Identifying and Validating an Acidosis-Related Signature Associated with Prognosis and Tumor Immune Infiltration Characteristics in Pancreatic Carcinoma

Background. Acidosis in the tumor microenvironment (TME) is involved in tumor immune dysfunction and tumor progression. We attempted to develop an acidosis-related index (ARI) signature to improve the prognostic prediction of pancreatic carcinoma (PC). Methods. Differential gene expression analyses of two public datasets (GSE152345 and GSE62452) from the Gene Expression Omnibus database were performed to identify the acidosis-related genes. The Cancer Genome Atlas–pancreatic carcinoma (TCGA-PAAD) cohort in the TCGA database was set as the discovery dataset. Univariate Cox regression and the Kaplan–Meier method were applied to screen for prognostic genes. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish the optimal model. The tumor immune infiltrating pattern was characterized by the single-sample gene set enrichment analysis (ssGSEA) method, and the prediction of immunotherapy responsiveness was conducted using the tumor immune dysfunction and exclusion (TIDE) algorithm. Results. We identified 133 acidosis-related genes, of which 37 were identified as prognostic genes by univariate Cox analysis in combination with the Kaplan–Meier method ( p values of both methods < 0.05). An acidosis-related signature involving seven genes (ARNTL2, DKK1, CEP55, CTSV, MYEOV, DSG2, and GBP2) was developed in TCGA-PAAD and further validated in GSE62452. Patients in the acidosis-related high-risk group consistently showed poorer survival outcomes than those in the low-risk group. The 5-year AUCs (areas under the curve) for survival prediction were 0.738 for TCGA-PAAD and 0.889 for GSE62452, suggesting excellent performance. The low-risk group in TCGA-PAAD showed a higher abundance of CD8+ T cells and activated natural killer cells and was predicted to possess an elevated proportion of immunotherapeutic responders compared with the high-risk counterpart. Conclusions. We developed a reliable acidosis-related signature that showed excellent performance in prognostic prediction and correlated with tumor immune infiltration, providing a new direction for prognostic evaluation and immunotherapy management in PC.

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A Novel Hypoxic-Angiogenesis-Immune-Related Gene Model for Prognostic and Therapeutic Effect Prediction in Hepatocellular Carcinoma Patients

Disease Markers ◽

10.1155/2022/9428660 ◽

2022 ◽

Vol 2022 ◽

pp. 1-27

Author(s):

Wen Lv ◽

Qi Yao

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Liver Cancer ◽

Cox Regression ◽

Risk Group ◽

Immune Dysfunction ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Prognostic Prediction

Background. Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant tumors that have been discovered so far, which makes the prognostic prediction difficult. The hypoxia, angiogenesis, and immunity-related genes (HAIRGs) are closely related to the development of liver cancer. However, the prognostic and treatment effect of hypoxia, angiogenesis, and immunity-related genes in HCC continues to be further clarified. Methods. The gene expression quantification data and clinical information in patients with liver cancer were downloaded from the TCGA database, and HAIRG signature was built by using the least absolute shrinkage and selection operator (LASSO) technique. Patient from the ICGC database validated the model. Then, tumor immune dysfunction and exclusion (TIDE) algorithm was applied to estimate the clinical response to immunotherapy and the sensitivity of drugs was evaluated by the half-maximal inhibitory concentration (IC50). Result. The HAIRGs were identified between the HCC patients and normal patients in the TCGA database. In univariate Cox regression analysis, seventeen differentially expressed genes (DEGs) were associated with overall survival (OS). An eight HAIRG signature model was constructed and was used to divide the patients into two groups according to the median value of the risk score base on the TCGA dataset. Patients in the high-risk group had a significant reduction in OS compared to those in the low-risk group ( P < 0.001 in the TCGA, P < 0.001 in the ICGC). For TCGA and ICGC databases of univariate Cox regression analyses, the risk score was used as an independent predictor of OS ( HR > 1 , P < 0.001 ). Functional analysis showed that the relevant immune pathways and immune responses were enriched, cellular component analysis showed that the immunoglobulin complex and other related substances were enriched, and immune status existed a difference in the high- and low-risk groups. Then, the tumor immune dysfunction and exclusion (TIDE) algorithm presented differences in immune response in the high- and low-risk groups ( P < 0.05 ), and based on drug sensitivity prediction, patients in the high-risk group were more sensitive to cisplatin compared to those in the low-risk group in both the TCGA and ICGC cohorts ( P < 0.05 ). Conclusions. HAIRG signature can be utilized for prognostic prediction in HCC, while it can be considered a prediction model for clinical evaluation of immunotherapy response and chemotherapy sensitivity in HCC.

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A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

BioMed Research International ◽

10.1155/2020/4845360 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Dakui Luo ◽

Zezhi Shan ◽

Qi Liu ◽

Sanjun Cai ◽

Qingguo Li ◽

...

Keyword(s):

Colon Cancer ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Differentially Expressed ◽

Metabolic Signature ◽

Metabolic Genes ◽

Prognostic Prediction

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.

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A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma

Frontiers in Oncology ◽

10.3389/fonc.2021.639874 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiaoxia Tong ◽

Xiaofei Qu ◽

Mengyun Wang

Keyword(s):

Gene Expression ◽

High Risk ◽

Risk Score ◽

Cutaneous Melanoma ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Rank Test ◽

Score Model

BackgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.MethodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.ResultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P < 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P < 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P< 0.001) and ulceration (P< 0.001).ConclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.

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Comprehensive analysis of the significance of ferroptosis-related genes in the prognosis and immunotherapy of oral squamous cell carcinoma

10.21203/rs.3.rs-115737/v1 ◽

2020 ◽

Author(s):

Junhao Yin ◽

Xiaoli Zeng ◽

Zexin Ai ◽

Miao Yu ◽

Yang'ou Wu ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Squamous Cell Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Oral Squamous Cell Carcinoma ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Regression Analyses

Abstract Background: Oral squamous cell carcinoma (OSCC) is a life-threatening disease that emerged as a major international health concern, associated with poor prognosis and the absence of specific biomarkers. Studies have shown that the ferroptosis-related genes (FRGs) can be used as tumor prognostic markers. However, FRGs’ prognostic value in OSCC needs further exploration. Our aim was to construct a novel FRG signature for overall survival (OS) prediction in OSCC patients and explore its role in immunotherapy.Methods: In our study, gene expression profile and clinical data of OSCC patients were collected from a public domain. FRGs were available from the FerrDb database. We performed univariate and multivariate Cox regression analyses to construct a multigene signature. The Kaplan-Meier (K-M) and receiver operating characteristic (ROC) methods were utilized to test the effectiveness of the FRG signature. A differential gene expression analysis was performed by the limma R package, followed by functional enrichment analyses. CIBERSORT was applied to analyze the tumor microenvironment (TME). Finally, the expression of human leukocyte antigen (HLA) and immune checkpoint molecules were analyzed to confirm the sensitivity of immunotherapy.Results: A total of 103 FRGs, expressed in OSCC (FRGs-OSCC), were identified from the two datasets by the Venn analysis. The Cox regression analysis identified 5 FRGs-OSCC that were associated with overall survival (all P < 0.01). The FRGs-OSCC risk model was established to classify patients into high risk and low risk groups. Compared with the low risk group, the survival time of the high-risk group was significantly reduced (P < 0.001). According to the multivariate Cox regression analyses, the risk score acted as an independent predictor for OS (HR > 1, P < 0.001). The accuracy of the FRGs-OSCC risk predictive model was confirmed by ROC curve analysis. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed significant enrichment of immune-related pathways, and a difference in tumor microenvironment between the two groups. The low risk group had the characteristics of higher expression of HLA and immune checkpoints (IDO1, LAG3, PDCD1 and TIGHT), a lower tumor purity and a higher infiltration of immune cells, indicating a more sensitive response to immunotherapy.Conclusions: The novel FRGs-OSCC risk score system can be used to predict OSCC prognosis. Ferroptosis targeting may be a therapeutic option for OSCC.

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A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer

BioMed Research International ◽

10.1155/2020/2147397 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Jia Li ◽

Huiyu Wang ◽

Zhaoyan Li ◽

Chenyue Zhang ◽

Chenxing Zhang ◽

...

Keyword(s):

Gene Expression ◽

High Risk ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Egfr Mutations ◽

Kaplan Meier ◽

Tumor Immune Microenvironment ◽

Nsclc Patients

Purpose. Establishing prognostic gene signature to predict clinical outcomes and guide individualized adjuvant therapy is necessary. Here, we aim to establish the prognostic efficacy of a gene signature that is closely related to tumor immune microenvironment (TIME). Methods and Results. There are 13,035 gene expression profiles from 130 tumor samples of the non-small cell lung cancer (NSCLC) in the data set GSE103584. A 5-gene signature was identified by using univariate survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) to build risk models. Then, we used the CIBERSORT method to quantify the relative levels of different immune cell types in complex gene expression mixtures. It was found that the ratio of dendritic cells (DCs) activated and mast cells (MCs) resting in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant (P<0.001 and P=0.03). Pathway enrichment results which were obtained by performing Gene Set Variation Analysis (GSVA) showed that the high-risk group identified by the 5-gene signature had metastatic-related gene expression, resulting in lower survival rates. Kaplan–Meier survival results showed that patients of the high-risk group had shorter disease-free survival (DFS) and overall survival (OS) than those of the low-risk group in the training set (P=0.0012 and P<0.001). The sensitivity and specificity of the gene signature were better and more sensitive to prognosis than TNM (tumor/lymph node/metastasis) staging, in spite of being not statistically significant (P=0.154). Furthermore, Kaplan–Meier survival showed that patients of the high-risk group had shorter OS and PFS than those of the low-risk group (P=0.0035, P<0.001, and P<0.001) in the validating set (GSE31210, GSE41271, and TCGA). At last, univariate and multivariate Cox proportional hazard regression analyses were used to evaluate independent prognostic factors associated with survival, and the gene signature, lymphovascular invasion, pleural invasion, chemotherapy, and radiation were employed as covariates. The 5-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses (P<0.001) (hazard ratio (HR): 3.93, 95% confidence interval CI (2.17–7.1), P=0.001, (HR) 5.18, 95% CI (2.6995–9.945), P<0.001), respectively. Our 5-gene signature was also related to EGFR mutations (P=0.0111), and EGFR mutations were mainly enriched in low-risk group, indicating that EGFR mutations affect the survival rate of patients. Conclusion. The 5-gene signature is a powerful and independent predictor that could predict the prognosis of NSCLC patients. In addition, our gene signature is correlated with TIME parameters, such as DCs activated and MCs resting. Our findings suggest that the 5-gene signature closely related to TIME could predict the prognosis of NSCLC patients and provide some reference for immunotherapy.

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Identification of Novel Tumor Microenvironment-Related Long Non-coding RNAs to Predict the Prognosis for Hepatocellular Carcinoma

10.21203/rs.3.rs-877901/v1 ◽

2021 ◽

Author(s):

Shenglan Huang ◽

Jian Zhang ◽

Dan Li ◽

Xiaolan Lai ◽

Lingling Zhuang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Clinicopathological Parameters ◽

Kaplan Meier ◽

The Impact

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Tumor microenvironment (TME) plays a vital role in the tumor progression of HCC. Thus, we aimed to analyze the association of TME with HCC prognosis, and construct an TME-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We firstly assessed the stromal/immune /Estimate scores within the HCC microenvironment using the ESTIMATE algorithm based on TCGA database, and its associations with survival and clinicopathological parameters were also analyzed. Then, different expression lncRNAs were filtered out according to immune/stromal scores. Cox regression was performed to built an TME-related lncRNAs risk signature. Kaplan–Meier analysis was carried out to explored the prognostic values of the risk signature. Furthermore, we explored the biological functions and immune microenvironment feathers in high- and low risk groups. Lastly, we probed the association of the risk signature with the treatment responses to immune checkpoint inhibitors (ICIs) in HCC by comparing the immunophenoscore (IPS).Results: Stromal/immune /Estimate scores of HCC patients were obtained based on the ESTIMATE algorithm. The Kaplan-Meier curve analysis showed the high stromal/immune/ Estimate scores were significantly associated with better prognosis of the HCC patients. Then, six TME-related lncRNAs were screened for constructing the prognosis model. Kaplan-Meier survival curves suggested that HCC patients in high-risk group had worse prognosis than those with low-risk. ROC curve and Cox regression analyses demonstrated the signature could predict HCC survival exactly and independently. Function enrichment analysis revealed that some tumor- and immune-related pathways associated with HCC tumorigenesis and progression might be activated in high-risk group. We also discovered that some immune cells, which were beneficial to enhance immune responses towards cancer, were remarkably upregulated in low-risk group. Besides, there was closely correlation of immune checkmate inhibitors (ICIs) with the risk signature and the signature can be used to predict treatment response of ICIs.Conclusions: We analyzed the impact of the tumor microenvironment scores on the prognosis of patients with HCC. A novel TME-related prognostic risk signature was established, which may improve prognostic predictive accuracy and guide individualized immunotherapy for HCC patients.

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Prognostic utility of neutrophil-to-lymphocyte ratio in patients with metastatic colorectal cancer treated using different modalities

Current Oncology ◽

10.3747/co.27.6573 ◽

2020 ◽

Vol 27 (5) ◽

Author(s):

G. Nogueira-Costa ◽

I. Fernandes ◽

R. Gameiro ◽

J. Gramaça ◽

A.T. Xavier ◽

...

Keyword(s):

Colorectal Cancer ◽

High Risk ◽

Metastatic Colorectal Cancer ◽

Real World ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

First Line ◽

Kaplan Meier

Introduction Inflammation is a critical component in carcinogenesis. The neutrophil-to-lymphocyte ratio (nlr) has been retrospectively studied as a biomarker of prognosis in metastatic colorectal cancer (mcrc). Compared with a low nlr, a high nlr is associated with worse prognosis. In the present study, we compared real-world survival for patients with mcrc based on their nlr group, and we assessed the utility of the nlr in determining first-line chemotherapy and metastasectomy benefit. Methods In this retrospective and descriptive analysis of patients with mcrc undergoing first-line chemotherapy in a single centre, the last systemic absolute neutrophil and lymphocyte count before treatment was used for the nlr. A receiver operating characteristic curve was used to estimate the nlr cut-off value, dividing the patients into low and high nlr groups. Median overall survival (mos) was compared using Kaplan–Meier curves and the log-rank test. A multivariate analysis was performed using a Cox regression model. Results The 102 analyzed patients had a median follow-up of 15 months. Regardless of systemic therapy, approximately 20% of patients underwent metastasectomy. The nlr cut-off was established at 2.35, placing 45 patients in the low-risk group (nlr < 2.35) and 57 in the high-risk group (nlr ≥ 2.35). The Kaplan–Meier analysis showed a mos of 39.1 months in the low-risk group and 14.4 months in the high-risk group (p < 0.001). Multivariate Cox regression on the nlr estimated a hazard ratio of 3.08 (p = 0.01). Survival analysis in each risk subgroup, considering the history of metastasectomy, was also performed. In the low-risk group, mos was longer for patients undergoing metastasectomy than for those not undergoing the procedure (95.2 months vs. 22.6 months, p = 0.05). In the high-risk group, mos was not statistically different for patients undergoing or not undergoing metastasectomy (24.3 months vs. 12.7 months, p = 0.08). Conclusions Our real-world data analysis of nlr in patients with mcrc confirmed that this biomarker is useful in predicting survival. It also suggests that nlr is an effective tool to choose first-line treatment and to predict the benefit of metastasectomy.

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Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.781307 ◽

2021 ◽

Vol 8 ◽

Author(s):

Shenglan Huang ◽

Jian Zhang ◽

Xiaolan Lai ◽

Lingling Zhuang ◽

Jianbing Wu

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Immune Cells ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Kaplan Meier ◽

Treatment Responses

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

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Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma

PeerJ ◽

10.7717/peerj.11074 ◽

2021 ◽

Vol 9 ◽

pp. e11074

Author(s):

Jin Duan ◽

Youming Lei ◽

Guoli Lv ◽

Yinqiang Liu ◽

Wei Zhao ◽

...

Keyword(s):

Gene Expression ◽

High Risk ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Cell Analysis ◽

Prognostic Signature

Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

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Development of a Novel Autophagy-Related Prognostic Signature and Nomogram for Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2020.591356 ◽

2020 ◽

Vol 10 ◽

Author(s):

Qiongxuan Fang ◽

Hongsong Chen

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Regression Analyses ◽

Gene Model

BackgroundHepatocellular carcinoma (HCC) is the seventh most common malignancy and the second most common cause of cancer-related deaths. Autophagy plays a crucial role in the development and progression of HCC.MethodsUnivariate and Lasso Cox regression analyses were performed to determine a gene model that was optimal for overall survival (OS) prediction. Patients in the GSE14520 and GSE54236 datasets of the Cancer Genome Atlas (TCGA) were divided into the high-risk and low-risk groups according to established ATG models. Univariate and multivariate Cox regression analyses were used to identify risk factors for OS for the purpose of constructing nomograms. Calibration and receiver operating characteristic (ROC) curves were used to evaluate model performance. Real-time PCR was used to validate the effects of the presence or absence of an autophagy inhibitor on gene expression in HepG2 and Huh7 cell lines.ResultsOS in the high-risk group was significantly shorter than that in the low-risk group. Gene set enrichment analysis (GSEA) indicated that the association between the low-risk group and autophagy- as well as immune-related pathways was significant. ULK2, PPP3CC, and NAFTC1 may play vital roles in preventing HCC progression. Furthermore, tumor environment analysis via ESTIMATION indicated that the low-risk group was associated with high immune and stromal scores. Based on EPIC prediction, CD8+ T and B cell fractions in the TCGA and GSE54236 datasets were significantly higher in the low-risk group than those in the high-risk group. Finally, based on the results of univariate and multivariate analyses three variables were selected for nomogram development. The calibration plots showed good agreement between nomogram prediction and actual observations. Inhibition of autophagy resulted in the overexpression of genes constituting the gene model in HepG2 and Huh7 cells.ConclusionsThe current study determined the role played by autophagy-related genes (ATGs) in the progression of HCC and constructed a novel nomogram that predicts OS in HCC patients, through a combined analysis of TCGA and gene expression omnibus (GEO) databases.

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