Identification and Validation of Constructing the Prognostic Model With Four DNA Methylation-Driven Genes in Pancreatic Cancer

Background: Pancreatic cancer (PC) is a highly aggressive gastrointestinal tumor and has a poor prognosis. Evaluating the prognosis validly is urgent for PC patients. In this study, we utilized the RNA-sequencing (RNA-seq) profiles and DNA methylation expression data comprehensively to develop and validate a prognostic signature in patients with PC.Methods: The integrated analysis of RNA-seq, DNA methylation expression profiles, and relevant clinical information was performed to select four DNA methylation-driven genes. Then, a prognostic signature was established by the univariate, multivariate Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses in The Cancer Genome Atlas (TCGA) dataset. GSE62452 cohort was utilized for external validation. Finally, a nomogram model was set up and evaluated by calibration curves.Results: Nine DNA methylation-driven genes that were related to overall survival (OS) were identified. After multivariate Cox and LASSO regression analyses, four of these genes (RIC3, MBOAT2, SEZ6L, and OAS2) were selected to establish the predictive signature. The PC patients were stratified into two groups according to the median risk score, of which the low-risk group displayed a prominently favorable OS compared with the high-risk group, whether in the training (p < 0.001) or validation (p < 0.01) cohort. Then, the univariate and multivariate Cox regression analyses showed that age, grade, risk score, and the number of positive lymph nodes were significantly associated with OS in PC patients. Therefore, we used these clinical variables to construct a nomogram; and its performance in predicting the 1-, 2-, and 3-year OS of patients with PC was assessed via calibration curves.Conclusion: A prognostic risk score signature was built with the four alternative DNA methylation-driven genes. Furthermore, in combination with the risk score, age, grade, and the number of positive lymph nodes, a nomogram was established for conveniently predicting the individualized prognosis of PC patients.

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Establishment of a Macrophage Phenotypic Switch Related Prognostic Signature in Patients With Pancreatic Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.619517 ◽

2021 ◽

Vol 11 ◽

Author(s):

Mu-xing Li ◽

Hang-yan Wang ◽

Chun-hui Yuan ◽

Zhao-lai Ma ◽

Bin Jiang ◽

...

Keyword(s):

Pancreatic Cancer ◽

Regression Analysis ◽

Signaling Pathway ◽

Risk Score ◽

Prognostic Value ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Prognostic Signature ◽

Phenotypic Switch

IntroductionMacrophage phenotype switch plays a vital role in the progression of malignancies. We aimed to build a prognostic signature by exploring the expression pattern of macrophage phenotypic switch related genes (MRGs) in the Cancer Genome Atlas (TCGA)—pancreatic adenocarcinoma (PAAD), Genotype-Tissue Expression (GTEx)-Pancreas, and Gene Expression Omnibus (GEO) databases.MethodsWe identified the differentially expressed genes between the PAAD and normal tissues. We used single factor Cox proportional risk regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and multivariate Cox proportional hazard regression analysis to establish the prognosis risk score by the MRGs. The relationships between the risk score and immune landscape, “key driver” mutations and clinicopathological factors were also analyzed. Gene-set enrichment analysis (GSEA) analysis was also performed.ResultsWe detected 198 differentially expressed MRGs. The risk score was constructed based on 9 genes (KIF23, BIN1, LAPTM4A, ERAP2, ATP8B2, FAM118A, RGS16, ELMO1, RAPGEFL1). The median overall survival time of patients in the low-risk group was significantly longer than that of patients in the high-risk group (P < 0.001). The prognostic value of the risk score was validated in GSE62452 dataset. The prognostic performance of nomogram based on risk score was superior to that of TNM stage. And GSEA analysis also showed that the risk score was closely related with P53 signaling pathway, pancreatic cancer and T cell receptor signaling pathway. qRT-PCR assay showed that the expressions of the 9 MRGs in PDAC cell lines were higher than those in human pancreatic ductal epithelium cell line.ConclusionsThe nine gene risk score could be used as an independent prognostic index for PAAD patients. Further studies validating the prognostic value of the risk score are warranted.

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Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

Journal of Immunology Research ◽

10.1155/2021/5554342 ◽

2021 ◽

Vol 2021 ◽

pp. 1-24

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

M2 Macrophages ◽

Regression Analyses ◽

Prognostic Signature ◽

Training Cohort

Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300 ; GSE15459, n = 191 ; and GSE26901, n = 109 ). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort ( n = 600 ) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432 ; GSE84437, n = 431 ; and TCGA, n = 336 ). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort ( AUC > 0.7 ). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC ( p < 0.001 ). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group ( p < 0.001 ), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor ( p = 0.011 ). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score ( p = 0.00085 ). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

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DNA Methylation Data-Based Classification and Identification of Prognostic Signature of Children With Wilms Tumor

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.683242 ◽

2021 ◽

Vol 9 ◽

Author(s):

Fucai Tang ◽

Zeguang Lu ◽

Hanqi Lei ◽

Yongchang Lai ◽

Zechao Lu ◽

...

Keyword(s):

Dna Methylation ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Wilms Tumor ◽

Risk Group ◽

Methylation Data ◽

Prognostic Signature ◽

Tumor Patients ◽

Cpg Sites

Background: As an epigenetic alteration, DNA methylation plays an important role in early Wilms tumorigenesis and is possibly used as marker to improve the diagnosis and classification of tumor heterogeneity.Methods: Methylation data, RNA-sequencing (RNA-seq) data, and corresponding clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The prognostic values of DNA methylation subtypes in Wilms tumor were identified.Results: Four prognostic subtypes of Wilms tumor patients were identified by consensus cluster analysis performed on 312 independent prognostic CpG sites. Cluster one showed the best prognosis, whereas Cluster two represented the worst prognosis. Unique CpG sites identified in Cluster one that were not identified in other subtypes were assessed to construct a prognostic signature. The prognostic methylation risk score was closely related to prognosis, and the area under the curve (AUC) was 0.802. Furthermore, the risk score based on prognostic signature was identified as an independent prognostic factor for Wilms tumor in univariate and multivariate Cox regression analyses. Finally, the abundance of B cell infiltration was higher in the low-risk group than in the high-risk group, based on the methylation data.Conclusion: Collectively, we divided Wilms tumor cases into four prognostic subtypes, which could efficiently identify high-risk Wilms tumor patients. Prognostic methylation risk scores that were significantly associated with the adverse clinical outcomes were established, and this prognostic signature was able to predict the prognosis of Wilms tumor in children, which may be useful in guiding clinicians in therapeutic decision-making. Further independent studies are needed to validate and advance this hypothesis.

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A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

10.21203/rs.3.rs-335140/v1 ◽

2021 ◽

Author(s):

Yanjia Hu ◽

Jing Zhang ◽

Jing Chen

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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Identification of Immune-Related Prognostic Biomarkers in Pancreatic Cancer

Nanoscience and Nanotechnology Letters ◽

10.1166/nnl.2020.3251 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1355-1367

Author(s):

Xiaoyan Lin ◽

Jiakang Ma ◽

Kaikai Ren ◽

Mingyu Hou ◽

Bo Zhou ◽

...

Keyword(s):

Pancreatic Cancer ◽

Tumor Microenvironment ◽

Risk Score ◽

Immune Cells ◽

Immune Cell ◽

Cox Regression ◽

Individual Treatment ◽

Survival Prediction ◽

Lasso Regression ◽

Immune Related Genes

Immunotherapy for pancreatic cancer (PC) faces significant challenges. It is urgent to find immunerelated genes for targeted therapy. We aimed to identify immune-related messenger ribonucleic acids (mRNAs) with multiple methods of comprehensive immunoenrichment analysis in predicting survival of PC. PC genomics and clinical data were downloaded from TCGA. We analyzed relative enrichment of 29 immune cells using ssGSEA and classified PC samples into three immuneinfiltrating subgroups. Immune cell infiltration level and pathways were evaluated by ESTIMATE data and KEGG. Independent risk factors were derived from the combined analysis of WGCNA, LASSO regression and Cox regression analyses. Immune risk score was calculated according to four mRNAs to identify its value in predicting survival. PPI analysis was used to analyze the connections and potential pathways among genes. Finally, PC samples were classified into three immuneinfiltrating subgroups. Immunity high subgroup had higher immune score, soakage of immune cells, HLA/PD-L1 expression level, immune-related pathways enrichment and better survivability. Four potential prognostic immune-related genes (ITGB7, RAC2, DNASE1L3, and TRAF1) were identified. Immune risk score could be a potential survival prediction indictor with high sensitivity and specificity (AUC values = 0.708, HR = 1.445). A PPI network with seven nodes and five potential targeted pathways were generated. In conclusion, we estimated the state of immune infiltration in the PC tumor microenvironment by calculating stromal and immune cells enrichment with ssGSEA algorithms, and identified four prognostic immune-related genes that affect the proportion and distribution of immune cells infiltration in the tumor microenvironment. They lay a theoretical foundation to be important immunity targets of individual treatment in PC.

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Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

10.21203/rs.3.rs-24691/v1 ◽

2020 ◽

Author(s):

Hui Wang ◽

Xiaoling Ma ◽

Jinhui Liu ◽

Yicong Wan ◽

Yi Jiang ◽

...

Keyword(s):

Endometrial Cancer ◽

High Risk ◽

Risk Score ◽

Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Gene Set Enrichment ◽

Test Set ◽

Gene Set

Abstract Background: Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.Methods: First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariat, LASSO, and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, gene set enrichment and somatic mutation analyses were also used for these prognostic autophagy-related genes. Results: A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (P<0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. Gene set enrichment analysis revealed high risk score was associated with tumor initiation and progression associated pathways.Conclusions: Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

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A Transcription Factor Signature Predicts Prognosis of Patients with Adrenocortical Carcinoma

10.21203/rs.3.rs-178087/v1 ◽

2021 ◽

Author(s):

Jianyu Zhao ◽

Bo Liu ◽

Xiaoping Li

Keyword(s):

Overall Survival ◽

High Risk ◽

Risk Score ◽

Adrenocortical Carcinoma ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Prognostic Signature ◽

Abdominal Masses ◽

Cox Analysis

Abstract Background: Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for prediction of survival of ACC patients.Methods: The gene expression profile for ACC patients were downloaded from TCGA and GEO datasets. The univariate Cox analysis was applied to identify survival-related TFs and the LASSO Cox regression was conducted to construct the TF signature. The multivariate analysis was used to reveal the independent prognostic factors.Results: We identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6 using the univariate Cox analysis and LASSO Cox regression. The risk score based on the TF-signature could classify patients into low- and high-risk group. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival compared to the low-risk patients. ROC curves showed that the prognostic signature predicted the overall survival of ACC patients with good sensitivity and specificity. Furthermore, the TF-risk score was an independent prognostic factor.Conclusion: Taken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients.

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Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy

PeerJ ◽

10.7717/peerj.9621 ◽

2020 ◽

Vol 8 ◽

pp. e9621

Author(s):

Shanliang Zhong ◽

Huanwen Chen ◽

Sujin Yang ◽

Jifeng Feng ◽

Siying Zhou

Keyword(s):

Breast Cancer ◽

Cancer Survival ◽

Cox Regression ◽

Risk Group ◽

Breast Cancer Survival ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Breast Cancer Patients ◽

Prognostic Signature

We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade breast cancer. In conclusion, our 6-ARG signature was of significance in predicting of overall survival of patients with breast cancer. EIF4EBP1 may be a key ARG associated with breast cancer survival.

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Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

10.21203/rs.3.rs-24691/v2 ◽

2020 ◽

Author(s):

Hui Wang ◽

Xiaoling Ma ◽

Jinhui Liu ◽

Yicong Wan ◽

Yi Jiang ◽

...

Keyword(s):

Endometrial Cancer ◽

High Risk ◽

Risk Score ◽

Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Gene Set Enrichment ◽

Test Set ◽

Gene Set

Abstract Background: Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC. Methods: First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariat, LASSO, and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, gene set enrichment and somatic mutation analyses were also used for these prognostic autophagy-related genes. Results: A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (P<0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. Gene set enrichment analysis revealed high risk score was associated with tumor initiation and progression associated pathways. Conclusions: Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

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Identification of Prognostic Markers For Hepatocellular Carcinoma Based On the Epithelial-Mesenchymal Transition-Related Gene BIRC5

10.21203/rs.3.rs-390539/v1 ◽

2021 ◽

Author(s):

yan li ◽

yiping li ◽

rongzhong xu ◽

liubing lin ◽

bo zhang ◽

...

Keyword(s):

Risk Score ◽

Poor Prognosis ◽

Prognostic Markers ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Risk Group ◽

Enrichment Analysis ◽

Risk Groups ◽

Regression Analyses ◽

Mesenchymal Transition

Abstract Background: The baculoviral IAP repeat containing 5 (BIRC5) related to epithelial-mesenchymal transition (EMT) plays a crucial role in the pathogenesis of hepatocellular carcinoma (HCC). However, it remains unclear whether BIRC5-related genes can be used as prognostic markers of HCC. Methods: Kaplan-Meier (K-M) survival curve was used to assess the Overall Survival (OS) of high- and low-expression group divided by the median of BIRC5 expression. The differentially expressed genes (DEGs) between the two groups were screened using the limma package, and performed the functional enrichment analysis by the clusterProfiler package. WGCNA was used to analyze the relationship of the module and the clinical traits. The risk signature was constructed by univariate and multivariate Cox regression analyses and the enrichment analysis of genes in the risk signature was performed by the Intelligent pathway analysis (IPA). The immunophenoscore (IPS) and the tumor immune dysfunction and exclusion (TIDE) were used to estimate the clinical significance of the risk groups.Results: BIRC5 was high-expressed in HCC samples and associated with a poor prognosis (p-value < 0.0001). WGCNA screened 180 module genes which were overlapped with the 241 DEGs, ultimately getting 33 candidate genes. After the Cox regression analyses, CENPA, CDCA8, EZH2, KIF20A, KPNA2, CCNB1, KIF18B and MCM4 were preserved and used to construct risk signature, followed by calculating the risk score. The patients in high-risk groups stratified by median of the risk score were associated with a poor prognosis. The risk score had high accuracy [the area under the curve (AUC) >0.72] and was closely associated with clinicopathological characteristics of HCC patients. IPA suggested that the 8 genes were enriched in Cancer and Immunological disease related pathways. IPS and TIDE score indicated that the genes in low-risk group could cause an immune response, and patients in the low-risk group may be more sensitive to the immune checkpoint blockade (ICB) therapy.Conclusion: The risk score constructed by the 8 genes could not only predict the clinical outcome but also distinguish the cohort of ICB therapy in HCC, which exerted a vital value in treatment and prognosis of HCC.

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