Immune cell infiltration as a biomarker for the diagnosis and prognosis of stage I–III colon cancer

Zhou, Rui; Zhang, Jingwen; Zeng, Dongqiang; Sun, Huiying; Rong, Xiaoxiang; Shi, Min; Bin, Jianping; Liao, Yulin; Liao, Wangjun

doi:10.1007/s00262-018-2289-7

Cited by 233 publications

(209 citation statements)

References 30 publications

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“…The high AUC value indicated that DIS was an effective predictor of diagnosis, indicating that the immune system participated in tumor carcinogenesis. Similarly, Zhou et al established a diagnostic immune risk score for the diagnosis of colon cancer. This method opens the door to new diagnostic strategies from the perspective of immune inflammation.…”

Section: Discussionmentioning

confidence: 99%

Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer

et al. 2019

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The digestive system cancers are aggressive cancers with the highest mortality worldwide. In this study, we undertook a systematic investigation of the tumor immune microenvironment to identify diagnostic and prognostic biomarkers. The fraction of 22 immune cell types of patients were estimated using CIBERSORT. The least absolute shrinkage and selection operator (LASSO) analysis was carried out to identify important immune predictors. By comparing immune cell compositions in 801 tumor samples and 46 normal samples, we constructed the diagnostic immune score (DIS), showing high specificity and sensitivity in the training (area under the receiver operating characteristic curve [AUC] = 0.929), validation (AUC = 0.935), and different cancer type cohorts (AUC > 0.70 for all).We also established the prognostic immune score (PIS), which was an effective prognostic factor for relapse-free survival in training, validation, and entire cohorts (P < .05). In addition, PIS provided a higher net benefit than TNM stage. A composite nomogram was built based on PIS and patients' clinical information with well-fitted calibration curves (c-index = 0.84). We further used other cohorts from Gene Expression Omnibus databases and obtained similar results, confirming the reliability and validity of the DIS and PIS. In addition, the unsupervised clustering analysis using immune cell proportions revealed 6 immune subtypes, suggesting that the immune types defined as having relatively high levels of M0 or/and M1 macrophages were the high-risk subtypes of relapse. In conclusion, this study comprehensively analyzed the tumor immune microenvironment and identified DIS and PIS for digestive system cancers. K E Y W O R D Sdiagnosis, digestive system cancer, immune cell, prognosis, TCGA

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Section: Discussionmentioning

confidence: 99%

Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer

et al. 2019

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“…We uploaded normalized gene expression data with standard annotation files to the Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) web portal, and the fractions of 22 immune cell types using 1,000 permutations and the LM22 gene signature as previously described. 29 The R package "Genefilter" was used to screen each sample, and the threshold was set at p-value < 0.05. The final CIBERSORT output was subsequently analyzed.…”

Section: Estimation Of Tumor-infiltrating Immune Cellsmentioning

confidence: 99%

A TP53 -associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma

Lin

et al. 2020

OncoImmunology

106

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The tumor-suppressor gene tumor protein p53 (TP53) is one of the most commonly mutated genes in human lung cancer, and TP53 mutations are associated with a worsened prognosis and causes resistance to cancer therapy. RNA sequencing and TP53 mutation data were downloaded to determine specific TP53associated signature based on differentially expressed genes between patients with lung squamous cell carcinoma (LUSC) with wild type (TP53 WT) and mutated (TP53 MUT) TP53. We investigated the predictive value of this signature on the immune microenvironment, tumor mutational burden (TMB), and likelihood of response to immunotherapy and chemotherapy. In total, 1,556 differentially expressed genes were identified based on TP53 mutation status. Three genes (KLK6, MUC22 and CSN1S1) identified by univariate and multivariate Cox regression analyses, comprised the prognostic signature which was an independent and specific prognostic marker of overall survival in patients with LUSC. A nomogram was also established to validate this signature for clinical use. Moreover, the high-risk group was characterized by increased infiltration of monocytes and macrophages M1, and decreased T cells CD8 and T cells follicular helper. Highrisk group exhibited a higher TMB, and was much more sensitive to immunotherapy and chemotherapy. KLK6 and CSN1S1 expression and the prognostic prediction values were further validated in clinical samples. The derived TP53-associated signature is a specific and independent prognostic biomarker for LUSC patients, and could provide potential prognostic biomarker or therapeutic targets for the development of novel immunotherapies and chemotherapies.

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“…The results of the predicted infiltrating immune cell fractions with an appropriate p < 0.05 were considered to be accurate, which were eligible for further analysis. For each sample, all the output estimates of each immune cell type were normalized to sum up to 1 (13), therefore, the annotated cell fraction can be directly compared between different immune cell subsets and platforms (17).…”

Section: Cibersort Estimationmentioning

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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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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Immune cell infiltration as a biomarker for the diagnosis and prognosis of stage I–III colon cancer

Cited by 233 publications

References 30 publications

Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer

Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer

A TP53 -associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma

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

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