Aerial View of the Association between m6A-Related LncRNAs and Clinicopathological Characteristics of Pancreatic Cancer

Abstract Background There have been few reports on how long non-coding RNA (lncRNA) under the regulation of N6-methyladenosine (m6A) modification influences pancreatic cancer progression. In our study, the association between m6A-related lncRNAs and pancreatic ductal adenocarcinoma (PDAC) was comprehensively described for the first time based on the construction of a lncRNAs prognostic model. Methods The lncRNAs expression level and the prognostic value were investigated in 440 PDAC patients and 171 normal tissues from Genotype-Tissue Expression (GTEx), The Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) databases. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, univariate Cox regression and Kaplan-Meier (K-M) methods were performed to screen the critical lncRNAs in PDAC patients. Then we used bioinformatic analysis and statistical analysis to illustrate the association between m6A-related lncRNAs and pancreatic cancer. Results Seven prognostic m6A-related lncRNAs were identified as prognostic lncRNAs, and they were inputted in the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to establish an m6A-related lncRNAs prognostic model in the TCGA database. Each patient has calculated a risk score and divided into low-risk and high-risk subgroups by the median value in two cohorts. Moreover, the model showed a robust prognostic ability in the stratification analysis of different risk subgroups, pathological grades, and recurrence events. The Cox regression demonstrated that the risk classification was an independent prognostic predictor. We established a competing endogenous RNA (ceRNA) network based on seven pivotal lncRNAs and twenty-six m6A regulators. Enrichment analysis indicated that malignancy-associated biological function and signaling pathways were enriched in the high-risk subgroup and m6A-related lncRNAs target mRNAs. We have even identified small molecule drugs that may affect the progression of pancreatic cancer. Conclusions In conclusion, we provide the first comprehensive aerial view between m6A-related lncRNAs and pancreatic cancer's clinicopathological characteristics.

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Aerial View of the Association Between m6A-Related LncRNAs and Clinicopathological Characteristics of Pancreatic Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.812785 ◽

2022 ◽

Vol 11 ◽

Author(s):

Bowen Huang ◽

Jianzhou Liu ◽

Jun Lu ◽

Wenyan Gao ◽

Li Zhou ◽

...

Keyword(s):

Pancreatic Cancer ◽

High Risk ◽

Cancer Progression ◽

Prognostic Model ◽

Cox Regression ◽

Pearson Correlation ◽

Clinicopathological Characteristics ◽

Survival Prognosis ◽

Pancreatic Cell ◽

Aerial View

Pancreatic cancer is a highly malignant tumor with a poor survival prognosis. We attempted to establish a robust prognostic model to elucidate the clinicopathological association between lncRNA, which may lead to poor prognosis by influencing m6A modification, and pancreatic cancer. We investigated the lncRNAs expression level and the prognostic value in 440 PDAC patients and 171 normal tissues from GTEx, TCGA, and ICGC databases. The bioinformatic analysis and statistical analysis were used to illustrate the relationship. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, univariate Cox regression and Kaplan-Meier methods were performed to identify the seven prognostic lncRNAs signatures. We inputted them in the LASSO Cox regression to establish a prognostic model in the TCGA database, verified in the ICGC database. The AUC of the ROC curve of the training set is 0.887, while the validation set is 0.711. Each patient has calculated a risk score and divided it into low-risk and high-risk subgroups by the median value. Moreover, the model showed a robust prognostic ability in the stratification analysis of different risk subgroups, pathological grades, and recurrence events. We established a ceRNA network between lncRNAs and m6A regulators. Enrichment analysis indicated that malignancy-associated biological function and signaling pathways were enriched in the high-risk subgroup and m6A-related lncRNAs target mRNA. We have even identified small molecule drugs, such as Thapsigargin, Mepacrine, and Ellipticine, that may affect pancreatic cancer progression. We found that seven lncRNAs were highly expressed in tumor patients in the GTEx-TCGA database, and LncRNA CASC19/UCA1/LINC01094/LINC02323 were confirmed in both pancreatic cell lines and FISH relative quantity. We provided a comprehensive aerial view between m6A-related lncRNAs and pancreatic cancer’s clinicopathological characteristics, and performed experiments to verify the robustness of the prognostic model.

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Differentially expressed liver exosome-related genes as a candidate prognostic biomarker for hepatocellular carcinoma

10.21203/rs.3.rs-105334/v1 ◽

2020 ◽

Author(s):

Bangyou Zuo ◽

Haitao Zhao ◽

Jin Bian ◽

Junyun Long ◽

Xu Yang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Cell Communication ◽

High Risk Group ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Differentially Expressed

Abstract Background The function of exosome includes cell-to-cell communication, neovascularization, and metastasis of cancer cell and drug resistance, which plays an important part in the occurrence and progression of hepatocellular carcinoma (HCC). Because the mechanism in this area is less studied, our goal is to identify exosome-related genes in HCC, establish a reliable prognostic model for liver cancer patients, and explore its underlying mechanisms. Methods The exoRbase database and The Cancer Genome Atlas (TCGA) database were used to analyze differentially expressed genes (DEGs). Cox regression and LASSO analysis were applied to determine DEGs closely related to overall survival (OS). Then the exosome-related prognostic model was constructed in TCGA and validated in the database of International Cancer Genome Consortium (ICGC). Nomogram graph was performed to predict the survival. CIBERSORT was used to estimate the score of different type of immune cells. DEGs related to immunotherapy are used to predict the effect of immunotherapy. Results 48 exosome-related DEGs were obtained and five genes (XPO1, IFI30, FBXO16, CALM1, MORC3) among them were selected to construct predictive model. Then we divided the HCC patients into low-risk and high-risk groups by the best cut-off value according to the X-tile software. The high-risk related to exosome were significantly associated with a poor prognosis. Moreover, the features related to exosome could positively regulate immune response. At the same time, the proportion of T cell regulatory factors (Tregs) and macrophages M2 is higher in the high-risk group, and high-risk group exhibited higher expression of immune checkpoint molecular including PD-L1, PD-L2, TIGIT, and IDO1. Conclusions Overall, our research showed that markers related to exosomes were potential biomarkers for the prognosis of HCC, providing an immunological perspective for the development of precision treatment.

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Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.728062 ◽

2021 ◽

Vol 12 ◽

Author(s):

Dongjie Chen ◽

Hui Huang ◽

Longjun Zang ◽

Wenzhe Gao ◽

Hongwei Zhu ◽

...

Keyword(s):

High Risk ◽

Pancreatic Ductal Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Pearson Correlation ◽

Risk Groups ◽

Low Risk ◽

Ductal Adenocarcinoma ◽

Prognostic Signature ◽

Score Model

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

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Screening and Identifying m6A Regulators as an Independent Prognostic Biomarker in Pancreatic Cancer Based on The Cancer Genome Atlas Database

BioMed Research International ◽

10.1155/2021/5573628 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Bi Lin ◽

Yangyang Pan ◽

Dinglai Yu ◽

Shengjie Dai ◽

Hongwei Sun ◽

...

Keyword(s):

Pancreatic Cancer ◽

Cox Regression ◽

Malignant Tumors ◽

Expression Profiles ◽

Tumor Therapy ◽

Disease Free Survival ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Cancer Genome Atlas ◽

Genome Atlas

Background. Pancreatic cancer is one of the most malignant tumors of the digestive system, and its treatment has rarely progressed for the last two decades. Studies on m6A regulators for the past few years have seemingly provided a novel approach for malignant tumor therapy. m6A-related factors may be potential biomarkers and therapeutic targets. This research is focused on the gene characteristics and clinical values of m6A regulators in predicting prognosis in pancreatic cancer. Methods. In our study, we obtained gene expression profiles with copy number variation (CNV) data and clinical characteristic data of 186 patients with pancreatic cancer from The Cancer Genome Atlas (TCGA) portal. Then, we determined the alteration of m6a regulators and their correlation with clinicopathological features using the log-rank tests, Cox regression model, and chi-square test. Additionally, we validated the prognostic value of m6A regulators in the International Cancer Genome Consortium (ICGC). Results. The results suggested that pancreatic cancer patients with ALKBH5 CNV were associated with worse overall survival and disease-free survival than those with diploid genes. Additionally, upregulation of the writer gene ALKBH5 had a positive correlation with the activation of AKT pathways in the TCGA database. Conclusion. Our study not only demonstrated genetic characteristic changes of m6A-related genes in pancreatic cancer and found a strong relationship between the changes of ALKBH5 and poor prognosis but also provided a novel therapeutic target for pancreatic cancer therapy.

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A Reliable Prognostic Model for HCC Using Histological Grades and the Expression Levels of Related Genes

Journal of Oncology ◽

10.1155/2021/9512774 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Hao Zhang ◽

Renzheng Liu ◽

Lin Sun ◽

Xiao Hu

Keyword(s):

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Histological Grade ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Liver Malignancy ◽

Expression Levels ◽

Proposed Model

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and is a leading cause of cancer-related death worldwide. This study aimed to establish a reliable prognostic model for HCC using histological grades and the expression levels of related genes. The histological grade of a tumor provides prognostic information. The expression data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) database. We employed the univariate and multivariate Cox regression analyses, as well as the least absolute shrinkage and selection operator (LASSO) regression to establish the prognostic model. After verification of the proposed model using data downloaded from the International Cancer Genome Consortium (ICGC) database, we found that the model was highly reliable, and it was revealed that the prognosis in the high-risk group was significantly worse than that in the low-risk group. Next, we explored the correlation of RiskScore with patients’ clinicopathological characteristics, and we found that the RiskScore could be used as an independent prognostic factor, which further confirmed the reliability of our model. In summary, the proposed model could accurately predict the prognosis of HCC patients, assisting clinicians to study the roles of different histological grades of HCC.

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A New Stemness-Related Prognostic Model for Predicting the Prognosis in Pancreatic Ductal Adenocarcinoma

BioMed Research International ◽

10.1155/2021/6669570 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Xiao-Yan Huang ◽

Wen-Tao Qin ◽

Qi-Sheng Su ◽

Cheng-Cheng Qiu ◽

Ruo-Chuan Liu ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Prognostic Model ◽

Cox Regression ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Ductal Adenocarcinoma ◽

Cox Regression Analysis ◽

Calibration Curves ◽

Key Genes

Objective. This study is aimed at identifying stemness-related genes in pancreatic ductal adenocarcinoma (PDAC). Methods. The RNA-seq data of PADC patients were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The mRNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC patients were evaluated. The mRNAsi-related gene sets in PADC were identified by weighted gene coexpression network analysis (WGCNA). The key genes were further analyzed using functional enrichment analysis. The Kaplan-Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognostic hub genes were used to establish nomograms. The receiver operating characteristic (ROC) curves, concordance index ( C -index), and calibration curves were used to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database. Results. A total of 36 key genes related to mRNAsi were identified by WGCNA. A prognostic gene signature compromising seven genes (TPX2, ZWINT, UBE2C, CCNB2, CDK1, BUB1, and BIRC5) was established to predict the overall survival (OS) of PADC patients. The Cox regression analysis revealed that the risk score was an independent prognostic factor for PADC. Patients were then divided into the high-risk and low-risk groups. The ROC curves, C -index, and calibration curves indicated good performance of the prognostic signature in the TCGA and GEO datasets. Moreover, the nomogram incorporating clinical parameters showed better sensitivity and specificity for predicting the OS of PADC patients. Conclusion. The stemness-related prognostic model successfully predicted the OS of PADC patients and could be used for the treatment of PADC.

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Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.561254 ◽

2021 ◽

Vol 12 ◽

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang

Keyword(s):

Pancreatic Cancer ◽

Overall Survival ◽

Lymph Node ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

External Validation ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis

Recently, growing evidence has revealed the significant effect of reprogrammed metabolism on pancreatic cancer in relation to carcinogenesis, progression, and treatment. However, the prognostic value of metabolism-related genes in pancreatic cancer has not been fully revealed. We identified 379 differentially expressed metabolic-related genes (DEMRGs) by comparing 178 pancreatic cancer tissues with 171 normal pancreatic tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx) databases. Then, we used univariate Cox regression analysis together with Lasso regression for constructing a prognostic model consisting of 15 metabolic genes. The unified risk score formula and cutoff value were taken into account to divide patients into two groups: high risk and low risk, with the former exhibiting a worse prognosis compared with the latter. The external validation results of the International Cancer Genome Consortium (IGCC) cohort and the Gene Expression Omnibus (GEO) cohort further confirm the effectiveness of this prognostic model. As shown in the receiver operating characteristic (ROC) curve, the area under curve (AUC) values of the risk score for overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were 0.871, 0.885, and 0.886, respectively. Based on the Gene Set Enrichment Analysis (GSEA), the 15-gene signature can affect some important biological processes and pathways of pancreatic cancer. In addition, the prognostic model was significantly correlated with the tumor immune microenvironment (immune cell infiltration, and immune checkpoint expression, etc.) and clinicopathological features (pathological stage, lymph node, and metastasis, etc.). We also built a nomogram based on three independent prognostic predictors (including individual neoplasm status, lymph node metastasis, and risk score) for the prediction of 1-, 3-, and 5-year OS of pancreatic cancer, which may help to further improve the treatment strategy of pancreatic cancer.

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N6-Methyladenosine-Related lncRNAs As Potential Biomarkers For Predicting The Prognoses And Immune Responses In Patients With Cervical Cancer

10.21203/rs.3.rs-538180/v1 ◽

2021 ◽

Author(s):

He Zhang ◽

Weimin Kong ◽

Chao Han ◽

Tingting Liu ◽

Jing Li ◽

...

Keyword(s):

Cervical Cancer ◽

Overall Survival ◽

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Cancer Genome ◽

Cell Infiltration ◽

Risk Scores ◽

Biological Processes ◽

Programmed Death

Abstract Background: Several recent studies have confirmed to us the epigenetic regulation of the immune response. However, the potential role of RNA N6-methyladenosine (m6A) modifications in cervical cancer and its tumor microenvironment (TME) cell infiltration remains unclear.Results: We evaluated and analyzed m6A modification patterns in 307 cervical cancer samples from The Cancer Genome Atlas (TCGA) dataset based on 13 m6A regulators. Pearson correlation analysis was used to identify lncRNAs associated with m6A, followed by univariate Cox regression analysis to screen their prognostic role in cervical cancer patients. We also correlated TME cell infiltration characteristics with modification patterns. We screened six m6A-associated lncRNAs as prognostic lncRNAs and established the prognostic profile of m6A-associated lncRNAs by least absolute shrinkage and choice of operator (LASSO) Cox regression. The corresponding risk scores of patients were derived based on their prognostic features, and the correlation between this feature model and disease prognosis was analyzed. The prognostic model constructed based on the TCGA-CESC (The Cancer Genome Cervical squamous cell carcinoma and endocervical adenocarcinoma) dataset showed strong prognostic power in the stratified analysis and was confirmed as an independent prognostic indicator for predicting overall survival of patients with CESC. Principal component analysis showed that low- and high-risk subgroups had significant m6A status. Enrichment analysis showed that biological processes, pathways, and markers associated with malignancy were more common in the high-risk subgroup. Risk scores were strongly correlated with tumor grade. ECM receptor interaction, pathways in cancer were enriched in cluster 2 while oxidative phosphorylation and other biological processes in cluster 1. The expression of immune checkpoint molecules including PD-1 (programmed death 1) and PD-L1 (programmed death ligand 1) was significantly increased in the high-risk subgroup, suggesting that this prognostic model could be a predictor of immunotherapy.Conclusions: This study reveals that m6A modifications play an integral role in the diversity and complexity of TME formation. Assessing the m6A modification patterns of individual tumors will help improve our understanding of TME infiltration characteristics and thus guide immunotherapy more effectively. We also developed an independent prognostic model based on m6A-associated lncRNA as a predictor of overall survival, which can also be used as a predictor of immunotherapy.

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Comprehensive Analysis to Identify the Epithelial–Mesenchymal Transition-Related Immune Signatures as a Prognostic and Therapeutic Biomarkers in Hepatocellular Carcinoma

Frontiers in Surgery ◽

10.3389/fsurg.2021.742443 ◽

2021 ◽

Vol 8 ◽

Author(s):

Guozhi Wu ◽

Yuan Yang ◽

Yu Zhu ◽

Yemao Li ◽

Zipeng Zhai ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Cancer Genome ◽

Mesenchymal Transition ◽

Immune Genes

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with the high rates of the morbidity and mortality due to the lack of the effective prognostic model for prediction.Aim: To construct a risk model composed of the epithelial–mesenchymal transition (EMT)-related immune genes for the assessment of the prognosis, immune infiltration status, and chemosensitivity.Methods: We obtained the transcriptome and clinical data of the HCC samples from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) databases. The Pearson correlation analysis was applied to identify the differentially expressed EMT-related immune genes (DE-EMTri-genes). Subsequently, the univariate Cox regression was introduced to screen out the prognostic gene sets and a risk model was constructed based on the least absolute shrinkage and selection operator-penalized Cox regression. Additionally, the receiver operating characteristic (ROC) curves were plotted to compare the prognostic value of the newly established model compared with the previous model. Furthermore, the correlation between the risk model and survival probability, immune characteristic, and efficacy of the chemotherapeutics were analyzed by the bioinformatics methods.Results: Six DE-EMTri-genes were ultimately selected to construct the prognostic model. The area under the curve (AUC) values for 1-, 2-, and 3- year were 0.773, 0.721, and 0.673, respectively. Stratified survival analysis suggested that the prognosis of the low-score group was superior to the high-score group. Moreover, the univariate and multivariate analysis indicated that risk score [hazard ratio (HR) 5.071, 95% CI 3.050, 8.432; HR 4.396, 95% CI 2.624, 7.366; p < 0.001] and stage (HR 2.500, 95% CI 1.721, 3.632; HR 2.111, 95% CI 1.443, 3.089; p < 0.001) served as an independent predictive factors in HCC. In addition, the macrophages, natural killer (NK) cells, and regulatory T (Treg) cells were significantly enriched in the high-risk group. Finally, the patients with the high-risk score might be more sensitive to cisplatin, doxorubicin, etoposide, gemcitabine, and mitomycin C.Conclusion: We established a reliable EMTri-genes-based prognostic signature, which may hold promise for the clinical prediction.

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Exploring the Pyroptosis-related Genes of Breast Cancer and their Effects on Tumor Immune Microenvironment

10.21203/rs.3.rs-1049643/v1 ◽

2021 ◽

Author(s):

Congli Jia ◽

Fu Yang ◽

Ruining Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment

Abstract Background: Breast cancer （BC） is the most common cancer among women, with high rates of metastasis and recurrence. Some studies have confirmed that pyroptosis is an immune-related programmed cell death. However, the correlation between the expression of pyroptosis-related genes in BC and its prognosis remains unclear. Methods: In this study, we identified 38 pyroptosis-related genes that were differentially expressed between BC and normal tissues. The prognostic value of each pyroptosis-related gene was evaluated using patient data from The Cancer Genome Atlas (TCGA). The Cox regression method was performed to establish a prognostic model for 16-gene signature, classifying all BC patients in the TCGA database into a low-or high-risk group. Results: The survival rate of BC patients in the high-risk group was significantly lower than that in the low-risk group (P＜0.01). Prognostic model is independent prognostic factor for BC patients compared to clinical features. Single sample gene set enrichment analysis (ssGSEA) showed a decrease for immune cells and immune function in the high-risk group. Conclusions: Pyroptosis-related genes influence the tumor immune microenvironment and can predict the prognosis of BC.

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