Identification and Validation of Three PDAC Subtypes and Individualized GSVA Immune Pathway-Related Prognostic Risk Score Formula in Pancreatic Ductal Adenocarcinoma Patients

Zhang, Deyu; Wang, Meiqi; Peng, Lisi; Yang, Xiaoli; Li, Keliang; Yin, Huabin; Xia, Chuanchao; Cui, Fang; Huang, Haojie; Jin, Zhendong

doi:10.1155/2021/4986227

“…Gene expression data from 472 melanoma tissues were extracted from mRNA expression data, data corrected using the R software limma package, and the abundance of infiltrating immune cells in each sample was estimated by single sample gene set enrichment analysis (R package GSVA) ( Zhang et al, 2021 ; Xu et al, 2022a ) and the MCPcounter algorithm. M1 macrophages, M2 macrophages, M0 macrophages, follicular helper T lymphocytes, unactivated CD4 + memory T lymphocytes, activated CD4 + memory T lymphocytes, γδ T lymphocytes, CD8 + T lymphocytes, regulatory T lymphocytes, naive CD4 + T lymphocytes, unactivated natural killer cells, activated natural killer cells, unactivated mast cells, activated mast cells, resting dendritic cells, activated dendritic cells, neutrophils, eosinophils.…”

Section: Methodsmentioning

confidence: 99%

“…Gene expression data from 472 melanoma tissues were extracted from mRNA expression data, data corrected using the R software limma package, and the abundance of infiltrating immune cells in each sample was estimated by single sample gene set enrichment analysis (R package GSVA) (Zhang et al, 2021;Xu et al, 2022a) and the MCPcounter algorithm.…”

Section: Estimation Of the Immune Microenvironment In Two Groups Of M...mentioning

confidence: 99%

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma

Shen¹,

Shang²,

Han³

et al. 2023

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Skin cutaneous melanoma (SKCM) is the skin cancer that causes the highest number of deaths worldwide. There is growing evidence that the tumour immune microenvironment is associated with cancer prognosis, however, there is little research on the role of immune status in melanoma prognosis. In this study, data on patients with Skin cutaneous melanoma were downloaded from the GEO, TCGA, and GTEx databases. Genes associated with the immune pathway were screened from published papers and lncRNAs associated with them were identified. We performed immune microenvironment and functional enrichment analyses. The analysis was followed by applying univariate/multivariate Cox regression algorithms to finally identify three lncRNAs associated with the immune pathway for the construction of prognostic prediction models (CXCL10, RXRG, and SCG2). This stepwise downscaling method, which finally screens out prognostic factors and key genes and then uses them to build a risk model, has excellent predictive power. According to analyses of the model’s reliability, it was able to differentiate the prognostic value and continued existence of Skin cutaneous melanoma patient populations more effectively. This study is an analysis of the immune pathway that leads lncRNAs in Skin cutaneous melanoma in an effort to open up new treatment avenues for Skin cutaneous melanoma.

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“…Besides, we assess the levels of immune cells of entire SKCM patients using the CIBERSORT algorithm ( Guan et al, 2022 ). Furthermore, we applied ssGSEA and GSVA analyses to explore the discrepancy between infiltrating fractions of immune cells and immune-related functions between the two groups ( Zhang et al, 2021a ; Xu et al, 2022 ). We analyzed tumor mutation burden (TMB) using the package “maftools” and divided all SKCM patients into high- and low-TMB groups according to the median TMB score.…”

Section: Methodsmentioning

confidence: 99%

Construction of five cuproptosis-related lncRNA signature for predicting prognosis and immune activity in skin cutaneous melanoma

Yang

¹

,

Wang

²

,

Sun

³

et al. 2022

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Cuproptosis is a newly discovered new mechanism of programmed cell death, and its unique pathway to regulate cell death is thought to have a unique role in understanding cancer progression and guiding cancer therapy. However, this regulation has not been studied in SKCM at present. In this study, data on Skin Cutaneous Melanoma (SKCM) patients were downloaded from the TCGA database. We screened the genes related to cuproptosis from the published papers and confirmed the lncRNAs related to them. We applied Univariate/multivariate and LASSO Cox regression algorithms, and finally identified 5 cuproptosis-related lncRNAs for constructing prognosis prediction models (VIM-AS1, AC012443.2, MALINC1, AL354696.2, HSD11B1-AS1). The reliability and validity test of the model indicated that the model could well distinguish the prognosis and survival of SKCM patients. Next, immune microenvironment, immunotherapy analysis, and functional enrichment analysis were also performed. In conclusion, this study is the first analysis based on cuproptosis-related lncRNAs in SKCM and aims to open up new directions for SKCM therapy.

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“…GSVA can detect the slight pathway activity changes within large number of gene sets [ 8 ]. It transforms the expression matrix of genes in different samples into the enrichment scores of gene sets to evaluate the enrichment of gene sets [ 9 ]. In this study, we used GSVA package in R to do gene set variation analysis, and a gene set c7.immunesigdb_HALLMARK related to immunity was used in GSVA.…”

Section: Methodsmentioning

confidence: 99%

“…The gene set variation analysis (GSVA) is a nonparametric and unsupervised gene set enrichment method, assaying the variation of gene set enrichment over sample population, thus condensing gene expression profiles into gene set or pathway summary [ 8 ]. Using GSVA can integrate the prognostic genes into a complex or pathways for advanced analysis, which can be more convenient for following statistics calculation and pathogenesis inferences [ 9 ]. GSVA method has been utilized in survival-associated gene mechanism researches for breast cancer [ 10 ], colon cancer [ 11 ], bladder cancer [ 12 ], and so on.…”

Section: Introductionmentioning

confidence: 99%

Identification of Specific Cervical Cancer Subtypes and Prognostic Gene Sets in Tumor and Nontumor Tissues Based on GSVA Analysis

Zhong

¹

,

Wang

²

,

Yin

³

et al. 2022

Journal of Oncology

4

0

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Background. Cervical cancer is the fourth common cancer among women. Its prognosis needs our more attention. Our purpose was to identity new prognostic gene sets to help other researchers develop more effective treatment for cervical cancer patients and improve the prognosis of patients. Methods. We used gene set variation analysis (GSVA) to calculate the enrichment scores of gene sets and identified three subtypes of cervical cancer through the Cox regression model, k-means clustering algorithm, and nonnegative matrix factorization method (NMF). Chi-square test was utilized to test whether a certain clinical characteristic is different among divided subtypes. We further screened the prognostic gene sets using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to analyze which pathways and function the genes from screened gene sets enriched. Search Tool for the Retrieval of Interacting Genes (STRING) was used to draw the protein-protein interaction network, and Cytoscape was used to visualize the hub genes of protein-protein interaction network. Results. We identified three novel subtypes of cervical cancer in The Cancer Genome Atlas (TCGA) samples and validated in Gene Expression Omnibus (GEO) samples. There were significant variations between the three subtypes in histological type, T stage, M stage, and N stage. T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP and N_HALLMARK_ANGIOGENESIS were screened prognostic gene sets. The prognostic model was as follows: riskScore = T _ GSE 36888 _ UNTREATED _ VS _ IL 2 _ TREATED _ STAT 5 _ AB _ KNOCKIN _ TCELL _ 2 H _ U P ∗ 2.617 + N _ HALLMARK _ ANGIOGENESI S ∗ 4.860 . Survival analysis presented that in these two gene sets, high enrichment scores were all significantly related to worse overall survival. The hub genes from T gene set included CXCL1, CXCL2, CXCL8, ALDOA, TALDO1, LDHA, CCL4, FCAR, FCER1G, SAMSN1, LILRB1, SH3PXD2B, PPM1N, PKM, and FKBP4. As for N gene sets, the hub genes included ITGAV, PTK2, SPP1, THBD, and APOH. Conclusions. Three novel subtypes and two prognostic gene sets were identified. 15 hub genes for T gene set and 5 hub genes for N gene set were discovered. Based on these findings, we can develop more and more effective treatments for cervical cancer patients. Based on the gene enriched pathways, we can development specific drugs targeting the pathways.

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Identification and Validation of Three PDAC Subtypes and Individualized GSVA Immune Pathway-Related Prognostic Risk Score Formula in Pancreatic Ductal Adenocarcinoma Patients

Cited by 18 publications

References 35 publications

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma

Construction of five cuproptosis-related lncRNA signature for predicting prognosis and immune activity in skin cutaneous melanoma

Identification of Specific Cervical Cancer Subtypes and Prognostic Gene Sets in Tumor and Nontumor Tissues Based on GSVA Analysis

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