S100A2 Is a Prognostic Biomarker Involved in Immune Infiltration and Predict Immunotherapy Response in Pancreatic Cancer

Pancreatic cancer (PC) is a highly fatal and aggressive disease with its incidence and mortality quite discouraging. It is of great significance to construct an effective prognostic signature of PC and find the novel biomarker for the optimization of the clinical decision-making. Due to the crucial role of immunity in tumor development, a prognostic model based on nine immune-related genes was constructed, which was proved to be effective in The Cancer Genome Atlas (TCGA) training set, TCGA testing set, TCGA entire set, GSE78229 set, and GSE62452 set. Furthermore, S100A2 (S100 Calcium Binding Protein A2) was identified as the gene occupying the most paramount position in risk model. Gene set enrichment analysis (GSEA), ESTIMATE and CIBERSORT algorithm revealed that S100A2 was closely associated with the immune status in PC microenvironment, mainly related to lower proportion of CD8+T cells and activated NK cells and higher proportion of M0 macrophages. Meanwhile, patients with high S100A2 expression might get more benefit from immunotherapy according to immunophenoscore algorithm. Afterwards, our independent cohort was also used to demonstrate S100A2 was an unfavorable marker of PC, as well as its remarkably positive correlation with the expression of PD-L1. In conclusion, our results demonstrate S100A2 might be responsible for the preservation of immune-suppressive status in PC microenvironment, which was identified with significant potentiality in predicting prognosis and immunotherapy response in PC patients.

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Establishment of Novel Prostate Cancer Risk Subtypes and A Twelve-Gene Prognostic Model

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.676138 ◽

2021 ◽

Vol 8 ◽

Author(s):

Enchong Zhang ◽

Fujisawa Shiori ◽

Mo Zhang ◽

Peng Wang ◽

Jieqian He ◽

...

Keyword(s):

Prostate Cancer ◽

Prognostic Model ◽

Clinical Decision Making ◽

Immune Cell ◽

Prostate Cancer Risk ◽

Enrichment Analysis ◽

Clinical Decision ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Consensus Clustering

Prostate cancer (PCa) is the most common malignancy among men worldwide. However, its complex heterogeneity makes treatment challenging. In this study, we aimed to identify PCa subtypes and a gene signature associated with PCa prognosis. In particular, nine PCa-related pathways were evaluated in patients with PCa by a single-sample gene set enrichment analysis (ssGSEA) and an unsupervised clustering analysis (i.e., consensus clustering). We identified three subtypes with differences in prognosis (Risk_H, Risk_M, and Risk_L). Differences in the proliferation status, frequencies of known subtypes, tumor purity, immune cell composition, and genomic and transcriptomic profiles among the three subtypes were explored based on The Cancer Genome Atlas database. Our results clearly revealed that the Risk_H subtype was associated with the worst prognosis. By a weighted correlation network analysis of genes related to the Risk_H subtype and least absolute shrinkage and selection operator, we developed a 12-gene risk-predicting model. We further validated its accuracy using three public datasets. Effective drugs for high-risk patients identified using the model were predicted. The novel PCa subtypes and prognostic model developed in this study may improve clinical decision-making.

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Mind Bomb 1 Promotes Pancreatic Cancer Proliferation by Activating β-Catenin Signaling

Journal of Nanoscience and Nanotechnology ◽

10.1166/jnn.2020.18755 ◽

2020 ◽

Vol 20 (12) ◽

pp. 7276-7282

Author(s):

Xiao Fu ◽

Neng Tang ◽

Weiqi Xie ◽

Liang Mao ◽

Yudong Qiu

Keyword(s):

Pancreatic Cancer ◽

Cancer Metastasis ◽

Mrna Level ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Pancreatic Tumors ◽

Mrna Levels ◽

Cancer Proliferation

Mind bomb 1 (MIB1), an E3 ligase, plays a vital role in chemo-resistance and cancer metastasis. According to The Cancer Genome Atlas (TCGA), MIB1 gene is preferentially amplified in pancreatic cancer. Copy number alterations in MIB1 gene are associated with worse survival. Gene Expression Omnibus (GEO) also showed that pancreatic cancer with high mRNA level of MIB1 tend to be more resistant to gemcitabine and higher mRNA levels of MIB1 are found in pancreatic tumors compared with adjacent normal tissues. MIB1 knockdown (KD) in Panc-1 and HPAF2 cell lines significantly inhibit proliferation and colony formation of pancreatic cancer. The gene set enrichment analysis (GSEA) has also showed that β-catenin is the downstream of MIB1. Western blot analysis showed that total and active β-catenin levels are decreased in MIB1 KD cells. β-catenin inhibitor also inhibits proliferation of Panc-1 and HPAF2 cells. We in this study implanted HPAF2 scramble and MIB1 KD cells orthotopically in athymic nude mice. Gemcitabine was used to treat the mice. Results revealed that after MIB1 KD HPAF2 cells were more sensitive to gemcitabine. In conclusion, we demonstrated that MIB1 promotes pancreatic cancer proliferation through activating β-catenin signaling. MIB1 may thus be a therapeutic target in pancreatic cancer therapy.

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Construction of a CXC Chemokine-Based Prediction Model for the Prognosis of Colon Cancer

BioMed Research International ◽

10.1155/2020/6107865 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Kaisheng Liu ◽

Minshan Lai ◽

Shaoxiang Wang ◽

Kai Zheng ◽

Shouxia Xie ◽

...

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Overall Survival ◽

Prediction Model ◽

Enrichment Analysis ◽

Forecast Model ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Characteristic Analysis ◽

Incidence And Mortality

Colon cancer is the third most common cancer, with a high incidence and mortality. Construction of a specific and sensitive prediction model for prognosis is urgently needed. In this study, profiles of patients with colon cancer with clinical and gene expression data were downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA). CXC chemokines in patients with colon cancer were investigated by differential expression gene analysis, overall survival analysis, receiver operating characteristic analysis, gene set enrichment analysis (GSEA), and weighted gene coexpression network analysis. CXCL1, CXCL2, CXCL3, and CXCL11 were upregulated in patients with colon cancer and significantly correlated with prognosis. The area under curve (AUC) of the multigene forecast model of CXCL1, CXCL11, CXCL2, and CXCL3 was 0.705 in the GSE41258 dataset and 0.624 in TCGA. The prediction model was constructed using the risk score of the multigene model and three clinicopathological risk factors and exhibited 92.6% and 91.8% accuracy in predicting 3-year and 5-year overall survival of patients with colon cancer, respectively. In addition, by GSEA, expression of CXCL1, CXCL11, CXCL2, and CXCL3 was correlated with several signaling pathways, including NOD-like receptor, oxidative phosphorylation, mTORC1, interferon-gamma response, and IL6/JAK/STAT3 pathways. Patients with colon cancer will benefit from this prediction model for prognosis, and this will pave the way to improve the survival rate and optimize treatment for colon cancer.

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Identification of a Ubiquitination-Related Gene Risk Model for Predicting Survival in Patients With Pancreatic Cancer

Frontiers in Genetics ◽

10.3389/fgene.2020.612196 ◽

2020 ◽

Vol 11 ◽

Author(s):

Hao Zuo ◽

Luojun Chen ◽

Na Li ◽

Qibin Song

Keyword(s):

Pancreatic Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Transcriptional Level ◽

Cox Regression Analysis

Pancreatic cancer is known as “the king of cancer,” and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.

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Comprehensive analysis of metabolism-related lncRNAs related to the progression and prognosis in osteosarcoma from TCGA

Journal of Orthopaedic Surgery and Research ◽

10.1186/s13018-021-02647-4 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Xingyin Chen ◽

Zhengyun Ye ◽

Pan Lou ◽

Wei Liu ◽

Ying Liu

Keyword(s):

Prognostic Factors ◽

Molecular Mechanisms ◽

Clinical Decision Making ◽

Predictive Ability ◽

Enrichment Analysis ◽

Clinical Decision ◽

Gene Set Enrichment Analysis ◽

Malignant Neoplasms ◽

Prognostic Signature ◽

Prognostic Analysis

Abstract Background Osteosarcoma is one of the most common malignant neoplasms in children and adolescents. Studies have shown that metabolism-related pathways are vital for the development and metastasis of osteosarcoma. Long non-coding RNA (lncRNA) plays a key role in the occurrence and progression of cancer in a variety of ways. However, the detailed molecular mechanisms of metabolism-related lncRNA in osteosarcoma remain to be deeply elucidated. Methods In this study, all metabolism-related mRNAs and lncRNAs in osteosarcoma were extracted and identified based on transcriptomic data from the TCGA database. Usingsurvival analysis, univariate and multivariate independent prognostic analysis, gene set enrichment analysis, and nomogram, a prognostic signature with metabolic lncRNAs as prognostic factors was constructed. Results Nine prognostic factors included lncRNA AC009779.2, lncRNA AL591895.1, lncRNA AC026271.3, lncRNA LPP-AS2, lncRNA LINC01857, lncRNA AP005264.1, lncRNA LINC02454, lncRNA AL133338.1, and lncRNA AC135178.5, respectively. Survival analysis indicated that alterations of specific lncRNA expression were strongly correlated with poor prognosis in osteosarcoma. Univariate and multivariate independent prognostic analysis showed that the prognostic signature had a good independent predictive ability for patient survival. The results of GSEA suggested that these predictors may be involved in the metabolism of certain substances or energy in cancer. The nomogram was further drawn for clinical guidance and assistance in clinical decision-making. Conclusions This study identified multiple metabolism-related lncRNAs, which may be novel therapeutic targets for osteosarcoma, and contributed to better explore the specific metabolic regulatory mechanisms of lncRNA in osteosarcoma.

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Comprehensive Analysis of Metabolism-Related lncRNA Related To The Progression And Prognosis In Osteosarcoma From TCGA

10.21203/rs.3.rs-461677/v1 ◽

2021 ◽

Author(s):

Xingyin Chen ◽

Zhengyun Ye ◽

Pan Lou ◽

Wei Liu ◽

Ying Liu

Keyword(s):

Survival Analysis ◽

Molecular Mechanisms ◽

Clinical Decision Making ◽

Malignant Neoplasm ◽

Enrichment Analysis ◽

Clinical Decision ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Prognostic Analysis

Abstract Background: Osteosarcoma is one of the most common malignant neoplasm among children and adolescents. Studies have shown that metabolism-related pathways are more important for the development and metastasis of osteosarcoma. Long non-coding RNA (LncRNA) plays a key role in the occurrence and progression of cancer in a variety of ways, Metabolism-related lncRNA-mediated molecular mechanisms in the regulation of osteosarcoma have not been fully elucidated.Methods: In this study, all metabolic-related mRNAs and metabolic-related LncRNA in osteosarcoma were extracted and identified based on transcriptomic data from the TCGA database. The survival analysis, The univariable and multivariable independent prognostic analysis, The results of gene set enrichment analysis (GSEA) and the nomogram were used to construct a prognosis signature with metabolic LncRNA as prognostic factor.Results: 9 prognostic factors including that LncRNA AC009779.2, LncRNA AL591895.1, LncRNA AC026271.3, LncRNA LPP-AS2, LncRNA LINC01857, LncRNA AP005264.1, LncRNA LINC02454, LncRNA AL133338.1 and LncRNA AC135178.5, respectively. The survival analysis showed that the difference in expression of an individual LncRNA was closely related to poor prognosis in osteosarcoma. The univariable and multivariable independent prognostic analysis showed that the signature had good independent predictive ability for patient survival. The results of gene set enrichment analysis (GSEA) suggest that these predictors may be involved in the metabolism of certain substances or energy in cancer. The nomogram is further drawn for clinical guidance and assistance in clinical decision-making.Conclusions: This study identified multiple metabolic-related lncRNA that can be considered as novel therapeutic targets for osteosarcoma and contribute to better exploring the specific regulatory mechanisms of lncRNA in the metabolism of osteosarcoma.

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DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009562 ◽

2021 ◽

Vol 17 (11) ◽

pp. e1009562

Author(s):

Maxim Barenboim ◽

Michal Kovac ◽

Baptiste Ameline ◽

David T. W. Jones ◽

Olaf Witt ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Random Forest ◽

Homologous Recombination ◽

Clinical Decision Making ◽

Genome Instability ◽

Enrichment Analysis ◽

Clinical Decision ◽

Gene Set Enrichment Analysis ◽

Molecular Features

Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85% and the average area under the ROC curve (AUC) was 0.95. There were 449 upregulated and 1,079 downregulated genes in the BRCAness-positive group (fdr < 0.05). GSEA of upregulated genes detected enrichment of DNA replication and mismatch repair and homologous recombination signatures (FWER < 0.05). Validation of the BRCAness classifier with an independent OS set (n = 20) collected later in the course of study showed AUC of 0.87 with an accuracy of 90%. GSEA signatures computed for this test set were matching the ones observed in the training set enrichment analysis. In conclusion, we developed a new classifier based on DNA-methylation patterns that detects BRCAness in OS samples with high accuracy. GSEA identified genome instability signatures. Machine-learning and gene expression approaches add new epigenomic and transcriptomic aspects to already established genomic methods for evaluation of BRCAness in osteosarcoma and can be extended to cancers characterized by genome instability.

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Identification of hub genes and construction of an mRNA-miRNA-lncRNA network of gastric carcinoma using integrated bioinformatics analysis

PLoS ONE ◽

10.1371/journal.pone.0261728 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0261728

Author(s):

Gang Wei ◽

Youhong Dong ◽

Zhongshi He ◽

Hu Qiu ◽

Yong Wu ◽

...

Keyword(s):

Gene Expression ◽

Gastric Carcinoma ◽

Regulatory Network ◽

Messenger Rna ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Incidence And Mortality

Background Gastric carcinoma (GC) is one of the most common cancer globally. Despite its worldwide decline in incidence and mortality over the past decades, gastric cancer still has a poor prognosis. However, the key regulators driving this process and their exact mechanisms have not been thoroughly studied. This study aimed to identify hub genes to improve the prognostic prediction of GC and construct a messenger RNA-microRNA-long non-coding RNA(mRNA-miRNA-lncRNA) regulatory network. Methods The GSE66229 dataset, from the Gene Expression Omnibus (GEO) database, and The Cancer Genome Atlas (TCGA) database were used for the bioinformatic analysis. Differential gene expression analysis methods and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify a common set of differentially co-expressed genes in GC. The genes were validated using samples from TCGA database and further validation using the online tools GEPIA database and Kaplan-Meier(KM) plotter database. Gene set enrichment analysis(GSEA) was used to identify hub genes related to signaling pathways in GC. The RNAInter database and Cytoscape software were used to construct an mRNA-miRNA-lncRNA network. Results A total of 12 genes were identified as the common set of differentially co-expressed genes in GC. After verification of these genes, 3 hub genes, namely CTHRC1, FNDC1, and INHBA, were found to be upregulated in tumor and associated with poor GC patient survival. In addition, an mRNA-miRNA-lncRNA regulatory network was established, which included 12 lncRNAs, 5 miRNAs, and the 3 hub genes. Conclusions In summary, the identification of these hub genes and the establishment of the mRNA-miRNA-lncRNA regulatory network provide new insights into the underlying mechanisms of gastric carcinogenesis. In addition, the identified hub genes, CTHRC1, FNDC1, and INHBA, may serve as novel prognostic biomarkers and therapeutic targets.

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LncRNAs specifically overexpressed in endocervical adenocarcinoma are associated with an unfavorable recurrence prognosis and the immune response

PeerJ ◽

10.7717/peerj.12116 ◽

2021 ◽

Vol 9 ◽

pp. e12116

Author(s):

Yong Song ◽

Long Nie ◽

Yu-Ting Zhang

Keyword(s):

Cervical Cancer ◽

Immune Responses ◽

Noncoding Rnas ◽

Enrichment Analysis ◽

Cervical Squamous Cell Carcinoma ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Endocervical Adenocarcinoma ◽

Incidence And Mortality ◽

The Relationship

Background Cervical cancer is the fourth most common gynecological tumor in terms of both the incidence and mortality of females worldwide. Cervical squamous cell carcinoma (CSCC) accounts for 70–80% of cervical cancers, and endocervical adenocarcinoma (EAC) accounts for 20–25%. Unlike CSCC, EAC has worse clinical outcomes and prognosis. In this study, we explored the relationship between various types of long noncoding RNAs (lncRNAs) and pathological types of cervical cancer. Methods RNA sequencing (RNA-Seq) and clinical data from The Cancer Genome Atlas (TCGA) were used in this study. A single-sample gene set enrichment analysis (ssGSEA) and the ESTIMATE package were used to assess lncRNA activity and immune responses, respectively. RT-qPCR was performed to verify our findings. Results We explored the relationship between various types of lncRNAs and pathological types of cervical cancer. A series of long intergenic noncoding RNAs (lincRNAs) and antisense RNAs, which are the major types of lncRNAs, were identified to be specifically expressed in EAC and associated with a poor recurrence prognosis in patients with cervical cancer, suggesting that they might serve as independent prognostic markers of recurrence in patients with cervical cancer. RT-qPCR was performed to verify the 10 EAC-specific lncRNAs in cervical cancer samples we collected. Furthermore, the overexpression of these lncRNAs was positively correlated with EAC pathology levels but negatively correlated with immune responses in the microenvironment of cervical cancer. Conclusions These lncRNAs potentially represent new biomarkers for the prediction of the recurrence prognosis and help obtain deeper insights into potential immunotherapeutic approaches for treating cervical cancer.

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ADAMTS12 acts as a tumor microenvironment related cancer promoter in gastric cancer

Scientific Reports ◽

10.1038/s41598-021-90330-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yangming Hou ◽

Yingjuan Xu ◽

Dequan Wu

Keyword(s):

Gastric Cancer ◽

Tumor Microenvironment ◽

Immune Cell ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Tumor Promoter ◽

Protein Protein Interaction ◽

Oxidative Phosphorylation Pathway ◽

Cox Proportional Hazard Regression

AbstractThe infiltration degree of immune and stromal cells has been shown clinically significant in tumor microenvironment (TME). However, the utility of stromal and immune components in Gastric cancer (GC) has not been investigated in detail. In the present study, ESTIMATE and CIBERSORT algorithms were applied to calculate the immune/stromal scores and the proportion of tumor-infiltrating immune cell (TIC) in GC cohort, including 415 cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were screened by Cox proportional hazard regression analysis and protein–protein interaction (PPI) network construction. Then ADAMTS12 was regarded as one of the most predictive factors. Further analysis showed that ADAMTS12 expression was significantly higher in tumor samples and correlated with poor prognosis. Gene Set Enrichment Analysis (GSEA) indicated that in high ADAMTS12 expression group gene sets were mainly enriched in cancer and immune-related activities. In the low ADAMTS12 expression group, the genes were enriched in the oxidative phosphorylation pathway. CIBERSORT analysis for the proportion of TICs revealed that ADAMTS12 expression was positively correlated with Macrophages M0/M1/M2 and negatively correlated with T cells follicular helper. Therefore, ADAMTS12 might be a tumor promoter and responsible for TME status and tumor energy metabolic conversion.

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