Identification of Immune-Related Prognostic Biomarkers Based on the Tumor Microenvironment in 20 Malignant Tumor Types With Poor Prognosis

Liu, Yu; Zhou, Hao; Zheng, Jing; Zeng, Xiaojun; Yu, Wei; Liu, Wei; Huang, Gan; Zhang, Yang; Fu, Weiling

doi:10.3389/fonc.2020.01008

Cited by 11 publications

(10 citation statements)

References 54 publications

(60 reference statements)

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“…Among them, low C16orf54 expression was considered to be a risk factor for SKCM, LUAD, CESC, and other tumours. Liu et al report that C16orf54 expression levels were associated with BRCA, LUAD, LGG, and SKCM prognosis, but it remains unclear whether low or high C16orf54 expression is a prognostic risk factor [24]. The above results show that C16orf54 is a potential marker of the poor prognosis of various tumours, further suggesting that C16orf54 participates in the progression of tumours.…”

Section: Discussionmentioning

confidence: 79%

Integrated Analysis of C16orf54 as a Potential Prognostic, Diagnostic, and Immune Marker across Pan-Cancer

Xia

Fan

et al. 2022

Disease Markers

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Chromosome 16 open reading frame 54 (C16orf54) is a protein coding gene, showing a biased expression in the bone marrow, lymph node, and 11 other tissues. Reports on the function of C16orf54 in the onset and development of tumours remain scarce. Clinical information and tumour expression profile data from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx) were utilized to determine the relationship between C16orf54 expression and prognosis, diagnosis, immune microenvironment, heterogeneity, and stemness across pan-cancer. The findings ascertained that C16orf54 was expressed at a low level in most cancers. Furthermore, C16orf54 could distinguish between cancer and normal tissues with high accuracy in most cancers, and the prognostic significance of low C16orf54 mRNA levels differs across cancers. C16orf54 expression was positively linked to the stromal, immune, and ESTIMATE scores. On the other hand, C16orf54 was reported to be negatively correlated with tumour purity in most cancers. Further, C16orf54 expression was positively correlated with immune cell infiltration and the expression of immune regulatory genes, including chemokines, receptors, major histocompatibility complexes, immune inhibitory, and immune stimulatory genes, in most cancers. Additionally, C16orf54 expression was significantly associated with tumour heterogeneity indicators, such as tumour mutation burden (TMB) and microsatellite instability (MSI), and was significantly correlated with DNAss and RNAss tumour stemness indicators. Moreover, Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis, as well as Gene Set Enrichment analysis (GSEA), revealed that C16orf54 expression was closely linked to the signalling pathways of immune cells and factors. The integrated analysis of C16orf54 indicates it as a potential prognostic, diagnostic, and immune marker, which could be adopted as a novel target for adjuvant immunotherapy across pan-cancer.

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Section: Discussionmentioning

confidence: 79%

Integrated Analysis of C16orf54 as a Potential Prognostic, Diagnostic, and Immune Marker across Pan-Cancer

Xia

Fan

et al. 2022

Disease Markers

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“…Thus, broad identification of general immune microenvironments may be more effective than relying on the identification of individual levels of biomarker genes when analyzing immune-related survival rates. 15 , 16 Thus, CIBERSORT, a popular TIL prediction method, was selected. Through the LM22 signature matrix, CIBERSORT provided the most diverse predictions of abundance levels across TIL subsets among the tumor microenvironment (TME) deconvolution tools that were currently available.…”

Section: Introductionmentioning

confidence: 99%

Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT

Kim

Kang

et al. 2021

OncoImmunology

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The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.

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“…Malignant tumor tissues include not only tumor cells, but also supportive tumor-associated healthy cells (stromal cells and immune cells), which comprise the tumor microenvironment (TME) ( 5 ). The TME has recently attracted increasing attention, as it provides a novel context for tumor diagnosis and prognosis ( 6 ). The TME also provides essential cues to maintain stemness and promote the seeding of tumor cells at sites of metastasis ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

“…The estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithm can be used to estimate and quantify the TME. Many studies ( 6 – 8 ) have shown that stromal score, immune score, and tumor purity measurements based on the TME can serve as prognostic tumor biomarkers. For HCC, immunohistochemical scoring of CD38 molecule in the TME can be used to predict responsiveness to anti-programmed cell death 1/CD274 molecule (i.e., anti-PD-1/PD-L1) immunotherapy ( 9 ).…”

Section: Introductionmentioning

confidence: 99%

Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

Liu

Rong

et al. 2021

Front. Immunol.

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Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC.Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score.Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts.Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.

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Identification of Immune-Related Prognostic Biomarkers Based on the Tumor Microenvironment in 20 Malignant Tumor Types With Poor Prognosis

Cited by 11 publications

References 54 publications

Integrated Analysis of C16orf54 as a Potential Prognostic, Diagnostic, and Immune Marker across Pan-Cancer

Integrated Analysis of C16orf54 as a Potential Prognostic, Diagnostic, and Immune Marker across Pan-Cancer

Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT

Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

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