contributing to BC aggressiveness. BC cell sequencing data from The Cancer Genome Atlas (TCGA) database were then combined with univariate, least absolute shrinkage and selection operator (LASSO), multivariate COX regression analysis to identify biomarkers of BC prognosis to construct a BC. We identified biomarkers of BC prognosis to construct a prognostic risk guidance system for BC. The feedback of patients in different risk strata to immunotherapy was analyzed. Finally, the regulation of prognostic genes on cancer cell activity was verified in vitro by Western blot and cell counting kit-8 (CCK8) assays.

RESULTS

Macrophages specifically expressing CD68 in BC were the cell type with the highest AUCell score, and CD68 was the biomarker of Tumor-associated macrophages (TAMs). CD68 macrophages were potentially the critical cell type in the aggressive BC subtype. Through univariate, LASSO, multivariate COX-based regression analysis. CTSS, GMFG, ANXA5, GSN, SLC2A3, and FTL were authenticated as prognostic biomarkers (p < 0.05) and composed the Risk Score. Patients in the low-risk group showed an excellent survival advantage (p < 0.01) and immunotherapy feedback. Additionally, inhibition of GSN expression decreased EMT activity to inhibit bladder cancer cell viability.

CONCLUSION

In conclusion, this study provided feedback on the immune cell types associated with aggressiveness in BC. Importantly, a prognostic management system for BC was created based on the genes involved, providing more insight into the aggressive pathological phenotype as well as the prognosis of BC.

Taxifolin Inhibits the Growth of Non-Small-Cell Lung Cancer via Downregulating Genes Displaying Novel and Robust Associations with Immune Evasion Factors

Using an LL2 cell-based syngeneic mouse LC model, taxifolin suppressed allografts along with the appearance of 578 differentially expressed genes (DEGs). These DEGs were associated with enhancement of processes related to the extracellular matrix and lymphocyte chemotaxis as well as the reduction in pathways relevant to cell proliferation. From these DEGs, we formulated 12-gene (TxflSig) and 7-gene (TxflSig1) panels; both predicted response to ICB (immune checkpoint blockade) therapy more effectively in non-small-cell lung cancer (NSCLC) than numerous well-established ICB biomarkers, including PD-L1. In both panels, the mouse counterparts of ITGAL, ITGAX, and TMEM119 genes were downregulated by taxifolin. They were strongly associated with immune suppression in LC, evidenced by their robust correlations with the major immunosuppressive cell types (MDSC, Treg, and macrophage) and multiple immune checkpoints in NSCLC and across multiple human cancer types. ITGAL, ITGAX, and IIT (ITGAL-ITGAX-TMEM119) effectively predicted NSCLC's response to ICB therapy; IIT stratified the mortality risk of NSCLC. The stromal expressions of ITGAL and ITGAX, together with tumor expression of TMEM119 in NSCLC, were demonstrated. Collectively, we report multiple novel ICB biomarkers-TxflSig, TxflSig1, IIT, ITGAL, and ITGAX-and taxifolin-derived attenuation of immunosuppressive activities in NSCLC, suggesting the inclusion of taxifolin in ICB therapies for NSCLC.

Unraveling dynamic interactions between tumor-associated macrophages and consensus molecular subtypes in colorectal cancer: An integrative analysis of single-cell and bulk RNA transcriptome

Background

Accumulating research substantiated that tumor-associated macrophages (TAMs) have a significant impact on the tumorigenesis, progression, and distant metastasis, representing a novel target for various cancers. However, the underlying dynamic changes and interactions between TAMs and tumor cells remain largely elusive in colorectal cancer (CRC).

Methods

We depicted the dynamic changes of macrophages using sing-cell RNA-seq data and extracted TAM differentiation-related genes. Next, we utilized the weighted gene co-expression network analysis (WGCNA) to acquire CMS-related modular genes using bulk RNA-seq data. Finally, we utilized univariate Cox and Lasso Cox regression analyses to identify TAM differentiation-related biomarkers and established a novel risk signature model. We employed quantitative real-time polymerase chain reaction (qRT-PCR) on CRC tissue samples and used immunohistochemistry (IHC) data frome the HPA database to validate the mRNA and protein expression of prognostic genes. The interaction of TAMs and each consensus molecular subtype (CMS) subpopulation was analyzed at the cellular level.

Results

A total of 47,285 cells from single-cell dataset and 1197 CRC patients from bulk dataset were obtained. Among those, 6400 myeloid cells were re-clustered and annotated. RNASE1, F13A1, DAPK1, CLEC10A, RPN2, REG4 and RGS19 were identified as prognostic genes and the risk signature model was established based on the above genes. The qRT-PCR analysis indicated that the expression of RNASE1 and DAPK1 were significantly up-regulated in CRC tumor tissues. The cell-cell communication analysis demonstrated complex interactions between TAMs and CMS malignant cell subpopulations.

Conclusion

This study presents an in-depth dissection of the dynamic features of TAMs in the tumor microenvironment and provides promising therapeutic targets for CRC.

Taxifolin Inhibits Breast Cancer Growth by Facilitating CD8+ T Cell Infiltration and Inducing a Novel Set of Genes including Potential Tumor Suppressor Genes in 1q21.3

Taxifolin inhibits breast cancer (BC) via novel mechanisms. In a syngeneic mouse BC model, taxifolin suppressed 4T-1 cell-derived allografts. RNA-seq of 4T-1 tumors identified 36 differentially expressed genes (DEGs) upregulated by taxifolin. Among their human homologues, 19, 7, and 2 genes were downregulated in BCs, high-proliferative BCs, and BCs with high-fatality risks, respectively. Three genes were established as tumor suppressors and eight were novel to BC, including HNRN, KPRP, CRCT1, and FLG2. These four genes exhibit tumor suppressive actions and reside in 1q21.3, a locus amplified in 70% recurrent BCs, revealing a unique vulnerability of primary and recurrent BCs with 1q21.3 amplification with respect to taxifolin. Furthermore, the 36 DEGs formed a multiple gene panel (DEG36) that effectively stratified the fatality risk in luminal, HER2+, and triple-negative (TN) equivalent BCs in two large cohorts: the METABRIC and TCGA datasets. 4T-1 cells model human TNBC cells. The DEG36 most robustly predicted the poor prognosis of TNBCs and associated it with the infiltration of CD8+ T, NK, macrophages, and Th2 cells. Of note, taxifolin increased the CD8+ T cell content in 4T-1 tumors. The DEG36 is a novel and effective prognostic biomarker of BCs, particularly TNBCs, and can be used to assess the BC-associated immunosuppressive microenvironment.

Gene S-phase kinase associated protein 2 is a novel prognostic marker in human neoplasms

BACKGROUND

Neoplasms are a series of diseases affecting human health. Prognostic and tumor status-related markers for various tumors should be identified.

METHODS

Based on 19,515 samples from multiple sources, for the first time, this study provided an overview of gene S-phase kinase associated protein 2 (SKP2) in pan-cancer. Differential SKP2 expression in multiple comparison groups was identified by the Kruskal-Wallis test and Wilcoxon rank-sum test. The prognosis significance of SKP2 in individuals with neoplasm was evaluated through univariate Cox regression analysis and Kaplan-Meier curves. The area under the curve was utilized to detect the accuracy of SKP2 in predicting cancer status. Spearman's rank correlation coefficients were calculated in all correlation analyses. Gene set enrichment analysis was used to identify essential signaling pathways of SKP2 in human neoplasms.

RESULTS

The study disclosed the upregulated SKP2 expression in 15 neoplasms and decreased SKP2 expression in three cancers (p < 0.05). The transcription factor Forkhead Box M1 may contribute to the increased expression levels of SKP2 in certain tumors. Over-expressed SKP2 represented a risk factor for the prognosis of most cancer patients (hazard ratio > 1, p < 0.05). SKP2 expression made it feasible to distinguish neoplasm and control tissues of 21 neoplasms (sensitivity = 0.79, specificity = 0.87, area under the curve = 0.90), implying its potential in screening a series of neoplasms. Further, the research revealed the close association of SKP2 expression with DNA methyltransferases, mismatch repair genes, microsatellite instability, tumor mutational burden, neoantigen count, and immunity.

CONCLUSIONS

SKP2 plays an essential role in multiple neoplasms and may serve as a marker for treating and identifying these neoplasms.

Copper-related genes predict prognosis and characteristics of breast cancer

Background

The role of copper in cancer treatment is multifaceted, with copper homeostasis-related genes associated with both breast cancer prognosis and chemotherapy resistance. Interestingly, both elimination and overload of copper have been reported to have therapeutic potential in cancer treatment. Despite these findings, the exact relationship between copper homeostasis and cancer development remains unclear, and further investigation is needed to clarify this complexity.

Methods

The pan-cancer gene expression and immune infiltration analysis were performed using the Cancer Genome Atlas Program (TCGA) dataset. The R software packages were employed to analyze the expression and mutation status of breast cancer samples. After constructing a prognosis model to separate breast cancer samples by LASSO-Cox regression, we examined the immune statement, survival status, drug sensitivity and metabolic characteristics of the high- and low-copper related genes scoring groups. We also studied the expression of the constructed genes using the human protein atlas database and analyzed their related pathways. Finally, copper staining was performed with the clinical sample to investigate the distribution of copper in breast cancer tissue and paracancerous tissue.

Results

Pan-cancer analysis showed that copper-related genes are associated with breast cancer, and the immune infiltration profile of breast cancer samples is significantly different from that of other cancers. The essential copper-related genes of LASSO-Cox regression were ATP7B (ATPase Copper Transporting Beta) and DLAT (Dihydrolipoamide S-Acetyltransferase), whose associated genes were enriched in the cell cycle pathway. The low-copper related genes scoring group presented higher levels of immune activation, better probabilities of survival, enrichment in pathways related to pyruvate metabolism and apoptosis, and higher sensitivity to chemotherapy drugs. Immunohistochemistry staining showed high protein expression of ATP7B and DLAT in breast cancer samples. The copper staining showed copper distribution in breast cancer tissue.

Conclusion

This study displayed the potential impacts of copper-related genes on the overall survival, immune infiltration, drug sensitivity and metabolic profile of breast cancer, which could predict patients' survival and tumor statement. These findings may serve to support future research efforts aiming at improving the management of breast cancer.

Identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma

Immunogenic cell death (ICD) is the trigger of adaptive immune responses. However, the role of ICD-related genes in clear cell renal carcinoma (ccRCC) remains unclear. We aimed to identify biomarkers associated with ICD and develop an ICD-related predictive model that predicts the immune microenvironment, prognosis, and response to immunotherapy in ccRCC. Our study included 739 patients (603 in the training set and 136 in the validation set) with clinicopathologic information and transcriptome sequencing data. Consensus clustering, principal component analysis (PCA), weighted gene co-expression network analysis (WGCNA), univariate COX analysis, multivariate COX analysis, and the Lasso-Cox algorithm were applied to shrink predictors and construct a predictive signature of overall survival (OS). We used CIBERSORT, ESTIMATE, and TIMER in the R package IOBR to evaluate the tumor microenvironment and immune infiltration pattern of each sample. Finally, the single cell sequencing results of immune cells in ccRCC were used to verify the results of immune infiltration analysis, and the performance of the prognostic model was evaluated by calibration curves and c-index. This study revealed that inability of the initial immune response and primary immunodeficiency were significantly enriched in the ICD subgroup with poor prognosis. We found that the ten candidate ICD genes (CALR, ENTPD1, FOXP3, HSP90AA1, IFNB1, IFNG, IL6, LY96, PIK3CA, and TLR4) could affect the prognosis of ccRCC (p < 0.05). The prediction model (PRE) we constructed can not only predict the long-term survival probability but also evaluate the landscape of immune infiltration in ccRCC. Our study demonstrated that low infiltration of dendritic cells in ccRCC implies a poor prognosis, whereas the degree of CTL infiltration is less important. An individualized prediction model was created to predict the 1-, 2-, 3-, and 5-year survival and responsiveness of ccRCC patients to immunotherapy, which may serve as a potent tool for clinicians to make better treatment decisions and thus improve the overall survival (OS) of ccRCC patients in the future.

A novel risk model of three SUMOylation genes based on RNA expression for potential prognosis and treatment sensitivity prediction in kidney cancer

Introduction: Kidney cancer is one of the most common and lethal urological malignancies. Discovering a biomarker that can predict prognosis and potential drug treatment sensitivity is necessary for managing patients with kidney cancer. SUMOylation is a type of posttranslational modification that could impact many tumor-related pathways through the mediation of SUMOylation substrates. In addition, enzymes that participate in the process of SUMOylation can also influence tumorigenesis and development. Methods: We analyzed the clinical and molecular data which were obtanied from three databases, The Cancer Genome Atlas (TCGA), the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC), and ArrayExpress. Results: Through analysis of differentially expressed RNA based on the total TCGA-KIRC cohort, it was found that 29 SUMOylation genes were abnormally expressed, of which 17 genes were upregulated and 12 genes were downregulated in kidney cancer tissues. A SUMOylation risk model was built based on the discovery TCGA cohort and then validated successfully in the validation TCGA cohort, total TCGA cohort, CPTAC cohort, and E-TMAB-1980 cohort. Furthermore, the SUMOylation risk score was analyzed as an independent risk factor in all five cohorts, and a nomogram was constructed. Tumor tissues in different SUMOylation risk groups showed different immune statuses and varying sensitivity to the targeted drug treatment. Discussion: In conclusion, we examined the RNA expression status of SUMOylation genes in kidney cancer tissues and developed and validated a prognostic model for predicting kidney cancer outcomes using three databases and five cohorts. Furthermore, the SUMOylation model can serve as a biomarker for selecting appropriate therapeutic drugs for kidney cancer patients based on their RNA expression.

SARS-CoV-2-correlated ASGR1 is a novel potential marker for the treatment and identification of multiple human cancers

OBJECTIVES

Cancer patients are reported to be more susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the COVID-19 (the Corona Virus Disease 2019) patients with cancer suffer from certain serious complications. ASGR1 has been recently identified as a novel receptor of SARS-CoV-2 in human cells; however, there are limited studies on ASGR1 in various human cancers.

METHODS

This study utilized a comprehensive analysis of COVID-19-related ASGR1 in multiple human cancers based on 18,589 multi-center samples. Using Wilcoxon rank-sum analysis, a difference in ASGR1 expression between cancer and control tissues was detected. Cox regression analysis, Kaplan-Meier curves, and receiver operating characteristic curves were utilized to determine the correlation between ASGR1 expression and the clinical parameters of cancer patients. The immune relevance and potential mechanisms of ASGR1 in various cancers were also investigated.

RESULTS

Abnormal ASGR1 mRNA expression was observed in 16 of 20 different cancers (e.g., it was upregulated in colon adenocarcinoma but downregulated in cholangiocarcinoma; P < 0.05). ASGR1 was related to prognosis, e.g., overall survival, in 14 cancers (P < 0.05), such as adrenocortical carcinoma. The gene was also found to be a potential marker that can be utilized to distinguish eleven cancers from controls with moderate to high accuracy (e.g., the area under the curve for cholangiocarcinoma = 1.000). ASGR1 expression was related to DNA methyltransferases, mismatch repair genes, immune checkpoints, levels of tumor mutational burden, microsatellite instability, neoantigen count, and immune infiltration levels in certain cancers (P < 0.05). The gene plays a role in multiple cancers by affecting four signaling pathways, such as cytokine-cytokine receptor interaction. Cancer patients with high ASGR1 expression are sensitive to 25 drugs, including ulixertinib.

CONCLUSIONS

SARS-CoV-2-correlated ASGR1 is a novel marker that can be used for treating and identifying multiple human cancers.

Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2-host protein-protein interaction network

The ongoing coronavirus disease 2019 (COVID-19) pandemic has highlighted the need to better understand virus-host interactions. We developed a network-based method that expands the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-host protein interaction network and identifies host targets that modulate viral infection. To disrupt the SARS-CoV-2 interactome, we systematically probed for potent compounds that selectively target the identified host proteins with high expression in cells relevant to COVID-19. We experimentally tested seven chemical inhibitors of the identified host proteins for modulation of SARS-CoV-2 infection in human cells that express ACE2 and TMPRSS2. Inhibition of the epigenetic regulators bromodomain-containing protein 4 (BRD4) and histone deacetylase 2 (HDAC2), along with ubiquitin-specific peptidase (USP10), enhanced SARS-CoV-2 infection. Such proviral effect was observed upon treatment with compounds JQ1, vorinostat, romidepsin and spautin-1, when measured by cytopathic effect and validated by viral RNA assays, suggesting that the host proteins HDAC2, BRD4 and USP10 have antiviral functions. We observed marked differences in antiviral effects across cell lines, which may have consequences for identification of selective modulators of viral infection or potential antiviral therapeutics. While network-based approaches enable systematic identification of host targets and selective compounds that may modulate the SARS-CoV-2 interactome, further developments are warranted to increase their accuracy and cell-context specificity.

Integrated multi-omics analysis identifies CD73 as a prognostic biomarker and immunotherapy response predictor in head and neck squamous cell carcinoma

BACKGROUND

Advances in tumor immunotherapy have been developed for patients with advanced recurrent or metastatic (R/M) HNSCC. However, the response of most HNSCC patients to immune checkpoint inhibitors (ICI) remains unsatisfactory. CD73 is a promising target for tumor immunotherapy, but its role in HNSCC remains insufficient. In this study, we aim to explore the function of CD73 in HNSCC.

METHODS

Transcriptomic and clinical data of TCGA-HNSC were downloaded from UCSC Xena for analysis of CD73 mRNA expression and prognosis. Immunohistochemical assay were performed to validate the expression of CD73 in tumor tissues and its relationship with CD8+ T cells. GSEA analysis was performed with the "clusterProfiler" R package. Immune infiltration analysis was calculated with ESTIMATE, CIBERSORT and MCP-counter algorithms. Single-cell transcriptomic data was originated from GSE103322. Cell clustering, annotation and CD73 expression were from the TISCH database. Correlation data between CD73 and tumor signatures were obtained from the CancerSEA database. Somatic mutation data were obtained from TCGA-HNSC and analyzed by "maftools" R package. Immune efficacy prediction was performed using TIDE algorithm and validated with the IMvigor210 cohort.

RESULTS

Compared with normal tissues, both mRNA and protein expressions of CD73 were elevated in tumor tissues (P = 9.7×10-10, P = 7.6×10-5, respectively). Kaplan-Meier analysis revealed that patients with high expression of CD73 had worse overall survival (log-rank P = 0.0094), and CD73 could be used as a diagnostic factor for HNSCC (AUC = 0.778). Both bulk RNA-seq and single-cell RNA-seq analysis showed that high CD73 expression can promote EMT and metastasis, samples with high CD73 expression had reduced CD8+ T cells. Furthermore, it was found that CD73-high group was more prone to have mutations in TP53, HRAS and CDKN2A, and were negatively correlated with TMB (P = 0.0055) and MSI (P = 0.00034). Mutational signature analysis found that CD73 was associated with APOBEC signature. Immunotherapy efficacy analysis showed that CD73-high group was less sensitive to immune efficacy.

CONCLUSIONS

Our results demonstrate that CD73 has an inhibitory effect on the tumor microenvironment, and is more likely to be unresponsive to ICI therapy. Collectively, targeting CD73 may provide new insights for tumor targeted therapy and/or immunotherapy.

Integrated analysis of bulk and single-cell RNA-seq reveals the role of MYC signaling in lung adenocarcinoma

MYC is one of the well-known oncogenes, and its important role in cancer still remains largely unknown. We obtained lung adenocarcinoma (LUAD) multi-omics data including genome, transcriptome, and single-cell sequencing data from multiple cohorts. We calculated the GSVA score of the MYC target v1 using the ssGSEA method, and obtained the genes highly correlated with this score by Spearman correlation analysis. Subsequent hierarchical clustering divided these genes into two gene sets highly associated with MYC signaling (S1 and S2). Unsupervised clustering based on these genes divided the LUAD samples into two distinct subgroups, namely, the MYC signaling inhibition group (C1) and activation group (C2). The MCP counter package in R was used to assess tumor immune cell infiltration abundance and ssGSEA was used to calculate gene set scores. The scRNA-seq was used to verify the association of MYC signaling to cell differentiation. We observed significant differences in prognosis, clinical characteristics, immune microenvironment, and genomic alterations between MYC signaling inhibition and MYC signaling activation groups. MYC-signaling is associated with genomic instability and can mediate the immunosuppressive microenvironment and promote cell proliferation, tumor stemness. Moreover, MYC-signaling activation is also subject to complex post-transcriptional regulation and is highly associated with cell differentiation. In conclusion, MYC signaling is closely related to the genomic instability, genetic alteration and regulation, the immune microenvironment landscape, cell differentiation, and disease survival in LUAD. The findings of this study provide a valuable reference to revealing the mechanism of cancer-promoting action of MYC in LUAD.

Telomere-related gene risk model for prognosis and drug treatment efficiency prediction in kidney cancer

Kidney cancer is one of the most common urological cancers worldwide, and kidney renal clear cell cancer (KIRC) is the major histologic subtype. Our previous study found that von-Hippel Lindau (VHL) gene mutation, the dominant reason for sporadic KIRC and hereditary kidney cancer-VHL syndrome, could affect VHL disease-related cancers development by inducing telomere shortening. However, the prognosis role of telomere-related genes in kidney cancer has not been well discussed. In this study, we obtained the telomere-related genes (TRGs) from TelNet. We obtained the clinical information and TRGs expression status of kidney cancer patients in The Cancer Genome Atlas (TCGA) database, The International Cancer Genome Consortium (ICGC) database, and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Totally 353 TRGs were differential between tumor and normal tissues in the TCGA-KIRC dataset. The total TCGA cohort was divided into discovery and validation TCGA cohorts and then using univariate cox regression, lasso regression, and multivariate cox regression method to conduct data analysis sequentially, ten TRGs (ISG15, RFC2, TRIM15, NEK6, PRKCQ, ATP1A1, ELOVL3, TUBB2B, PLCL1, NR1H3) risk model had been constructed finally. The kidney patients in the high TRGs risk group represented a worse outcome in the discovery TCGA cohort (p<0.001), and the result was validated by these four cohorts (validation TCGA cohort, total TCGA cohort, ICGC cohort, and CPTAC cohort). In addition, the TRGs risk score is an independent risk factor for kidney cancer in all these five cohorts. And the high TRGs risk group correlated with worse immune subtypes and higher tumor mutation burden in cancer tissues. In addition, the high TRGs risk group might benefit from receiving immune checkpoint inhibitors and targeted therapy agents. Moreover, the proteins NEK6, RF2, and ISG15 were upregulated in tumors both at the RNA and protein levels, while PLCL1 and PRKCQ were downregulated. The other five genes may display the contrary expression status at the RNA and protein levels. In conclusion, we have constructed a telomere-related genes risk model for predicting the outcomes of kidney cancer patients, and the model may be helpful in selecting treatment agents for kidney cancer patients.

Violacein switches off low molecular weight tyrosine phosphatase and rewires mitochondria in colorectal cancer cells

In the last decade, emerging evidence has shown that low molecular weight protein tyrosine phosphatase (LMWPTP) not only contributes to the progression of cancer but is associated with prostate low survival rate and colorectal cancer metastasis. We report that LMWPTP favors the glycolytic profile in some tumors. Therefore, the focus of the present study was to identify metabolic enzymes that correlate with LMWPTP expression in patient samples. Exploratory data analysis from RNA-seq, proteomics, and histology staining, confirmed the higher expression of LMWPTP in CRC. Our descriptive statistical analyses indicate a positive expression correlation between LMWPTP and energy metabolism enzymes such as acetyl-CoA carboxylase (ACC) and fatty acid synthase (FASN). In addition, we examine the potential of violacein to reprogram energetic metabolism and LMWPTP activity. Violacein treatment induced a shift of glycolytic to oxidative metabolism associated with alteration in mitochondrial efficiency, as indicated by higher oxygen consumption rate. Particularly, violacein treated cells displayed higher proton leak and ATP-linked oxygen consumption rate (OCR) as an indicator of the OXPHOS preference. Notably, violacein is able to bind and inhibit LMWPTP. Since the LMWPTP acts as a hub of signaling pathways that offer tumor cells invasive advantages, such as survival and the ability to migrate, our findings highlight an unexplored potential of violacein in circumventing the metabolic plasticity of tumor cells.

Comprehensive Computational Analysis of Honokiol Targets for Cell Cycle Inhibition and Immunotherapy in Metastatic Breast Cancer Stem Cells

Breast cancer stem cells (BCSCs) play a critical role in chemoresistance, metastasis, and poor prognosis of breast cancer. BCSCs are mostly dormant, and therefore, activating them and modulating the cell cycle are important for successful therapy against BCSCs. The tumor microenvironment (TME) promotes BCSC survival and cancer progression, and targeting the TME can aid in successful immunotherapy. Honokiol (HNK), a bioactive polyphenol isolated from the bark and seed pods of Magnolia spp., is known to exert anticancer effects, such as inducing cell cycle arrest, inhibiting metastasis, and overcoming immunotherapy resistance in breast cancer cells. However, the molecular mechanisms of action of HNK in BCSCs, as well as its effects on the cell cycle, remain unclear. This study aimed to explore the potential targets and molecular mechanisms of HNK on metastatic BCSC (mBCSC)-cell cycle arrest and the impact of the TME. Using bioinformatics analyses, we predicted HNK protein targets from several databases and retrieved the genes differentially expressed in mBCSCs from the GEO database. The intersection between the differentially expressed genes (DEGs) and the HNK-targets was determined using a Venn diagram, and the results were analyzed using a protein-protein interaction network, hub gene selection, gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, genetic alteration analysis, survival rate, and immune cell infiltration levels. Finally, the interaction between HNK and two HNK-targets regulating the cell cycle was analyzed using molecular docking analysis. The identified potential therapeutic targets of HNK (PTTH) included CCND1, SIRT2, AURKB, VEGFA, HDAC1, CASP9, HSP90AA1, and HSP90AB1, which can potentially inhibit the cell cycle of mBCSCs. Moreover, our results showed that PTTH could modulate the PI3K/Akt/mTOR and HIF1/NFkB/pathways. Overall, these findings highlight the potential of HNK as an immunotherapeutic agent for mBCSCs by modulating the tumor immune environment.

Development and Validation of Prognostic Model for Lung Adenocarcinoma Patients Based on m6A Methylation Related Transcriptomics

Existing studies suggest that m⁶A methylation is closely related to the prognosis of cancer. We developed three prognostic models based on m⁶A-related transcriptomics in lung adenocarcinoma patients and performed external validations. The TCGA-LUAD cohort served as the derivation cohort and six GEO data sets as external validation cohorts. The first model (mRNA model) was developed based on m⁶A-related mRNA. LASSO and stepwise regression were used to screen genes and the prognostic model was developed from multivariate Cox regression model. The second model (lncRNA model) was constructed based on m⁶A related lncRNAs. The four steps of random survival forest, LASSO, best subset selection and stepwise regression were used to screen genes and develop a Cox regression prognostic model. The third model combined the risk scores of the first two models with clinical variable. Variables were screened by stepwise regression. The mRNA model included 11 predictors. The internal validation C index was 0.736. The lncRNA model has 15 predictors. The internal validation C index was 0.707. The third model combined the risk scores of the first two models with tumor stage. The internal validation C index was 0.794. In validation sets, all C-indexes of models were about 0.6, and three models had good calibration accuracy. Freely online calculator on the web at https://lhj0520.shinyapps.io/LUAD_prediction_model/.

Pan-Cancer Analysis, Reveals COVID-19-Related BSG as a Novel Marker for Treatment and Identification of Multiple Human Cancers

Background: Coronavirus disease 2019 (COVID-19) has been a public threat and healthcare concern caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. During the period of the pandemic of COVID-19, cancer patients should be paid more attention as more severe events are found in cancer patients infected with SARS-CoV-2. Basigin (BSG) is an essential factor for the infection and progression of COVID-19 and tumorigenesis of multiple tumors, which may serve as a novel target for the effective treatment against COVID-19 and multiple human cancers.

Methods: A total of 19,020 samples from multiple centers were included in our research for the comprehensive investigation of the differences in BSG expression among human organs, cancer cells, cancer tissues, and normal tissues. Cox regression analysis and Kaplan–Meier curves were utilized to explore the prognosis factor of BSG in cancers. Correlation analyses were used to determine associations of BSG expression with tumor mutational burden, the immune microenvironment, etc. Gene set enrichment analysis was applied to explore the underlying mechanisms of BSG in cancers.

Results: Compared with normal tissues, BSG expression was high in 13 types of cancers (cholangiocarcinoma, etc.) and low in colon adenocarcinoma and rectum adenocarcinoma. BSG expression was related to the prognosis of eight cancers (e.g., invasive breast carcinoma) (p &lt; 0.05). The gene also demonstrated a pronounced effect in identifying 12 cancers (cholangiocarcinoma, etc.) from their control samples (AUC &gt;0.7). The BSG expression was associated with DNA methyltransferases, mismatch repair genes, immune infiltration levels, tumor mutational burden, microsatellite instability, neoantigen, and immune checkpoints, suggesting the potential of BSG as an exciting target for cancer treatment. BSG may play its role in several cancers by affecting several signaling pathways such as drug cytochrome metabolism P450 and JAK-STAT.

Conclusion:BSG may be a novel biomarker for treating and identifying multiple human cancers.

Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population

Objective

Gastric cancer (GC) is one of the most common tumour diseases worldwide and has poor survival, especially in the Asian population. Exploration based on biomarkers would be efficient for better diagnosis, prediction, and targeted therapy.

Methods

Expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Survival-related genes were identified by gene set enrichment analysis (GSEA) and univariate Cox. Then, we applied a Bayesian hierarchical lasso Cox model for prognostic signature screening. Protein-protein interaction and Spearman analysis were performed. Kaplan–Meier and receiver operating characteristic (ROC) curve analysis were applied to evaluate the prediction performance. Multivariate Cox regression was used to identify prognostic factors, and a prognostic nomogram was constructed for clinical application.

Results

With the Bayesian lasso Cox model, a 9-gene signature included TNFRSF11A, NMNAT1, EIF5A, NOTCH3, TOR2A, E2F8, PSMA5, TPMT, and KIF11 was established to predict overall survival in GC. Protein-protein interaction analysis indicated that E2F8 was likely related to KIF11. Kaplan-Meier analysis showed a significant difference between the high-risk and low-risk groups (P<0.001). Multivariate analysis demonstrated that the 9-gene signature was an independent predictor (HR = 2.609, 95% CI 2.017–3.370), and the C-index of the integrative model reached 0.75. Function enrichment analysis for different risk groups revealed the most significant enrichment pathway/term, including pyrimidine metabolism and respiratory electron transport chain.

Conclusion

Our findings suggested that a novel prognostic model based on a 9-gene signature was developed to predict GC patients in high-risk and improve prediction performance. We hope our model could provide a reference for risk classification and clinical decision-making.

Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method

Indication expansion aims to find new indications for existing targets in order to accelerate the process of launching a new drug for a disease on the market. The rapid increase in data types and data sources for computational drug discovery has fostered the use of semantic knowledge graphs (KGs) for indication expansion through target centric approaches, or in other words, target repositioning. Previously, we developed a novel method to construct a KG for indication expansion studies, with the aim of finding and justifying alternative indications for a target gene of interest. In contrast to other KGs, ours combines human-curated full-text literature and gene expression data from biomedical databases to encode relationships between genes, diseases, and tissues. Here, we assessed the suitability of our KG for explainable target-disease link prediction using a glass-box approach. To evaluate the predictive power of our KG, we applied shortest path with tissue information- and embedding-based prediction methods to a graph constructed with information published before or during 2010. We also obtained random baselines by applying the shortest path predictive methods to KGs with randomly shuffled node labels. Then, we evaluated the accuracy of the top predictions using gene-disease links reported after 2010. In addition, we investigated the contribution of the KG’s tissue expression entity to the prediction performance. Our experiments showed that shortest path-based methods significantly outperform the random baselines and embedding-based methods outperform the shortest path predictions. Importantly, removing the tissue expression entity from the KG severely impacts the quality of the predictions, especially those produced by the embedding approaches. Finally, since the interpretability of the predictions is crucial in indication expansion, we highlight the advantages of our glass-box model through the examination of example candidate target-disease predictions.

TMEM2 Combined with IDH and 1p19q in Refining Molecular Subtypes for Predicting Survival of Patients with Glioma

Gliomas are common intracranial tumors with high morbidity and mortality in adults. Transmembrane protein 2 (TMEM2) is involved in the malignant behavior of solid tumors. TMEM2 regulates cell adhesion and metastasis as well as intercellular communication by degrading nonprotein components of the extracellular matrix. This study aimed to evaluate the relationship between TMEM2 expression levels and glioma subtypes or patient prognosis. Our findings revealed that TMEM2 expression was abnormally upregulated in high-grade glioma. Moreover, combining TMEM2, the status of isocitrate dehydrogenase (IDH) and 1p19q, we subdivided molecular subtypes with significant differences in survival. Patients in the MT-codel-low subgroup had better prognosis than those in the WT-no-codel-high subgroup, who fared the worst. Additionally, correlation analysis of TMEM2 and immune cell infiltration indicated an altered tumor microenvironment (TME) and cell redistribution in the TMEM2 high-expression subtype. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that focal adhesion and PI3K-Akt signaling pathways were enriched in the TMEM2-expressing group. In conclusion, aberrant TMEM2 expression can be used as an independent prognostic marker for refining glioma molecular subtyping and accurate prognosis. These findings will improve rational decision making to provide individualized therapy for patients with glioma.

Proteo-transcriptomics meta-analysis identifies SUMO2 as a promising target in glioblastoma multiforme therapeutics

Background

Glioblastoma multiforme (GBM) is a deadly brain tumour with minimal survival rates due to the ever-expanding heterogeneity, chemo and radioresistance. Kinases are known to crucially drive GBM pathology; however, a rationale therapeutic combination that can simultaneously inhibit multiple kinases has not yet emerged successfully.

Results

Here, we analyzed the GBM patient data from several publicly available repositories and deduced hub GBM kinases, most of which were identified to be SUMOylated by SUMO2/3 isoforms. Not only the hub kinases but a significant proportion of GBM upregulated genes involved in proliferation, metastasis, invasion, epithelial-mesenchymal transition, stemness, DNA repair, stromal and macrophages maintenance were also identified to be the targets of SUMO2 isoform. Correlatively, high expression of SUMO2 isoform was found to be significantly associated with poor patient survival.

Conclusions

Although many natural products and drugs are evidenced to target general SUMOylation, however, our meta-analysis strongly calls for the need to design SUMO2/3 or even better SUMO2 specific inhibitors and also explore the SUMO2 transcription inhibitors for universally potential, physiologically non-toxic anti-GBM drug therapy.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02279-y.

The major highlights of this study are as follows:

Key upregulated hub kinases and coding genes in GBM are found to be targets of SUMO2 conjugation.

SUMO2 is significantly expressed in adult primary and recurrent GBMs as well as in pediatric GBM tumours.

Orthotropic xenografts from adult and pediatric GBMs confirm high expression of SUMO2 in GBM tumour samples.

SUMO2 is significantly associated with patient survival plot and pan-cancer cell fitness.

Rationale design of SUMO2 inhibitors or search for its transcriptional inhibitors is urgently required through industry-academia collaboration for an anti-GBM and potentially pan-cancer therapeutics.

Identification and analysis of genes associated with lung adenocarcinoma by integrated bioinformatics methods

Lung adenocarcinoma (LUAD) is one of the most common forms of lung cancer, with a very high mortality rate. Although the treatments available for LUAD have become more effective in recent years, significant improvement is still needed. Advances in sequencing technologies and bioinformatics analysis have enabled new approaches to be developed for identifying drug targets. In this work we utilized data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify hub genes related to LUAD through Weighted Gene Correlation Network Analysis (WGCNA) and other bioinformatics methods, with the goal of identifying new drug targets for cancer treatment.

Coupling of autism genes to tissue-wide expression and dysfunction of synapse, calcium signalling and transcriptional regulation

Autism Spectrum Disorder (ASD) is a heterogeneous disorder that is often accompanied with many co-morbidities. Recent genetic studies have identified various pathways from hundreds of candidate risk genes with varying levels of association to ASD. However, it is unknown which pathways are specific to the core symptoms or which are shared by the co-morbidities. We hypothesised that critical ASD candidates should appear widely across different scoring systems, and that comorbidity pathways should be constituted by genes expressed in the relevant tissues. We analysed the Simons Foundation for Autism Research Initiative (SFARI) database and four independently published scoring systems and identified 292 overlapping genes. We examined their mRNA expression using the Genotype-Tissue Expression (GTEx) database and validated protein expression levels using the human protein atlas (HPA) dataset. This led to clustering of the overlapping ASD genes into 2 groups; one with 91 genes primarily expressed in the central nervous system (CNS geneset) and another with 201 genes expressed in both CNS and peripheral tissues (CNS+PT geneset). Bioinformatic analyses showed a high enrichment of CNS development and synaptic transmission in the CNS geneset, and an enrichment of synapse, chromatin remodelling, gene regulation and endocrine signalling in the CNS+PT geneset. Calcium signalling and the glutamatergic synapse were found to be highly interconnected among pathways in the combined geneset. Our analyses demonstrate that 2/3 of ASD genes are expressed beyond the brain, which may impact peripheral function and involve in ASD co-morbidities, and relevant pathways may be explored for the treatment of ASD co-morbidities.

HPAStainR: a Bioconductor and Shiny app to query protein expression patterns in the Human Protein Atlas

The Human Protein Atlas is a website of protein expression in human tissues. It is an excellent resource of tissue and cell type protein localization, but only allows the query of a single protein at a time. We introduce HPAStainR as a new Shiny app and Bioconductor/R package used to query the scored staining patterns in the Human Protein Atlas with multiple proteins/genes of interest. This allows the user to determine if an experimentally-generated protein/gene list associates with a particular cell type. We validated the tool using the Panglao Database cell type specific marker genes and a Genotype Expression (GTEx) tissue deconvolution dataset.  HPAStainR identified 92% of the Panglao cell types in the top quartile of confidence scores limited to tissue type of origin results. It also appropriately identified the correct cell types from the GTEx dataset. HPAStainR fills a gap in available bioinformatics tools to identify cell type protein expression patterns and can assist in establishing ground truths and exploratory analysis. HPAStainR is available from: https://32tim32.shinyapps.io/HPAStainR/

Prognostic Score-based Clinical Factors and Metabolism-related Biomarkers for Predicting the Progression of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor representing more than 90% of primary liver cancer. This study aimed to identify metabolism-related biomarkers with prognostic value by developing the novel prognostic score (PS) model. Transcriptomic profiles derived from TCGA and EBIArray databases were analyzed to identify differentially expressed genes (DEGs) in HCC tumor samples compared with normal samples. The overlapped genes between DEGs and metabolism-related genes (crucial genes) were screened and functionally analyzed. A novel PS model was constructed to identify optimal signature genes. Cox regression analysis was performed to identify independent clinical factors related to prognosis. Nomogram model was constructed to estimate the predictability of clinical factors. Finally, protein expression of crucial genes was explored in different cancer tissues and cell types from the Human Protein Atlas (HPA). We screened a total of 305 overlapped genes (differentially expressed metabolism-related genes). These genes were mainly involved in "oxidation reduction," "steroid hormone biosynthesis," "fatty acid metabolic process," and "linoleic acid metabolism." Furthermore, we screened ten optimal DEGs (CYP2C9, CYP3A4, and TKT, among others) by using the PS model. Two clinical factors of pathologic stage (P < .001, HR: 1.512 [1.219-1.875]) and PS status (P <.001, HR: 2.259 [1.522-3.354]) were independent prognostic predictors by cox regression analysis. Nomogram model showed a high predicted probability of overall survival time, and the AUC value was 0.837. The expression status of 7 proteins was frequently altered in normal or differential tumor tissues, such as liver cancer and stomach cancer samples.We have identified several metabolism-related biomarkers for prognosis prediction of HCC based on the PS model. Two clinical factors were independent prognostic predictors of pathologic stage and PS status (high/low risk). The prognosis prediction model described in this study is a useful and stable method for novel biomarker identification.

A 14-Methylation-Driven Differentially Expressed RNA as a Signature for Overall Survival Prediction in Patients with Uterine Corpus Endometrial Carcinoma

DNA methylation has been implicated as an important mechanism for the development of uterine corpus endometrial carcinoma (UCEC), indicating that methylation-driven genes may be potential biomarkers for survival prediction. In this study, we aimed to identify a new prognostic methylation signature for UCEC based on differentially expressed genes (DEGs) and long noncoding RNAs (lncRNAs) (DELs). Sample-matched RNA-sequencing and methylation-array data were downloaded from The Cancer Genome Atlas database, by analysis of which a total of 269 DEGs and 4 DELs were identified to be methylation driven. Least absolute shrinkage and selection operator analysis screened that 14 methylation-driven genes were significantly associated with overall survival (OS) and thus were used as a signature to establish a prognostic risk model. Based on the median threshold, the patients were divided into the low-risk and the high-risk groups, which showed significantly different survival periods under the Kaplan-Meier curve. The area under receiver operating characteristic curve (AUC) was 0.934, 0.919, and 0.952 for the training, validation, and entire cohort, respectively. Stratification analysis showed that the established risk model may add prognostic values to conventional clinical factors (age, neoplasm histologic grade, and clinical stage). A nomogram was constructed based on the risk model and clinical parameters, with the AUC of 0.978 and c-index of 0.8079. Database for Annotation, Visualization, and Integrated Discovery (DAVID) function enrichment and Human Protein Atlas (HPA) protein expression validation showed 5 of these 14 genes may be especially important for UCEC (hypermethylated lowly expressed: CCBE1, FOXL2, PHLDB2, and DTNA; hypomethylated highly expressed: CCNE1). Comparison with breast cancer in the methylation level indicated ABCA12, CCNE1, and CLRN3 may be specific methylation-driven genes for UCEC. LncRNA HCG11 may function by coexpressing with DTNA. In conclusion, this 14-DNA methylation signature combined with clinical factors may a potentially effective biomarker in predicting OS for UCEC patients.