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Background: Fatty acid metabolism (FAM), as a hallmark of caner, plays important roles in tumor initiation and carcinogenesis. However, the significance of fatty acid metabolism-related genes in colon adenocarcinoma (COAD) are largely unknown. Methods: RNA sequencing data and clinical information were downloaded from the Cancer Genome Atlas (TCGA) cohort. Univariate and multivariate Cox regression analyses were utilized to construct a fatty acid metabolism-related gene signature. Kaplan-Meier survival and receiver operating characteristic (ROC) analyses were used to verify the performance of this signature. GEO datasets were applied to validate the signature. Maftools package was utilized to analyze the mutation profiles of this signature. Correlation between the risk signature and stemness scores was compared by RNA stemness score (RNAss). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set variation analysis (GSVA) were performed to explore the potential functions and signaling pathways. Immune landscape of the signature was explored by analyzing different immune cells infiltration, immune functions and microsatellite instability. A nomogram was constructed by combining the risk signature and multiple clinical factors. Expression levels and prognostic values of the risk genes were revealed in the cancer genome atlas and GEO databases. Moreover, the expression the risk genes were measured in cell lines using real time quantitative PCR (qRT-PCR). Results: Eight fatty acid metabolism-related genes (CD36, ENO3, MORC2, PTGR1, SUCLG2, ELOVL3, ELOVL6 and CPT2) were used to construct a risk signature. This signature demonstrated better prognostic value than other clinicopathological parameters, with AUC value was 0.734 according to the cancer genome atlas database. There was negative correlation between the riskscore and RNA stemness score. The patients in the high-risk group demonstrated higher infiltration of M0 macrophages, and less infiltration of activated CD4 memory T cells and Eosinophils. There were more MSI patients in the high-risk group than those in the low-risk group (38% vs. 30%). The risk scores of patients in the MSI group were slightly higher than those in the microsatellite stability group. Gene ontology, kyoto encyclopedia of genes and genomes and gene set variation analysis enrichment analyses showed that several metabolism-related functions and signaling pathways were enriched. A nomogram showed good predictive capability of the signature. Moreover, qRT-PCR revealed upregulated expression of ENO3, MORC2, SUCLG2 and ELOVL6, and downregulated expression of CPT2 in all examined colon adenocarcinoma cell lines. Conclusion: This study provided novel insights into a fatty acid metabolism-related signature in the prognosis an immune landscape of colon adenocarcinoma patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Fang, Lu and Liu.)

Background: Tripartite motif (TRIM) family genes get involved in the pathogenesis and development of various biological processes; however, the prognostic value of TRIM genes for idiopathic pulmonary fibrosis (IPF) needs to be explored., Methods: We acquired gene expression based on bronchoalveolar lavage (BAL) cells and clinical data of three independent IPF cohorts in the GSE70866 dataset from the Gene expression omnibus (GEO) database. Differentially expressed TRIM genes (DETGs) between IPF patients and healthy donors were identified and used to establish a risk signature by univariate and multivariate Cox regression analysis in the training cohort. The risk signature was further validated in other IPF cohorts, and compared with previously published signatures. Moreover, we performed functional enrichment analysis to explore the potential mechanisms. Eventually, the quantitative real time PCR was conducted to validate the expressions of the key genes in BAL from 12 IPF patients and 12 non-IPF controls from our institution., Results: We identified 4 DETGs including TRIM7, MEFV, TRIM45 and TRIM47 significantly associated with overall survival (OS) of IPF patients (P < 0.05). A multiple stepwise Cox regression analysis was performed to construct a 4-TRIM-gene prognostic signature. We categorized IPF patients into one low-risk group and the other high-risk group as per the average risk value of the TRIM prognostic signature in the training and validation cohorts. The IPF individuals in the low-risk group demonstrated an obvious OS advantage compared with the high-risk one (P < 0.01). The time-dependent receiver operating characteristic approach facilitated the verification of the predictive value of the TRIM prognostic signature in the training and validation cohorts, compared with other published signatures. A further investigation of immune cells and IPF survival displayed that higher proportion of resting memory CD4+ T cells and resting mast cells harbored OS advantage over lower proportion, however lower proportion of neutrophils, activated dendritic cells and activated NK cells indicated worse prognosis., Conclusion: The TRIM family genes are significant for the prognosis of IPF and our signature could serve as a robust model to predict OS., (© 2022. The Author(s).)

Background: Endometrial cancer (EC) is one of the most common malignant tumors in female reproductive system. The incidence of lymph node metastasis (LNM) is only about 10% in clinically suspected early-stage EC patients. Discovering prognostic models and effective biomarkers for early diagnosis is important to reduce the mortality rate., Methods: A least absolute shrinkage and selection operator (LASSO) regression was conducted to identify the characteristic dimension decrease and distinguish porgnostic LNM related genes signature. Subsequently, a novel prognosis-related nomogram was constructed to predict overall survival (OS). Survival analysis was carried out to explore the individual prognostic significance of the risk model and key gene was validated in vitro., Results: In total, 89 lymph node related genes (LRGs) were identified. Based on the LASSO Cox regression, 11 genes were selected for the development of a risk evaluation model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better OS (p = 3.583e-08). The area under the ROC curve (AUC) of this model was 0.718 at 5 years of OS. Then, we developed an OS-associated nomogram that included the risk score and clinicopathological features. The concordance index of the nomogram was 0.769. The survival verification performed in three subgroups from the nomogram demonstrated the validity of the model. The AUC of the nomogram was 0.787 at 5 years OS. Proliferation and metastasis of HMGB3 were explored in EC cell line. External validation with 30 patients in our hospital showed that patients with low-risk scores had a longer OS (p-value = 0.03). Finally, we revealed that the most frequently mutated genes in the low-risk and high-risk groups are PTEN and TP53, respectively., Conclusions: Our results suggest that LNM plays an important role in the prognosis, and HMGB3 was potential as a biomarker for EC patients., (© 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: Ubiquitin-conjugating enzyme E2 T (UBE2T) is a potential oncogene. However, Pan-cancer analyses of the functional, prognostic and predictive implications of this gene are lacking., Methods: We first analyzed UBE2T across 33 tumor types in The Cancer Genome Atlas (TCGA) project. We investigated the expression level of UBE2T and its effect on prognosis using the TCGA database. The correlation between UBE2T and cell cycle in pan-cancer was investigated using the single-cell sequencing data in Cancer Single-cell State Atlas (CancerSEA) database. The Weighted Gene Co-expression Network analysis (WGCNA), Univariate Cox and Least absolute shrinkage and selection operator (LASSO) Cox regression models, and receiver operating characteristic (ROC) were applied to assess the prognostic impact of UBE2T-related cell cycle genes (UrCCGs). Furthermore, the consensus clustering (CC) method was adopted to divide TCGA-lung adenocarcinoma (LUAD) patients into subgroups based on UrCCGs. Prognosis, molecular characteristics, and the immune panorama of subgroups were analyzed using Single-sample Gene Set Enrichment Analysis (ssGSEA). Results derived from TCGA-LUAD patients were validated in International Cancer Genome Consortium (ICGC)-LUAD data., Results: UBE2T is highly expressed and is a prognostic risk factor in most tumors. CancerSEA database analysis revealed that UBE2T was positively associated with the cell cycle in various cancers(r > 0.60, p < 0.001). The risk signature of UrCCGs can reliably predict the prognosis of LUAD (AUC 1 year  = 0.720, AUC 3 year  = 0.700, AUC 5 year  = 0.630). The CC method classified the TCGA-LUAD cohort into 4 UrCCG subtypes (G1-G4). Kaplan-Meier survival analysis demonstrated that G2 and G4 subtypes had worse survival than G3 (Log-rank test P TCGA training set  < 0.001, P ICGC validation set  < 0.001). A comprehensive analysis of immune infiltrates, immune checkpoints, and immunogenic cell death modulators unveiled different immune landscapes for the four subtypes. High immunophenoscore in G3 and G4 tumors suggested that these two subtypes were immunologically "hot," tending to respond to immunotherapy compared to G2 subtypes (p < 0.001)., Conclusions: UBE2T is a critical oncogene in many cancers. Moreover, UrCCG classified the LUAD cohort into four subgroups with significantly different survival, molecular features, immune infiltrates, and immunotherapy responses. UBE2T may be a therapeutic target and predictor of prognosis and immunotherapy sensitivity., (© 2022. The Author(s).)

Background: Uterine corpus endometrial carcinoma (UCEC) is the third most common gynecologic malignancy. Fatty acid metabolism (FAM) is an essential metabolic process in the immune microenvironment that occurs reprogramming in the presence of tumor signaling and nutrient competition. This study aimed to identify the fatty acid metabolism-related genes (FAMGs) to develop a risk signature for predicting UCEC., Methods: The differentially expressed FAMGs between UCEC samples and controls from TCGA database were discovered. A prognostic signature was then constructed by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Based on the median risk score, UCEC samples were categorized into high- and low-FAMGs groups. Kaplan-Meier (K-M) curve was applied to determine patients' overall survival (OS). The independent prognostic value was assessed by uni- and multivariate analyses. The associations between the risk score and immune status, immune score, and drug resistance were evaluated. Quantitative Real-time PCR (qRT-PCR) was utilized to confirm FAMGs expression levels in UCEC cells., Results: We built a 10-FAMGs prognostic signature and examined the gene mutation and copy number variations (CNV). Patients with a high-FAMGs had a worse prognosis compared to low-FAMGs patients in TCGA train and test sets. We demonstrated that FAMGs-based risk signature was a significant independent prognostic predictor of UCEC. A nomogram was also created incorporating this risk model and clinicopathological features, with high prognostic performance for UCEC. The immune status of each group was varied, and immune score was higher in a low-FAMGs group. HLA-related genes such as DRB1, DMA, DMB, and DQB2 had higher expression levels in the low-FAMGs group. Meanwhile, high-FAMGs patients were likely to response more strongly to the targeted drugs Bortezomib, Foretinib and Gefitinib. The qRT-PCR evidence further verified the significant expression of FAMGs in this signature., Conclusions: A FAMGs-based risk signature might be considered as an independent prognostic indicator to predict UCEC prognosis, evaluate immune status and provide a new direction for therapeutic strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Guo, He, Chen, Li, Wang, Bao, Zeng, Jiang, Zhou and Zhang.)

Background: Synapse-associated proteins (SAPs) play important roles in central nervous system (CNS) tumors. Recent studies have reported that γ-aminobutyric acidergic (GABAergic) synapses also play critical roles in the development of gliomas. However, biomarkers of GABAergic synapses in low-grade gliomas (LGGs) have not yet been reported., Methods: mRNA data from normal brain tissue and gliomas were obtained from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases, respectively. A validation dataset was also obtained from the Chinese Glioma Genome Atlas (CGGA) database. The expression patterns of GABAergic synapse-related genes (GSRGs) were evaluated with difference analysis in LGGs. Then, a GABAergic synapse-related risk signature (GSRS) was constructed with least absolute shrinkage and selection operator (LASSO) Cox regression analysis. According to the expression value and coefficients of identified GSRGs, the risk scores of all LGG samples were calculated. Univariate and multivariate Cox regression analyses were conducted to evaluate related risk scores for prognostic ability. Correlations between characteristics of the tumor microenvironment (TME) and risk scores were explored with single-sample gene set enrichment analysis (ssGSEA) and immunity profiles in LGGs. The GSRS-related pathways were investigated by gene set variation analysis (GSVA). Real-time PCR and the Human Protein Atlas (HPA) database were applied to explore related expression of hub genes selected in the GSRS., Results: Compared with normal brain samples, 25 genes of 31 GSRGs were differentially expressed in LGG samples. A constructed five-gene GSRS was related to clinicopathological features and prognosis of LGGs by the LASSO algorithm. It was shown that the risk score level was positively related to the infiltrating level of native CD4 T cells and activated dendritic cells. GSVA identified several cancer-related pathways associated with the GSRS, such as P53 pathways and the JAK-STAT signaling pathway. Additionally, CA2, PTEN, OXTR, and SLC6A1 (hub genes identified in the GSRS) were regarded as the potential predictors in LGGs., Conclusion: A new five-gene GSRS was identified and verified by bioinformatics methods. The GSRS provides a new perspective in LGG that may contribute to more accurate prediction of prognosis of LGGs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Jiang, Sun, Li and Chen.)

Background: Cuproptosis is the recently defined regulatory cell death (RCD) that plays essential roles in tumorigenesis and progression. Long noncoding RNAs (lncRNAs) regulate the gene expression through various means. However, the clinical value of cuproptosis-related lncRNAs in bladder cancer (BLCA) remains poorly described., Methods: We downloaded the transcriptome sequencing data and clinical information from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and lasso Cox regression analyses were performed to construct the prognostic risk signature, the predictive accuracy of which was validated in the subsequent independence and stratification analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the underlying molecular mechanisms involved in the signature to explore therapeutic vulnerabilities and potential targets in BLCA. Tumor mutational burden (TMB) and tumor immune dysfunction and exclusion (TIDE) were used to estimate the response to immune checkpoint inhibitors (ICIs). We further explored the potential new drug-target candidates based on the half maximal inhibitory concentration for this patient population., Results: Fifteen cuproptosis-related lncRNAs significantly associated with survival were identified to construct the risk signature based on the normalized expression level and regression coefficient of each gene. The patients with BLCA and high-risk scores defined by the signature were associated with worse survival outcomes. The differentially expressed genes (DEGs) between the 2 risk groups had different biological activity. Furthermore, the patients in the low-risk group exhibited a higher TMB index and a lower TIDE score. The sensitivity of multiple antitumor drugs was negatively related to risk score, including AR-42, AS605240, FK866, TAK-715, and tubastatin A, while the sensitivity of some antitumor drugs, such as AMG-706, BX-795, and RO-3306, were positively correlated with risk score., Conclusions: Our study established and verified a novel clinical risk signature with cuproptosis-related lncRNAs that may predict therapy response and prognosis with robust and stable accuracy in patients with BLCA and enhance the personalized management of this patient population., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-5294/coif). The authors have no conflicts of interest to declare., (2022 Annals of Translational Medicine. All rights reserved.)

Clear cell renal cell carcinoma (ccRCC) is the most frequent kind of kidney malignancy. Inflammation is a physiological response of the immune system to harmful stimuli. Notably, the role inflammation plays in ccRCC is still unknown. In this study, consensus clustering analysis sorted the ccRCC specimens from the TCGA dataset into C1 and C2 clusters. The C2 cluster comprised ccRCC specimens with a high TNM stage and tumor grade. These specimens were characterized by the activation of the inflammatory response and an immunosuppressive microenvironment. A seven-gene inflammation-related risk signature was designed employing the LASSO and Cox regression analyses for the inflammation-related genes. The ccRCC specimens were classified into two groups with high and low risk by calculating the risk scores. The specimens in the group with high risk showed a poor prognosis and were positively correlated with immune inhibitory factors. Moreover, a nomogram was created by incorporating inflammation-related risk signatures and clinical characteristics. The ROC and DCA curves indicated a satisfactory efficiency of the nomogram for predicting the survival outcomes. Furthermore, we identified the potential therapeutic drug molecules through CMap analysis. The findings of our study may act as a guide for further research on new prognostic biomarkers and therapies.

Background: Accumulating evidence has highlighted the significance of chromatin regulator (CR) in pathogenesis and progression of cancer. However, the prognostic role of CRs in LUAD remains obscure. We aim to detect the prognostic value of CRs in LUAD and create favorable signature for assessing prognosis and clinical value of LUAD patients., Methods: The mRNA sequencing data and clinical information were obtained from TCGA and GEO databases. Gene consensus clustering analysis was utilized to determine the molecular subtype of LUAD. Cox regression methods were employed to set up the CRs-based signature (CRBS) for evaluating survival rate in LUAD. Biological function and signaling pathways were identified by KEGG and GSEA analyses. In addition, we calculated the infiltration level of immunocyte by CIBERSORT algorithm. The expressions of model hub genes were detected in LUAD cell lines by real-time polymerase chain reaction (PCR)., Results: KEGG analysis suggested the CRs were mainly involved in histone modification, nuclear division and DNA modification. Consensus clustering analysis identified a novel CRs-associated subtype which divided the combined LUAD cohort into two clusters (C1 = 217 and C2 = 296). We noticed that a remarkable discrepancy in survival rate among two clusters. Then, a total of 120 differentially expressed CRs were enrolled into stepwise Cox analyses. Four hub CRs (CBX7, HMGA2, NPAS2 and PRC1) were selected to create a risk signature which could accurately forecast patient outcomes and differentiate patient risk. GSEA unearthed that mTORC1 pathway, PI3K/Akt/mTOR and p53 pathway were greatly enriched in CRBS-high cohort. Moreover, the infiltration percentages of macrophage M0, macrophage M2, resting NK cells, memory B cells, dendritic cells and mast cells were statistically significantly different in the two groups. PCR assay confirmed the differential expression of four model biomarkers., Conclusions: Altogether, our project developed a robust risk signature based on CRs and offered novel insights into individualized treatment for LUAD cases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Shi, Han, Liu, Wang and Ma.)

Background: Renal cancer is one of the most lethal cancers because of its atypical symptoms and metastatic potential. The metabolism of amino acids and their derivatives is essential for cancer cell survival and proliferation. Thus, the construction of the amino acid metabolism-related risk signature might enhance the accuracy of the prognostic model and shed light on the treatments of renal cancers., Methods: RNA expression and clinical data were downloaded from Santa Cruz (UCSC) Xena, GEO, and ArrayExpress databases. The "DESeq2" package identified the differentially expressed genes. Univariate COX analysis selected prognostic genes related to the metabolism of amino acids. Patients were divided into two clusters using the "ConsensusClusterPlus" package, and the CIBERSORT, ESTIMATE methods were explored to assess the immune infiltrations. The LASSO regression analysis constructed a risk model which was evaluated the prediction accuracy in two independent cohorts. The genomic alterations and drug sensitivity of 18-LASSO-genes were assessed. The differentially expressed genes between two clusters were used to perform functional enrichment analysis and weighted gene co-expression network analysis (WGCNA). Furthermore, external validation of TMEM72 expression was conducted in the FUSCC cohort containing 33 ccRCC patients., Results: The amino acid metabolism-related genes had significant correlations with prognosis. The patients in Cluster A demonstrated better survival, lower Treg cell proportion, higher ESTIMATE scores, and higher cuproptosis-related gene expressions. Amino acid metabolism-related genes with prognostic values were used to construct a risk model and patients in the low risk group were associated with improved outcomes. The Area Under Curve of the risk model was 0.801, 0.777, and 0.767 at the first, second, and third year respectively. The external validation cohort confirmed the stable prognostic value of the risk model. WGCNA identified four gene modules correlated with immune cell infiltrations and cuproptosis. We found that TMEM72 was downregulated in tumors by using TCGA, GEO datasets (p<0.001) and the FUSCC cohort (p=0.002)., Conclusion: Our study firstly constructed an 18 amino acid metabolism related signature to predict the prognosis in clear cell renal cell carcinoma. We also identified four potential gene modules potentially correlated with cuproptosis and identified TMEM72 downregulation in ccRCC which deserved further studies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Su, Tian, Zhang, Xu, Anwaier, Ye, Zhu, Wang, Shi, Qu, Zhang and Ye.)

Background: Liver hepatocellular carcinoma (HCC) is a prevalent cancer that lacks a sufficiently efficient approach to guide immunotherapy. Additionally, cuproptosis is a recently identified regulated cell death program that is triggered by copper ionophores. However, its possible significance in tumor immune cell infiltration is still unclear., Methods: Cuproptosis subtypes in HCC were identified using unsupervised consensus cluster analysis based on 10 cuproptosis regulators expressions, and a cuproptosis-related risk signature was generated using univariate and LASSO Cox regression and validated using the ICGC data. Moreover, the relationship between signature and tumor immune microenvironment (TME) was studied through tumor immunotherapy responsiveness, immune cell infiltration, and tumor stem cell analysis. Finally, clinical specimens were analyzed using immunohistochemistry to verify the expression of the three genes in the signature., Results: Two subtypes of cuproptosis regulation were observed in HCC, with different immune cell infiltration features. Genes expressed differentially between the two cuproptosis clusters in the TCGA were determined and used to construct a risk signature that was validated using the ICGC cohort. Greater immune and stromal cell infiltration were observed in the high-risk group and were associated with unfavorable prognosis. Elevated risk scores were linked with higher RNA stemness scores (RNAss) and tumor mutational burden (TMB), together with a greater likelihood of benefitting from immunotherapy., Conclusion: It was found that cuproptosis regulatory patterns may play important roles in the heterogeneity of immune cell infiltration. The risk signature associated with cuproptosis can assess each patient's risk score, leading to more individualized and effective immunotherapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Xiao, Zhao, Sun, Guo, Li, Zhang, Bian, Qiu and Wang.)

The outcome of colon adenocarcinoma (COAD) patients remains dismal, and lactate metabolism has been characterized to promote tumor development and immune evasion. Based on the above background, it is worthwhile to explore novel prognostic and therapeutic biomarkers for COAD patients from the aspect of lactate metabolism. Above all, 228 available lactate-metabolism-related genes (LMRGs) were acquired, and the landscape of copy number variation and the expression difference of mRNA levels between colon normal and tumor samples were investigated among these LMRGs. Importantly, eight overall survival (OS)-involved LMRGs were then distinguished by means of univariate Cox regression analysis in both GSE40967 and TCGA-COAD data sets. Subsequently, prognostic risk scores were established, integrating seven OS-related LMRGs by LASSO Cox regression analysis in the GSE40967 set, and then verified in the TCGA-COAD cohort. From the comprehensive analyses, COAD patients with high risk had comparatively more inferior survival probability in all populations of the study, and they tended to have more severe clinicopathological features with the risk score increasing. Moreover, by integrating age, AJCC T and pathological stage, and risk score, we constructed a prognostic nomogram that demonstrated great prediction effectiveness for OS of COAD patients. Furthermore, the potential effect of various risk score on tumor immune was assessed from enrichment of immune-related pathways, tumor-infiltrating immune cells, and expression levels of immune checkpoints separately. We could draw a conclusion that COAD patients with higher lactate-metabolism-related risk scores may acquire an immunosuppressive tumor microenvironment, which subsequently led to immune escapes and poor prognoses. Conclusively, all findings in the present study illustrate a great prognostic value of the lactate-metabolism-related risk signature, providing more in-depth insights into the indispensable function of lactate metabolism in prognosis and tumor immunity of COAD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zou, Chai, Li, Lin, He and Xiong.)

N7-methylguanosine (m 7 G) modification on internal RNA positions plays a vital role in several biological processes. Recent research shows m 7 G modification is associated with multiple cancers. However, in hepatocellular carcinoma (HCC), its implications remain to be determined. In this place, we need to interrogate the mRNA patterns for 29 key regulators of m 7 G RNA modification and assess their prognostic value in HCC. Initial, the details from The Cancer Genome Atlas (TCGA) database concerning transcribed gene data and clinical information of HCC patients were inspected systematically. Second, according to the mRNA profiles of 29 m 7 G RNA methylation regulators, two clusters (named 1 and 2, respectively) were identified by consensus clustering. Furthermore, robust risk signature for seven m 7 G RNA modification regulators was constructed. Last, we used the Gene Expression Omnibus (GEO) dataset to validate the prognostic associations of the seven-gene risk signature. We figured out that 24/29 key regulators of m 7 G RNA modification varied remarkably in their grades of expression between the HCC and the adjacent tumor control tissues. Cluster one compared with cluster two had a substandard prognosis and was also positively correlated with T classification (T), pathological stage, and vital status (fustat) significantly. Consensus clustering results suggested the expression pattern of m 7 G RNA modification regulators was correlated with the malignancy of HCC strongly. In addition, cluster one was extensively enriched in metabolic-related pathways. Seven optimal genes ( METTL1 , WDR4 , NSUN2 , EIF4E , EIF4E2 , NCBP1 , and NCBP2 ) were selected to establish the risk model for HCC. Indicating by further analyses and validation, the prognostic model has fine anticipating command and this probability signature might be a self supporting presage factor for HCC. Finally, a new prognostic nomogram based on age, gender, pathological stage, histological grade, and prospects were established to forecast the prognosis of HCC patients accurately. In essence, we detected association of HCC severity and expression levels of m 7 G RNA modification regulators, and developed a risk score model for predicting prognosis of HCC patients' progression., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Yao, Sun, Wang, Yue, Cui, Yu, Xu and Li.)

Aim: To explore the prognostic value of methylated snoRNA genes in glioma and construct a prognostic risk signature. Materials & methods: We retrieved clinical information and 450K methylation data from The Cancer Genome Atlas and obtained five methylated snoRNA genes. Then we established a risk signature and verified the effect of SNORA71B on glioma cells with functional assays. Results: A risk signature containing five methylated snoRNA genes was constructed and demonstrated to be an independent predictor of glioma prognosis. Silencing SNORA71B restrained the proliferation, migration and invasion of glioma cells and reduced the expression of mesenchymal and cell cycle marker proteins. Conclusion: This study constructed a methylated snoRNA gene risk signature, which may provide a reference for glioma patients' prognosis assessment.

Oxidative stress (OS) response is crucial in oncogenesis and progression of tumor. But the potential prognostic importance of OS-related genes (OSRGs) in stomach adenocarcinoma (STAD) lacked comprehensive study. STAD clinical information and transcriptome data were retrieved from the Gene Expression Omnibus and The Cancer Genome Atlas databases. The prognostic OSRGs were filtered via the univariate Cox analysis and OSRG-based molecular subtypes of STAD were developed using consensus clustering. Weighted gene co-expression network analysis (WGCNA) was subsequently conducted to filter molecular subtype-associated gene modules. The prognosis-related genes were screened via univariate and least absolute shrinkage and selection operator Cox regression analysis were used to construct a prognostic risk signature. Finally, a decision tree model and nomogram were developed by integrating risk signature and clinicopathological characteristics to analyze individual STAD patient's survival. Four OSRG-based molecular subtypes with significant diversity were developed based on 36 prognostic OSRGs for STAD, and an OSRGs-based subtype-specific risk signature with eight genes for prognostic prediction of STAD was built. Survival analysis revealed a strong prognostic performance of the risk signature exhibited in predicting STAD survival. There were significant differences in mutation patterns, chemotherapy sensitivity, clinicopathological characteristics, response to immunotherapy, biological functions, immune microenvironment, immune cell infiltration among different molecular subtypes and risk groups. The risk score and age were verified as independent risk factors for STAD, and a nomogram integrating risk score and age was established, which showed superior predictive performance for STAD prognosis. We developed an OSRG-based molecular subtype and identified a novel risk signature for prognosis prediction, providing a useful tool to facilitate individual treatment for patients with STAD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhou, Peng and Li.)

Objective: To develop an autophagy-related lncRNA-based risk signature and corresponding nomogram to predict overall survival (OS) for LUAD patients and investigate the possible meaning of screened factors. Methods: Differentially expressed lncRNAs and autophagy genes were screened between normal and LUAD tumor samples from the TCGA LUAD dataset. Univariate and multivariate Cox regression analyses were performed to construct the lncRNA-based risk signature and nomogram incorporating clinical information. Then, the accuracy and sensitivity were confirmed by the AUC of ROC curves in both training and validation cohorts. qPCR, immunoblot, shRNA, and ectopic expression were used to verify the positive regulation of NFYC-AS1 on BIRC6. CCK-8, immunofluorescence, and flow cytometry were used to confirm the influence of NFYC-AS1 on cell proliferation, autophagy, and apoptosis via BIRC6. Results: A 12-lncRNA risk signature and a nomogram combining related clinical information were constructed. Furthermore, the abnormal increase of NFYC-AS1 may promote LUAD progression through the autophagy-related gene BIRC6. Conclusion: 12-lncRNA signature may function as a predictive marker for LUAD patients, and NFYC-AS1 along with BIRC6 may function as carcinogenic factors in a combinatorial manner., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tong, Xu, Xu and Zhang.)

The high risk of postoperative mortality in lung adenocarcinoma (LUAD) patients is principally driven by cancer recurrence and low response rates to adjuvant treatment. Here, A combined cohort containing 1,026 stage I-III patients was divided into the learning ( n  = 678) and validation datasets ( n  = 348). The former was used to establish a 16-mRNA risk signature for recurrence prediction with multiple statistical algorithms, which was verified in the validation set. Univariate and multivariate analyses confirmed it as an independent indicator for both recurrence-free survival (RFS) and overall survival (OS). Distinct molecular characteristics between the two groups including genomic alterations, and hallmark pathways were comprehensively analyzed. Remarkably, the classifier was tightly linked to immune infiltrations, highlighting the critical role of immune surveillance in prolonging survival for LUAD. Moreover, the classifier was a valuable predictor for therapeutic responses in patients, and the low-risk group was more likely to yield clinical benefits from immunotherapy. A transcription factor regulatory protein-protein interaction network (TF-PPI-network) was constructed via weighted gene co-expression network analysis (WGCNA) concerning the hub genes of the signature. The constructed multidimensional nomogram dramatically increased the predictive accuracy. Therefore, our signature provides a forceful basis for individualized LUAD management with promising potential implications., Competing Interests: The authors have declared that no competing interest exists., (© 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.)

Background: Hepatocellular carcinoma (HCC) is a common and deadly malignancy worldwide. Current treatment methods for hepatocellular carcinoma have many disadvantages; thus, it is urgent to improve the efficacy of these therapies. Glycolysis is critical in the occurrence and development of tumors. However, survival and prognosis biomarkers related to glycolysis in HCC patients remain to be fully identified. Methods: Glycolysis-related genes (GRGs) were downloaded from "The Molecular Signatures Database" (MSigDB), and the mRNA expression profiles and clinical information of HCC patients were obtained from TCGA. Consensus clustering was performed to classify the HCC patients into two subgroups. We used the least absolute shrinkage and selection operator (LASSO) regression analysis to construct the risk signature model. Kaplan-Meier (K-M) survival analysis was performed to evaluate the prognostic significance of the risk model, and the receiver operating characteristic (ROC) curve analysis was used to evaluate the prediction accuracy. The independent prediction ability of the risk model was validated by univariate and multivariate Cox regression analyses. The differences of immune infiltrates and relevant oncogenic signaling between different risk groups were compared. Finally, biological experiments were performed to explore the functions of screened genes. Results: HCC patients were classified into two subgroups, according to the expression of prognostic-related GRGs. Almost all GRGs categorized in cluster 2 showed upregulated expressions, whereas GRGs in cluster 1 conferred survival advantages. GSEA identified a positive correlation between cluster 2 and the glycolysis process. Ten genes were selected for risk signature construction. Patients were assigned to high-risk and low-risk groups based on the median risk score, and K-M survival analysis indicated that the high-risk group had a shorter survival time. Additionally, the risk gene signature can partially affect immune infiltrates within the HCC microenvironment, and many oncogenic pathways were enriched in the high-risk group, including glycolysis, hypoxia, and DNA repair. Finally, in vitro knockdown of ME1 suppressed proliferation, migration, and invasion of hepatocellular carcinoma cells. Conclusion: In our study, we successfully constructed and verified a novel glycolysis-related risk signature for HCC prognosis prediction, which is meaningful for classifying HCC patients and offers potential targets for the treatment of hepatocellular carcinoma., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zheng, Liu, Zhu, Liu, Gao, Dai, Zhuo, Mao, He and Liu.)

Background: Endometrial cancer (EC) is one of the most common type of female genital malignancies. The purpose of the present study was to reveal the underlying oncogene and mechanism that played a pivotal role in postmenopausal EC patients., Methods: Weighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of EC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify significant gene modules and hub genes associated with postmenopausal status in EC patients. LASSO regression was conducted to build and validate the risk model. Finally, expression of hub gene was validated in pre- and post-menopausal EC patients in our center., Results: 1240 common genes were used to construct the WGCNA model. According to the WGCNA results, we identified a brown module with 471 genes which was significantly associated with postmenopausal status in EC patients. Furthermore, we constructed an 11-gene risk signature to predict the overall survival of EC patients. The Kaplan-Meier curve and area under the ROC curve (AUC) of this model showed high accuracy in prediction. We also validate the risk model in patients in our center and it also has a high accuracy. Among the 11 genes, PKD1 was recognized as a potential biomarker in the progression of EC patients with postmenopausal status., Conclusion: Taken together, we uncovered a common PKD1-mediated mechanism underlying postmenopausal EC patients' progression by integrated analyses. This finding may improve targeted therapy for EC patients.

Background: Glioblastoma multiforme (GBM) is one of the most malignant tumors in brain with high morbidity and mortality. Mitophagy plays a significant role in carcinogenesis, metastasis, and invasion. In our study, we aim to construct a mitophagy-related risk model to predict prognosis in GBM., Methods: RNA-seq data combined with clinical information were downloaded from TCGA. The 4-gene risk model and nomograph was then constructed and validated in external cohort. Evaluation of immune infiltration, functional enrichment and tumor microenvironment (TME) were then performed., Result: A mitophagy-related risk model was established and patients in TCGA and CGGA were classified into low-risk and high-risk groups. In both cohorts, patients in low-risk group had improved survival, while high-risk group had poor prognosis. Also, the risk model was identified as an independent factor for predicting overall survival via Cox regression. Furthermore, a prognostic nomogram including mitophagy signatures was established with excellent predictive performance. In addition, the risk model was closely associated with regulation of immune infiltration as well as TME., Conclusion: In conclusion, our study constructed a mitophagy-related risk model, which can be utilized for the clinical prognostic prediction in GBM., (© 2022. The Author(s).)

Simple Summary: Despite clinical advancements in the diagnosis and treatment of DCIS, tailoring individual treatment for women diagnosed with DCIS remains an unmet clinical need. Definitive predictive tools that can predict who will or not benefit from radiation therapy (RT) after breast conserving surgery (BCS) remains elusive. Here, we used a prospective–retrospective design to validate DCISionRT®, using data from the SweDCIS randomized clinical trial. DCISionRT identified women with elevated recurrence risk who benefited substantially from RT after BCS. In addition, the test identified women with low recurrence risk and little benefit from RT. These results support our conclusions that knowledge of the individual risk and benefit from RT provided by the test can help clinicians and patients make individualized treatment decisions for women diagnosed with DCIS. Prediction of radiotherapy (RT) benefit after breast-conserving surgery (BCS) for DCIS is crucial. The aim was to validate a biosignature, DCISionRT®, in the SweDCIS randomized trial. Women were randomly assigned to RT or not after BCS, between 1987 and 2000. Tumor blocks were collected, and slides were sent to PreludeDxTM for testing. In 504 women with complete data and negative margins, DCISionRT divided 52% women into Elevated (DS > 3) and 48% in Low (DS ≤ 3) Risk groups. In the Elevated Risk group, RT significantly decreased relative 10-year ipsilateral total recurrence (TotBE) and 10-year ipsilateral invasive recurrence (InvBE) rates, HR 0.32 and HR 0.24, with absolute decreases of 15.5% and 9.3%. In the Low Risk group, there were no significant risk differences observed with radiotherapy. Using a cutoff of DS > 3.0, the test was not predictive for RT benefit (p = 0.093); however, above DS > 2.8 RT benefit was greater for InvBE (interaction p = 0.038). Recurrences at 10 years without radiotherapy increased significantly per 5 DS units (TotBE HR:1.5 and InvBE HR:1.5). Continuous DS was prognostic for TotBE risk although categorical DS did not reach significance. Absolute 10-year TotBE and InvBE risks appear sufficiently different to indicate that DCISionRT can aid physicians in selecting individualized adjuvant DCIS treatment strategies. Further analyses are planned in combined cohorts to increase statistical power. [ABSTRACT FROM AUTHOR]

Despite the high prevalence of gastric cancer (GC), molecular biomarkers that can reliably detect GC are yet to be discovered. The present study aimed to establish a robust gene signature based on cancer driver genes (CDGs) that can predict GC prognosis. Transcriptional profiles and clinical data from GC patients were analyzed using univariate Cox regression analysis and the least absolute shrinkage and selection (LASSO)-penalized Cox regression analysis to select optimal prognosis-related genes for modeling. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analyses were done to assess the predictive power of this gene signature. A nomogram model for prediction of survival of GC patients was established using the CDG signature and clinical information, and a seven-CDG signature was identified. Risk scores were calculated using this signature, and patients were subsequently divided into high- and low-risk groups; high-risk patients in the training and validation datasets had poorer prognoses than low-risk patients. Cox regression analysis revealed that the CDG signature is an independent prognostic factor for GC. The signature and other clinical features were used to construct a nomogram for predicting overall GC patient survival. Calibration and decision curve analysis showed that the nomogram accurately predicted survival, highlighting its clinical utility. Thus, we established a novel CDG signature and nomogram for predicting GC prognosis, which may facilitate personalized treatment of GC.

Background: Oxidative stress (OS) responses have been linked to oncogenesis and tumor progression and have recently been regarded as a potential strategy for tumor therapy. However, OS-related therapeutic targets have not been identified to date in the bladder cancer (BC)., Methods: The mRNA expression and clinical data of BC were downloaded from the public database. Prognostic risk score signature was constructed using LASSO Cox regression analysis. External validation was performed in GSE15307 cohort. ESTIMATE, CIBERSORT, and ssGSEA algorithm were used to analyze immune cell infiltration and immune microenvironment. Next, functional enrichment analysis was performed to elucidate the mechanism underlying the signature. Additionally, we performed a nomogram to forecast the survival rate of individual BC patients., Results: An OS-related genes (OSRGs) signature was constructed. Overall survival was lower in the high-risk group than in the low-risk group, according to survival analyses. The area under the curve (AUC) of ROC curves further validated the prognostic signature's strong prediction performance in these two cohorts. The risk score was verified as an independent risk factor for BC by independent prognostic analysis. Moreover, as compared to TNM stage alone, a nomogram that integrated the risk score with TNM stage showed a much superior predictive value. Immune infiltration and tumor microenvironment studies indicated that immune cells and functions may play a significant role in carcinogenesis and development. The levels of expression of prognostic genes were shown to be substantially linked with drug sensitivity., Conclusion: We developed a novel OSRGs signature for predicting overall survival and impacting the immune status in patients with BC. New nomogram can help clinicians predict the survival rate of BC patients. These findings shed new light on the potential usage of OSRGs signature in BC patients., Competing Interests: The authors declare that they have no competing interests in this work., (© 2022 Zhang et al.)

Background: Glioma is a common type of tumor in the central nervous system characterized by high morbidity and mortality. Autophagy plays vital roles in the development and progression of glioma, and is involved in both normal physiological and various pathophysiological progresses., Patients and Methods: A total of 531 autophagy-related genes (ARGs) were obtained and 1738 glioma patients were collected from three public databases. We performed least absolute shrinkage and selection operator regression to identify the optimal prognosis-related genes and constructed an autophagy-related risk signature. The performance of the signature was validated by receiver operating characteristic analysis, survival analysis, clinic correlation analysis, and Cox regression. A nomogram model was established by using multivariate Cox regression analysis. Schoenfeld's global and individual test were used to estimate time-varying covariance for the assumption of the Cox proportional hazard regression analysis. The R programming language was used as the main data analysis and visualizing tool., Results: An overall survival-related risk signature consisting of 15 ARGs was constructed and significantly stratified glioma patients into high- and low-risk groups ( P < 0.0001). The area under the ROC curve of 1-, 3-, 5-year survival was 0.890, 0.923, and 0.889, respectively. Univariate and multivariate Cox analyses indicated that the risk signature was a satisfactory independent prognostic factor. Moreover, a nomogram model integrating risk signature with clinical information for predicting survival rates of patients with glioma was constructed (C-index=0.861±0.024)., Conclusion: This study constructed a novel and reliable ARG-related risk signature, which was verified as a satisfactory prognostic marker. The nomogram model could provide a reference for individually predicting the prognosis for each patient with glioma and promoting the selection of optimal treatment., Competing Interests: The authors report no conflicts of interest in this work., (© 2022 Fu et al.)

Autophagy has an important role in regulating tumor cell survival. However, the roles of autophagy-related genes (ARGs) during colon adenocarcinoma (COAD) progression and their prognostic value have remained elusive. The present study aimed to identify the correlation between ARGs and the progression of COAD, as well as the prognostic significance of ARGs. The transcriptome profiles and the corresponding clinicopathological information of patients with COAD were downloaded from The Cancer Genome Atlas and Genotype-Tissue Expression databases. A list of ARGs was obtained from the Human Autophagy Database and bioinformatics analysis was performed to investigate the functions of these ARGs. Statistical analyses of these genes were performed to identify independent prognostic markers. The selected prognostic markers were then validated in 15 patients with COAD via immunohistochemistry. Differentially expressed ARGs between normal and tumor tissues were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that the differentially expressed ARGs were mainly enriched in toxoplasmosis and pathways in cancer. The ATG4B, DAPK1 and SERPINA1 genes were determined to be associated with COAD progression. In addition, a risk signature was proposed that may serve as an independent prognostic marker. In conclusion, ATG4B, DAPK1 and SERPINA1 are crucial participants in tumorigenesis of COAD. The present study may promote the development of novel treatment strategies for COAD. [ABSTRACT FROM AUTHOR]

Ferroptosis, a new form of programmed cell death, provides a new option for anti-tumor treatment. However, the roles of ferroptosis-related (FR) genes in pancreatic adenocarcinoma (PAAD) were not fully elaborated. In the present study, 185 TCGA samples and 81 ICGC samples were used as training and validation cohorts, respectively. A novel FR risk signature (ALOX5, ALOX12, PTGS2, SAT1, STEAP3 and SQLE) was constructed via the Lasso regression analysis. In TCGA cohort, the risk signature was identified as an independent prognostic factor. Decision curve analysis (DCA) indicated that FR risk score could increase the net benefit when making clinical-decision. In addition, we constructed a nomogram to predict the overall survival rate (OSR) of individual at 1,2,3 year. Meanwhile, the prognostic value was partly validated in ICGC cohort. Through immune analyses, we found that high FR risk could affect the immune abundances of five lymphocytes but not effectively affect the activities of immune-related pathways. The expressions of most FR risk genes did not correlate with that of PD-L1(CD274) and CTLA4. Further, through RT-qPCR tests, the expressions of PTGS2 and SQLE were proven to be significantly upregulated in normal pancreatic duct epithelia cell (HPDE6-C7) compared to pancreatic cancer cells (SW1990 and BxPC-3). MTT, wound-healing and transwell assays revealed that silencing PTGS2 and SQLE could inhibit the proliferation, migration and invasion of pancreatic cancer cells. Besides, western-blot assays showed that blocking PTGS2 and SQLE expressions could suppress the protein expressions of cyclin D1 and N-cadherin, but facilitate that of E-cadherin, which suggested that they were involved in the epithelial to mesenchymal transition (EMT). Collectively, FR risk signature provides an important complement for PAAD prognostic analysis. High FR risk level can adversely affect anti-tumor immune process, but may not serve as a predictive marker of ICIs efficacy. PTGS2 and SQLE are proven to possess cancer-promoting abilities in PAAD. [ABSTRACT FROM AUTHOR]

Background: Diffuse glioma is the most common primary tumor of the central nervous system and has a poor prognosis. Recently, a new type of programmed cell death (PCD), pyroptosis, has been found to be widely involved in the process of tumor diseases. However, the expression of pyroptosis-related genes (PRGs) in diffuse gliomas and their relationship with prognosis have rarely been evaluated., Methods: In this study, we obtained RNA sequencing and clinical data from the Cancer Genome Atlas (TCGA) database and the Chinese Glioma Genome Atlas (CGGA) of diffuse glioma patients. Simultaneously, differentially expressed PRGs between TCGA-Glioma tumor samples and the normal brain samples from the Genome Tissue Expression (GTEx) were investigated. Besides, univariate and multivariate Cox regression analysis were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve and principal component analysis (PCA) was undertaken to assess the prognostic capacity of the signature. Gene set enrichment analyses (GSEA) and single sample GSEA (ssGSEA) were used to further understand the molecular mechanisms and the difference of immune microenvironment. External validation of two separate cohorts from the CGGA database was then performed., Results: Caspase 3 ( CASP3 ) and interleukin-18 ( IL18 ) were identified as potential prognostic biomarkers. A novel prognostic model was constructed to predict diffuse glioma patients' overall survival (OS) time. Patients in high-risk subgroup had shorter survival than those with high-risk with P<0.0001. GSEA and ssGSEA showed the activation of immune-related pathways and the extensive infiltration of immune cells [such as cytotoxic T cells, dendritic cells (DC), natural killer T cell (NKT), induced regulatory T cells (iTreg), naturally occurring regulatory T cells (nTreg)] in high-risk subgroup., Conclusions: A novel two-PRGs prognostic signature based on gene expression was identified, which could predict diffuse glioma patients' OS time. Pyroptosis may be involved in the establishment of immune microenvironment in diffuse glioma., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/atm-21-6011). The authors have no conflicts of interest to declare., (2021 Annals of Translational Medicine. All rights reserved.)

The dysregulation of long non-coding RNAs (lncRNAs) plays a crucial role in ovarian cancer (OC). In this study, we screened out five differentially expressed lncRNAs (AC092718.4, AC138035.1, BMPR1B-DT, RNF157-AS1, and TPT1-AS1) between OC and normal ovarian based on TCGA and GTEx RNA-seq databases by using Kaplan-Meier analysis and univariate Cox, LASSO, and multivariate Cox regression. Then, a risk signature was constructed, with 1, 3, 5-year survival prediction accuracy confirmed by ROC curves, and an online survival calculator for easier clinical use. With lncRNA-microRNA-mRNA regulatory networks established, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, suggesting the involvement of a variety of cancer-related functions and pathways. Finally, five candidate small-molecule drugs (thioridazine, trifluoperazine, loperamide, LY294002, and puromycin) were predicted by Connectivity Map. In conclusion, we identified a 5-lncRNA signature of prognostic value with its ceRNA networks, and five candidate drugs against OC.[Figure: see text].

Aim: Transcription factor (TF) in glioma, including proliferation, invasion/migration, and tumor microenvironment, has been receiving increasing attention. However, there are still no systematical analyses based on global TF. Herein, using global TF target gene sets, we comprehensively investigated their relationship with prognosis and potential biological effect in lower-grade glioma (LGG). We aimed to develop a less-biased prognostic model and provide new insight for personalized management of this disease., Methods: TF target gene sets were collected from MSigDB and GRID database followed by ssGSEA calculating normalized enrichment score. Comprehensive survival analysis was combined with Kaplan-Meier and Cox algorithms. Consensus cluster and lasso regression were performed to develop prognostic signatures with validation of ROC and independent external cohort. Approaches of xCell/CIBERSORT/TIMER were involved in analyzing the immune microenvironment. We also correlated identified prognostic signatures with tumor mutational burden (TMB) and m6A genes., Results: Fourteen TFs were significantly screened based on survival. Patients were classified into 2 prognosis-related clusters based on 14-TFs features. The function of differentially expressed TF target genes between Cluster1/2 was enriched mostly on glioma invasion/migration. The prognostic model was trained by 6 out of 14-TFs followed by generating risk-score as an independent prognostic indicator. We found differences between the high/low-risk group in TMB and the immune microenvironment, where the high-risk group represented "hot-tumor". Besides, 6-TFs were correlated with m6A regulation genes., Conclusion: Our findings suggested that the 6-TFs model could be used to predict prognosis and predict the status of the immune microenvironment in LGG., Competing Interests: The authors report no conflicts of interest in this work., (© 2021 Liu et al.)

• Current prognostic model could not provide full prognostic information for not incorporating information from tumor environments. • Thus combine TNM staging system with tumor environments information might improve the prognostic accuracy of current model. • In the current study, we developed a prognostic stratification to predictions based on integration of CD66b + neutrophils and T immune cells who already had surgery and investigated their relation with clinical outcomes. • At last, the risk signature model provides a possible predictive system to evaluate outcomes for patients received adjuvant chemotherapy. Tumor immune microenvironment biomarkers might add predictive value for outcomes. This study aimed to construct a risk signature with tumor infiltration immune and inflammatory cells to improve the prediction of survival. A risk signature model in combination with CD66b + neutrophils, CD3+ T, CD8+ T lymphocytes, and FOXP3 + regulatory T cells was developed in a training cohort of 327 GC patients undergoing surgical resection between 2011 and 2012, and validated in a validation cohort of 285 patients from 2012 to 2013. The high CD66b expression predicted the poor disease special survival (DSS) and inversely correlated with the CD8 (P < 0.05) and FOXP3 expression (P < 0.05) in the training cohort. This was comparable to the disease-free survival (DFS) findings observed in the validation cohort. Furthermore, a risk stratification was developed from the integration of CD66b + neutrophils and T immune cells. For DFS and DSS, both demonstrated the worse prognosis in the high-risk group, when compared to the low-risk group in both the training cohort and validation cohort (all P < 0.05). In addition, the high-risk group was associated with post-operative relapses, and this risk signature model increased the predictive accuracy and efficiency for post-operative relapses. Moreover, the high-risk group identified a subgroup of GC patients who tended not to benefit from the adjuvant chemotherapy. The incorporation of neutrophils into T lymphocytes could provide more accurate prognostic information in GC and this risk stratification has potential for identifying the subgroup of GC patients who could benefit from adjuvant chemotherapy. [ABSTRACT FROM AUTHOR]

Aims: To explore the pathways and target genes related to N6-methyladenosine (m 6 A) methylation in ovarian cancer and their effect on patient prognosis. Methods & materials: The Cancer Genome Atlas was used to screen genes related to m 6 A regulators in terms of gene expression, mutation and copy number variation. These genes were subjected to pathway enrichment analysis. Prognosis-related genes were screened and involved in risk signature construction. Immunohistochemistry was used for verification. Results: We obtained 1408 genes dysregulated in parallel to m 6 A regulators, which were mainly involved in the platelet activation pathway. The m 6 A-related signature was constructed based on the expression of four prognosis-related genes ( RPS6KA2 , JUNB , HNF4A  and P2RX1 ). Conclusion: This work provides new insights into the mechanism of m 6 A methylation in ovarian cancer.

Background: Head and neck squamous cell carcinoma (HNSCC) is a fatal malignancy owing to the lack of effective tools to predict overall survival (OS). MicroRNAs (miRNAs) play an important role in HNSCC occurrence, development, invasion and metastasis, significantly affecting the OS of patients. Thus, the construction of miRNA-based risk signatures and nomograms is desirable to predict the OS of patients with HNSCC. Accordingly, in the present study, miRNA sequencing data of 71 HNSCC and 13 normal samples downloaded from The Cancer Genome Atlas (TCGA) were screened to identify differentially expressed miRNAs (DEMs) between HNSCC patients and normal controls. Based on the exclusion criteria, the clinical information and miRNA sequencing data of 67 HNSCC samples were selected and used to establish a miRNA-based signature and a prognostic nomogram. Forty-three HNSCC samples were assigned to an internal validation cohort for verifying the credibility and accuracy of the primary cohort. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the functions of 11 miRNA target genes. Results: In total, 11 DEMs were successfully identified. An 11-miRNA risk signature and a prognostic nomogram were constructed based on the expression levels of these 11 DEMs and clinical information. The signature and nomogram were further validated by calculating the C-index, area under the curve (AUC) in receiver-operating characteristic curve analysis, and calibration curves, which revealed their promising performance. The results of the internal validation cohort shown the reliable predictive accuracy both of the miRNA-based signature and the prognostic nomogram. GO and KEGG analyses revealed that a mass of signal pathways participated in HNSCC proliferation and metastasis. Conclusion: Overall, we constructed an 11-miRNA-based signature and a prognostic nomogram with excellent accuracy for predicting the OS of patients with HNSCC. [ABSTRACT FROM AUTHOR]

Introduction: Autophagy functions as a prosurvival mechanism in multiple myeloma (MM). The objective of this research was to establish an autophagy-related gene (ARG) signature for predicting the survival outcomes of MM patients with TP53 mutations., Material and Methods: Information about MM patients with TP53 mutations was downloaded from the Gene Expression Omnibus (GEO) database. Cox proportional hazard regression was employed to determine the independent prognostic ARG and construct a risk signature. Time-dependent receiver-operating characteristic (tROC) curve analysis was used to explore the predictive accuracy of the prognostic model. A nomogram was constructed to give a more precise prediction of the probability of 5-year, 8-year and 10-year overall survival (OS). In addition, we used the CIBERSORT algorithm to explore the distribution difference of 22 immune-infiltrating cells., Results: Three differentially expressed ARGs ( CASP8 , MAPK8 , RB1CC1 ) were finally incorporated to construct the risk model. Area under the curve (AUC) values of the corresponding tROC curve for 5-year, 8-year and 10-year OS were 0.735, 0.686 and 0.662, respectively. Multiple myeloma patients were categorized into high and low-risk groups in accordance with the median threshold value (-1.724549). An ARG-based risk score model was an independent prognostic element correlated with OS, giving an hazard ratio (HR) of 3.29 (95% CI 2.35-4.60, p < 0.001). Thirteen immune infiltrating cells were found to have distribution differences between the two groups., Conclusions: We established a three-ARG risk signature which manifested an independent prognostic factor. The nomogram was testified to perform well in forecasting the long-term survival of TP53-mutated MM patients., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2021 Termedia & Banach.)

Hepatocellular carcinoma (HCC) is one of the leading cancer death and is the primary malignancy of the liver. Tumor hypoxia is the stressor that is involved in tumorigenesis and significantly increased the aggressiveness of HCC. Here, we systematically analyzed the expression profiles and prognostic values of 84 hypoxia associated genes in HCC. mRNA expression of 84 hypoxia associated genes and clinical parameters of HCC patients were downloaded from TCGA, GSE14520, GSE109211 and ICGC. Consensus clustering analysis was performed for unsupervised classes on the basis of 84 hypoxia associated genes. Univariate and LASSO analysis were used to develop the risk signature. A risk signature was developed, including the expression of APEX1, ATR, CTSA, DNAJC5, ENO1, EPO, HMOX1, LDHA, NDRG1, and PER1, and found to be significantly related with OS and DFS of HCC patients. We stratified HCC patients into the high-risk group and low-risk group by means of the risk signature. Patients of high-risk group had shorter OS and DFS, while that of the low-risk group had longer OS and DFS. The risk signature showed better predictive efficiency than the TNM staging in predicting OS and DFS. Also, macrophage M0 cells, regulatory T cells, and neutrophils were found to be significantly enriched in patients of high-risk group. Next, we validated the discrimination and prognostic value of the risk signature in GSE14520 and the ICGC HCC cohort. Finally, significantly lower risk scores were found in sorafenib treatment responders of GSE109211 cohort, and the AUC for predicting sorafenib treatment response was 0.881. In conclusion, a risk signature developed with the expression of 10 hypoxia associated genes improved the prognosis prediction of HCC and correlated with sorafenib treatment response., Competing Interests: None., (AJTR Copyright © 2020.)

The purpose of this study was to investigate the genetic variation, gene expression differences, and clinical significance of SUMOylation regulators in pan-cancers. Based on previous studies, we gained a better understanding of the biological process of SUMOylation and the status of current research. In the present study, we employed a wide range of bioinformatics methods. We used genetic variation and mRNA expression data in the Cancer Genome Atlas (TCGA) to construct a panoramic view of the single nucleotide variants, copy number variants, and gene expression changes in SUMOylation regulators in various tumors. Subsequently, we used the String website and the Cytoscape tool to construct the PPI network between these regulators. We used the GSCALite website to determine the relationship between these regulators and cancer pathways and drug sensitivity. We constructed images of co-expression between these regulators using the R programming language. Using clinical data from TCGA, we performed hazard ratio analysis for these regulators in pan-cancer. Most importantly, we used these regulators to successfully establish risk signatures related to patient prognosis in multiple tumors. Finally, in KIRC, we conducted gene-set enrichment analysis (GSEA) of the five molecules in its risk signatures. We found that these five molecules are involved in multiple cancer pathways. In short, we have comprehensively interpreted the detailed biological process of SUMOylation at the genetic level for the first time, successfully constructed multiple risk signatures, and conducted GSEA in KIRC. We believe that these findings provide credible and valuable information that is relevant for future clinical diagnoses and scientific research., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Osteosarcoma (OS) is the most common primary solid malignant bone tumor, and its metastasis is a prominent cause of high mortality in patients. In this study, a prognosis risk signature was constructed based on metastasis-associated genes. Four microarrays datasets with clinical information were downloaded from Gene Expression Omnibus, and 256 metastasis-associated genes were identified by limma package. Further, a protein-protein interaction network was constructed, and survival analysis was performed using data from the Therapeutically Applicable Research to Generate Effective Treatments data matrix, identifying 19 genes correlated with prognosis. Six genes were selected by the least absolute shrinkage and selection operator regression for multivariate cox analysis. Finally, a three-gene (MYC, CPE, and LY86) risk signature was constructed, and datasets GSE21257 and GSE16091 were used to validate the prediction efficiency of the signature. The survival times of low- and high-risk groups were significantly different in the training set and validation set. Additionally, gene set enrichment analysis revealed that the genes in the signature may affect the cell cycle, gap junctions, and interleukin-6 production. Therefore, the three-gene survival risk signature could potentially predict the prognosis of patients with OS. Further, proteins encoded by CPE and LY86 may provide novel insights into the prediction of OS prognosis and therapeutic targets., (© 2020 Wiley Periodicals, Inc.)

Aim: To identify methylation-driven genes and establish a novel epigenetic signature for gastrointestinal (GI) pan-adenocarcinomas. Materials & methods: Methylation and RNA-seq data for GI adenocarcinomas were downloaded from the Cancer Genome Atlas database. A methylation-driven gene signature was established by multivariate Cox regression analysis. We developed a prognostic nomogram using a combination of methylation-driven gene risk score and clinicopathological variables. A joint survival analysis based on gene expression and methylation was conducted to further investigate the prognostic role of methylation-driven genes. Results: An epigenetic signature was established based on five methylation-driven genes. We also established a prognostic nomogram based on methylation-driven gene risk score and clinicopathologic factors, with a favorable predictive ability. Joint survival analysis revealed that 28 methylation-driven genes could be independent prognostic factors for overall survival for GI adenocarcinomas. Conclusion: An epigenetic signature was established that effectively predicts the overall survival for GI adenocarcinomas across anatomic boundaries.

N6‑methyladenosine (m6A) RNA methylation is the most prevalent type of mRNA modification; however, little is known about its function in clear cell renal cell carcinoma (ccRCC). The present study aimed to establish and validate a m6A‑related risk signature as a prognostic factor for patients with ccRCC. Consensus clustering was used to divide patients with ccRCC from The Cancer Genome Atlas (TCGA) cohort (n=489) into three clusters (cluster 1/2/3) based on 19 m6A RNA methylation regulators. In addition, a m6A‑related risk signature was constructed using TCGA data, and its accuracy was validated using data from the International Cancer Genome Consortium (n=91). The prognostic performance of the risk signature was evaluated by Kaplan‑Meier analyses, least absolute shrinkage and selection operator Cox regression, multivariate Cox regression, receiver operating characteristic curves and nomograms. The results revealed that the majority of the 19 m6A RNA methylation regulators were differentially expressed among ccRCC stratified by different clinicopathological features. The cluster 1 group exhibited a higher frequency of metastasis and poorer overall survival compared with the cluster 2/cluster 3 group. The hallmarks of RNA metabolism, transcription misregulation in cancer and regulation of autophagy, were significantly enriched in the cluster 1 group. A m6A‑related risk signature was constructed and validated with high prognostic accuracy for the prediction of 5‑year survival and recurrence (area under the curve, 0.736 and 0.728, respectively). The present study also established robust nomograms for evaluating the risk of mortality and recurrence for patients with ccRCC (c‑index, 0.783 and 0.819, respectively). The dysregulation of hub m6A RNA methylation regulator expression levels and m6A RNA methylation levels were also validated in multiple RCC cells using in vitro experiments. Taken together, the m6A RNA methylation regulators promoted the malignant progression of ccRCC and exhibited good performance in prognostic predictions. These results provided insight into the development of m6A‑targeted treatments for ccRCC.

Significant limitations exist in our ability to predict breast cancer risk at the individual level. Circulating microRNAs (C-miRNAs) have emerged as measurable biomarkers (liquid biopsies) for cancer detection. We evaluated the ability of C-miRNAs to identify women most likely to develop breast cancer by profiling miRNA from serum obtained long before diagnosis. 24 breast cancer cases and controls (matched for risk and age) were identified from women enrolled in the High-Risk Breast Program at the UVM Cancer Center. Isolated RNA from serum was profiled for over 2500 human miRNAs. The miRNA expression data were input into a stepwise linear regression model to discover a multivariable miRNA signature that predicts long-term risk of breast cancer. 25 candidate miRNAs were identified that individually classified cases and controls based on statistical methodologies. A refined 6-miRNA risk-signature was discovered following regression modeling that distinguishes cases and controls (AUC0.896, CI 0.804-0.988) in this cohort. A functional relationship between miRNAs that cluster together when cases are contrasted against controls was suggested and confirmed by pathway analyses. The discovered 6 miRNA risk-signature can discriminate high-risk women who ultimately develop breast cancer from those who remain cancer-free, improving current risk assessment models. Future studies will focus on functional analysis of the miRNAs in this signature and testing in larger cohorts. We propose that the combined signature is highly significant for predicting cancer risk, and worthy of further screening in larger, independent clinical cohorts., Competing Interests: CONFLICTS OF INTEREST The authors declare no potential conflicts of interest.