An immune infiltration signature to predict the overall survival of patients with colon cancer

Peng, Dengfa; Wang, Lianjun; Li, Huqing; Cai, Changsong; Tan, Yong Sheng; Xu, Bin; Le, Hao

doi:10.1002/iub.2124

Cited by 56 publications

(49 citation statements)

References 46 publications

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“…Macrophages were the most majority tumor immune-infiltrating cells in GBM, including M0, M1, and M2 cells. It is generally considered that M0 macrophages are unactivated and without specific function (19). While M0 can differentiate into M1 and M2 under different stimulations, and they, respectively, exhibit inflammatory response against tumor cells and immunosuppressive response promoting tumor cells proliferation and differentiation (23).…”

Section: Discussionmentioning

confidence: 99%

“…LASSO Cox analysis, as a wildly used high-dimensional predictor regression method (18), selecting the optimal penalty parameter lambda using 10-fold cross-validations to prevent overfitting (19), can achieve shrinkage and variable identify simultaneously (20), and thus, which is an appropriate solution to establish signatures if there are numerous correlated covariates (21). Therefore, we utilized LASSO Cox regression analysis in the training set to establish an IIRPSS by a linear combination of selected prognostic cell compositions among 22 immune cell types weighted by the optimal coefficients.…”

Section: Construction An Immune Infiltration-related Prognostic Scorimentioning

confidence: 99%

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Development of an Immune Infiltration-Related Prognostic Scoring System Based on the Genomic Landscape Analysis of Glioblastoma Multiforme

Tang

2020

Front. Oncol.

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Introduction: Glioblastoma multiforme (GBM) is the most common deadly brain malignancy and lacks effective therapies. Immunotherapy acts as a promising novel strategy, but not for all GBM patients. Therefore, classifying these patients into different prognostic groups is urgent for better personalized management. Materials and Methods: The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to estimate the fraction of 22 types of immune-infiltrating cells, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune infiltration-related prognostic scoring system (IIRPSS). Additionally, a quantitative predicting survival nomogram was also established based on the immune risk score (IRS) derived from the IIRPSS. Moreover, we also preliminarily explored the differences in the immune microenvironment between different prognostic groups. Results: There was a total of 310 appropriate GBM samples (239 from TCGA and 71 from CGGA) included in further analyses after CIBERSORT filtering and data processing. The IIRPSS consisting of 17 types of immune cell fractions was constructed in TCGA cohort, the patients were successfully classified into different prognostic groups based on their immune risk score (p = 1e-10). What's more, the prognostic performance of the IIRPSS was validated in CGGA cohort (p = 0.005). The nomogram also showed a superior predicting value. (The predicting AUC for 1-, 2-, and 3-year were 0.754, 0.813, and 0.871, respectively). The immune microenvironment analyses reflected a significant immune response and a higher immune checkpoint expression in high-risk immune group. Conclusion: Our study constructed an IIRPSS, which maybe valuable to help clinicians select candidates most likely to benefit from immunological checkpoint inhibitors (ICIs) and laid the foundation for further improving personalized immunotherapy in patients with GBM.

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Section: Discussionmentioning

confidence: 99%

Section: Construction An Immune Infiltration-related Prognostic Scorimentioning

confidence: 99%

Development of an Immune Infiltration-Related Prognostic Scoring System Based on the Genomic Landscape Analysis of Glioblastoma Multiforme

Tang

2020

Front. Oncol.

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“…Several studies based on the scRNA-seq of mouse lung tissue have already reported on the complexity of lung composition [29,32,[38][39][40]. Alternatively, some bioinformatic methods, such as CIBERSORT, can be used to characterize cell composition of complex tissues from their bulk transcriptome data [41] and have previously been successfully used to enumerate immune subsets in the tumor microenvironment [42][43][44][45][46][47]. CIBERSORTx, an updated version of CIBERSORT, is a machine learning method that can infer cell-type-specific gene expression profiles without physical cell isolation, as well as dissect large-scale tissue data using scRNA-seq data [28].…”

Section: Discussionmentioning

confidence: 99%

Deconvolution of RNA-Seq Analysis of Hyperbaric Oxygen-Treated Mice Lungs Reveals Mesenchymal Cell Subtype Changes

Yuan

Zhou

et al. 2020

IJMS

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Hyperbaric oxygen (HBO) is widely applied to treat several hypoxia-related diseases. Previous studies have focused on the immediate effect of HBO-exposure induced oxidative stress on the lungs, but knowledge regarding the chronic effects from repetitive HBO exposure is limited, especially at the gene expression level. We found that repetitive HBO exposure did not alter the morphology of murine lungs. However, by deconvolution of RNA-seq from those mice lungs using CIBERSORTx and the expression profile matrices of 8 mesenchymal cell subtypes obtained from bleomycin-treated mouse lungs, we identify several mesenchymal cell subtype changes. These include increases in Col13a1 matrix fibroblasts, mesenchymal progenitors and mesothelial cell populations and decreases in lipofibroblasts, endothelial and Pdgfrb high cell populations. Our data suggest that repetitive HBO exposure may affect biological processes in the lungs such as response to wounding, extracellular matrix, vasculature development and immune response.

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“…Misregulated RNA expression profiles, including autophagy‐related gene sets, 38 metabolism‐associated gene sets, 39 immune gene sets, 40 hypoxia‐associated gene sets, 41 microRNAs 42 and long non‐coding RNAs, 43 have all been shown to affect disease progression and prognosis in CRC. In this study, we demonstrated that dysregulation of RPGs could allow the stratification of CRC patients based on different outcomes.…”

Section: Discussionmentioning

confidence: 99%

RNA processing genes characterize RNA splicing and further stratify colorectal cancer

Zhou

Meng

et al. 2020

Cell Proliferation

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Objectives Due to the limited evaluation of the prognostic value of RNA processing genes (RPGs), which are regulators of alternative splicing events (ASEs) that have been shown to be associated with tumour progression, this study sought to determine whether colorectal cancer (CRC) could be further stratified based on the expression pattern of RPGs. Materials and Methods The gene expression profiles of CRCs were collected from TCGA (training set) and three external validation cohorts, representing 1060 cases totally. Cox regression with least absolute shrinkage and selection operator (LASSO) penalty was used to develop an RNA processing gene index (RPGI) risk score. Kaplan‐Meier curves, multivariate Cox regression and restricted mean survival (RMS) analyses were harnessed to evaluate the prognostic value of the RPGI. Results A 22‐gene RPGI signature was developed, and its risk score served as a strong independent prognostic factor across all data sets when adjusted for major clinical variables. Moreover, ASEs for certain genes, such as FGFR1 and the RAS oncogene family, were significantly correlated with RPGI. Expression levels of genes involved in splicing‐ and tumour‐associated pathways were significantly correlated with RPGI score. Furthermore, a combination of RPGI with age and tumour stage resulted in significantly improved prognostic accuracy. Conclusions Our findings highlighted the prognostic value of RPGs for risk stratification of CRC patients and provide insights into specific ASEs associated with the development of CRC.

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An immune infiltration signature to predict the overall survival of patients with colon cancer

Cited by 56 publications

References 46 publications

Development of an Immune Infiltration-Related Prognostic Scoring System Based on the Genomic Landscape Analysis of Glioblastoma Multiforme

Development of an Immune Infiltration-Related Prognostic Scoring System Based on the Genomic Landscape Analysis of Glioblastoma Multiforme

Deconvolution of RNA-Seq Analysis of Hyperbaric Oxygen-Treated Mice Lungs Reveals Mesenchymal Cell Subtype Changes

RNA processing genes characterize RNA splicing and further stratify colorectal cancer

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