scholarly journals Tumor Microenvironment Characteristics of Pancreatic Cancer to Determine Prognosis and Immune-Related Gene Signatures

2021 ◽  
Vol 8 ◽  
Author(s):  
Congjun Zhang ◽  
Jun Ding ◽  
Xiao Xu ◽  
Yangyang Liu ◽  
Wei Huang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Tumor Microenvironment ◽  
Immune Cell ◽  
Inhibitory Effect ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Diagnosis And Prognosis ◽  
Gap Statistic ◽  
New Strategy ◽  
Immune Cell Subpopulations

Background: Pancreatic cancer (PC) is one of the most lethal types of cancer with extremely poor diagnosis and prognosis, and the tumor microenvironment plays a pivotal role during PC progression. Poor prognosis is closely associated with the unsatisfactory results of currently available treatments, which are largely due to the unique pancreatic tumor microenvironment (TME).Methods: In this study, a total of 177 patients with PC from The Cancer Genome Atlas (TCGA) cohort and 65 patients with PC from the GSE62452 cohort in Gene Expression Omnibus (GEO) were included. Based on the proportions of 22 types of infiltrated immune cell subpopulations calculated by cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the TME was classified by K-means clustering and differentially expressed genes (DEGs) were determined. A combination of the elbow method and the gap statistic was used to explore the likely number of distinct clusters in the data. The ConsensusClusterPlus package was utilized to identify radiomics clusters, and the samples were divided into two subtypes.Result: Survival analysis showed that the patients with TMEscore-high phenotype had better prognosis. In addition, the TMEscore-high had better inhibitory effect on the immune checkpoint. A total of 10 miRNAs, 311 DEGs, and 68 methylation sites related to survival were obtained, which could be biomarkers to evaluate the prognosis of patients with pancreatic cancer.Conclusions: Therefore, a comprehensive description of TME characteristics of pancreatic cancer can help explain the response of pancreatic cancer to immunotherapy and provide a new strategy for cancer treatment.

scholarly journals An independent poor-prognosis subtype of hepatocellular carcinoma based on the tumor microenvironment

2021 ◽  
Vol 49 (2) ◽  
pp. 030006052098064
Author(s):  
Junfeng Wang ◽  
Jianying Lou ◽  
Lei Fu ◽  
Qu Jin
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Poor Prognosis ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Using Data

Background Hepatocellular carcinoma (HCC) is a highly malignant tumor with a particularly poor prognosis. The tumor microenvironment (TME) is closely associated with tumorigenesis, progression, and treatment. However, the relationship between TME genes and HCC patient prognosis is poorly understood. Methods In this study, we identified two prognostic subtypes based on the TME using data from The Cancer Genome Atlas and Gene Expression Omnibus. The Microenvironment Cell Populations-counter method was used to evaluate immune cell infiltration in HCC. Differentially expressed genes between molecular subtypes were calculated with the Limma package, and clusterProfiler was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses to identify genes related to the independent subtypes. We also integrated mRNA expression data into our bioinformatics analysis. Results We identified 4227 TME-associated genes and 640 genes related to the prognosis of HCC. We defined two major subtypes (Clusters 1 and 2) based on the analysis of TME-associated gene expression. Cluster 1 was characterized by increased expression of immune-associated genes and a worse prognosis than Cluster 2. Conclusions The identification of these HCC subtypes based on the TME provides further insight into the molecular mechanisms and prediction of HCC prognosis.

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

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.

scholarly journals Screening the genome for HCC-specific CpG methylation signatures as biomarkers for diagnosis and prognosis evaluation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Rui-kun Zhang ◽  
Jia-lin Liu
Keyword(s):  
Cox Regression ◽  
Malignant Tumors ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Training Set ◽  
Cpg Sites ◽  
Diagnosis And Prognosis ◽  
Patient Prognosis ◽  
Prognosis Evaluation

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and invasive malignant tumors in the world. The change in DNA methylation is a key event in HCC. Methods Methylation datasets for HCC and 17 other types of cancer were downloaded from The Cancer Genome Atlas (TCGA). The CpG sites with large differences in methylation between tumor tissues and paracancerous tissues were identified. We used the HCC methylation dataset downloaded from the TCGA as the training set and removed the overlapping sites among all cancer datasets to ensure that only CpG sites specific to HCC remained. Logistic regression analysis was performed to select specific biomarkers that can be used to diagnose HCC, and two datasets—GSE157341 and GSE54503—downloaded from GEO as validation sets were used to validate our model. We also used a Cox regression model to select CpG sites related to patient prognosis. Results We identified 6 HCC-specific methylated CpG sites as biomarkers for HCC diagnosis. In the training set, the area under the receiver operating characteristic (ROC) curve (AUC) for the model containing all these sites was 0.971. The AUCs were 0.8802 and 0.9711 for the two validation sets from the GEO database. In addition, 3 other CpG sites were analyzed and used to create a risk scoring model for patient prognosis and survival prediction. Conclusions Through the analysis of HCC methylation datasets from the TCGA and Gene Expression Omnibus (GEO) databases, potential biomarkers for HCC diagnosis and prognosis evaluation were ascertained.

scholarly journals Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Wei Han ◽  
Biao Huang ◽  
Xiao-Yu Zhao ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Metastatic Melanoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Subtype Identification ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

Identification of Immune-Related Prognostic Biomarkers in Pancreatic Cancer

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.

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

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.

scholarly journals Comprehensive analysis of tumor microenvironment landscape and potential therapeutic agents based on tumor-infiltrating immune cells in colorectal cancer

2021 ◽  
Author(s):  
Wenhui Zhong ◽  
Feng Zhang ◽  
Xin Lu ◽  
Kaijun Huang ◽  
Junming Bi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Transforming Growth Factor ◽  
Immune Cell ◽  
Cancer Therapeutics ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Chemotherapy Drugs ◽  
Drug Compounds

Abstract Background: Tumor-infiltrating immune cells (TIIC) are the major components of the tumor microenvironment (TME) and play vital roles in the tumorigenesis and progression of colorectal cancer (CRC). Increasing evidence has elucidated their significances in predicting prognosis and therapeutic efficacy. Nonetheless, the immune infiltrative landscape of CRC remains largely unknown. Methods: All the RNA-seq transcriptome data and full clinical annotation of 1213 colorectal cancer patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene-Expression Omnibus (GEO) database. The “CIBERSORT” and “estimate” R package were applied to calculate 22 infiltrated immune cell fractions and stromal and immune score. Three TIIC patterns were determined by Unsupervised clustering methods. Through using principal-component analysis, TIIC scores were established. Data for potential agents comes from the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) and Cancer Therapeutics Response Portal database (CTRP). Results：In this study, we identified three distinct TIIC patterns characterized by distinct immunological features in 1213 CRC samples from multiple platforms. Base on the TIIC-related gene signatures from three clusters, we constructed a scoring system to quantify the immune infiltration level of individual samples in the CRC cohort and the clinical benefits of different groups. The high TIIC score group was marked by increased immune activation status and favorable prognosis. Conversely, low TIIC score group was featured with immune-desert phenotype and poor prognosis, along with the activation of transforming growth factor-β (TGF-β), WNT, ECM receptor interaction, and VEGF signaling pathways. Meanwhile, the high TIIC score group was also correlated with enhanced efficacy of immunotherapy. Additional, four chemotherapy drugs, seven CTRP-derived drug compounds and six PRISM-derived drug compounds were identified as potential drug for CRC among high and low TIIC subgroups.Conclusions: Collectively, as an effective prognostic biomarker and predictive indicator, the TIIC score plays an important role in the evaluation of CRC prognosis and the response of immunotherapy. Investigation of the TIIC patterns might provide us a promising target for improving immunotherapeutic efficacy in CRC.

scholarly journals A Novel Ferroptosis-Related lncRNAs Signature Predicts Clinical Prognosis and is Associated with Immune Landscape in Pancreatic Cancer

2021 ◽  
Author(s):  
Haiqin Ping ◽  
Xingqing Jia ◽  
Hengning Ke
Keyword(s):  
Pancreatic Cancer ◽  
Immune Cell ◽  
Pearson Correlation ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Clinical Prognosis ◽  
Pathway Enrichment ◽  
Cancer Genome Atlas ◽  
Pearson Correlation Analysis ◽  
Selection Operator

Abstract Pancreatic cancer is one of the most lethal malignancies and currently therapies are severely lacking. In this study, we aimed to establish a novel ferroptosis-related lncRNAs signature to predict the prognosis of patients with pancreatic cancer and evaluate the predictive abilities of candidate lncRNAs. According to The Cancer Genome Atlas (TCGA) database, a total of 182 patients with pancreatic cancer were included in our study. Ferroptosis-related lncRNAs were screened by Pearson correlation analysis with 60 reported ferroptosis-related genes. Through univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate regression analyses, a novel signature based on five ferroptosis-related lncRNAs(ZNF236-DT, CASC8, PAN3-AS1, SH3PXD2A-AS1, LINP1) was constructed. Risk-related differentially expressed genes (DEGs) were subjected to enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.The results revealed that that immune cell infiltration, immune-related functions and checkpoints were factors to affect prognoisis of pancreatic cancer. In summary, we identified the prognostic ferroptosis-related lncRNAs in pancreatic cancer and these lncRNAs may serve as therapeutic targets for pancreatic cancer.

scholarly journals Effects of Hypoxia in Intestinal Tumors on Immune Cell Behavior in the Tumor Microenvironment

2020 ◽  
Author(s):  
Luping Zhang ◽  
Shaokun Wang ◽  
Yachen Wang ◽  
Weidan Zhao ◽  
Yingli Zhang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Groups ◽  
Low Risk ◽  
Computational Method ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Behavior

Abstract Background: Imbalanced nutritional supply and demand in the tumor microenvironment often leads to hypoxia. The subtle interaction between hypoxia and immune cell behavior plays an important role in tumor occurrence and development. However, the functional relationship between hypoxia and the tumor microenvironment remains unclear. Therefore, we aimed to investigate the effect of hypoxia on the intestinal tumor microenvironment.Method: We extracted the names of hypoxia-related genes from the Gene Set Enrichment Analysis (GSEA) database and screened them for those associated with the prognosis of colorectal cancer, with the final list including ALDOB, GPC1, ALDOC, and SLC2A3. Using the sum of the expression levels of these four genes, provided by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and the expression coefficients, we developed a hypoxia risk score model. Using the median risk score value, we divided the patients in the two databases into high- and low-risk groups.GSEA was used to compare the enrichment differences between the two groups.We used the CIBERSORT computational method to analyze immune cell infiltration.Finally,the correlation between these five genes and hypoxia was analyzed. Result: The prognosis of the two groups differed significantly, with a higher survival rate in the low-risk group than in the high-risk group.We found that the different risk groups were enriched by immune-related and inflammatory pathways. We identified activated CD4 memory T cells and M0 macrophages in TCGA and GEO databases and found that CCL2/4/5, CSF1, and CX3CL1 contributed toward the increased infiltration rate of these immune cell types. Finally, we observed a positive correlation between the five candidate genes’ expression and the risk of hypoxia, with significant differences in the level of expression of each of these genes between patient risk groups.Conclusion: Overall, our data suggest that hypoxia is associated with the prognosis and rate of immune system infiltration in patients with colorectal cancer. This finding may improve immunotherapy for colorectal cancer.

scholarly journals Immune implication of an autophagy-related prognostic signature in uveal melanoma

2021 ◽  
Author(s):  
Samuel Chuah ◽  
Valerie Chew
Keyword(s):  
Uveal Melanoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Lasso Regression ◽  
Cancer Hallmarks ◽  
Selection Operator ◽  
Stat3 Signalling

Uveal Melanoma (UM) is a rare cancer deriving from melanocytes within the uvea. It has a high rate of metastasis, especially to the liver, and a poor prognosis thereafter. Autophagy, an intracellular programmed digestive process, has been associated with the development and progression of cancers, with controversial pro- and anti-tumour roles. Although previous studies have been conducted on autophagy-related genes (ARGs) in various cancer types, its role in UM requires a deeper understanding for improved diagnosis and development of novel therapeutics. In the current study, Zheng et al. used univariate Cox regression followed by least absolute shrinkage and selection operator (Lasso) regression to identify a robust 9-ARG signature prognostic of survival in a total of 230 patients with UM. The authors used the Cancer Genome Atlas (TCGA) UM cohort as a training cohort (n=80) to identify the signature and validated it in another four independent cohorts of 150 UM patients from the Gene Expression Omnibus (GEO) repository (GSE22138, GSE27831, GSE44295 and GSE84976). This 9-ARG signature was also significantly associated with the enrichment of cancer hallmarks, including angiogenesis, IL6-KJAK-STAT3 signalling, reactive oxygen species pathway and oxidative phosphorylation. More importantly, this signature is associated with immune-related functional pathways and immune cell infiltration. Thus, this 9-ARG signature predicts prognosis and provides deeper insights into the immune mechanisms in UM, with potential implications for future immunotherapy.

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