scholarly journals Identification of an Individualized Immune-Related Prognostic Risk Score in Lung Squamous Cell Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuan Zhuang ◽  
Sihan Li ◽  
Chang Liu ◽  
Guang Li
Keyword(s):  
Risk Score ◽  
Squamous Cell ◽  
Cell Cancer ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Background: Lung squamous cell carcinoma (LUSC) is one of the most common histological subtypes of non-small cell lung cancer (NSCLC), and its morbidity and mortality are steadily increasing. The purpose of this study was to study the relationship between the immune-related gene (IRGs) profile and the outcome of LUSC in patients by analyzing datasets from The Cancer Genome Atlas (TCGA).Methods: We obtained publicly available LUSC RNA expression data and clinical survival data from The Cancer Genome Atlas (TCGA), and filtered IRGs based on The ImmPort database. Then, we identified risk immune-related genes (r-IRGs) for model construction using Cox regression analysis and defined the risk score in this model as the immune gene risk index (IRI). Multivariate analysis was used to verify the independent prognostic value of IRI and its association with other clinicopathological features. Pearson correlation analysis was used to explore the molecular mechanism affecting the expression of IRGs and the correlation between IRI and immune cell infiltration.Results: We screened 15 r-IRGs for constructing the risk model. The median value of IRI stratified the patients and there were significant survival differences between the two groups (p = 4.271E-06). IRI was confirmed to be an independent prognostic factor (p < 0.001) and had a close correlation with the patients' age (p < 0.05). Interestingly, the infiltration of neutrophils or dendritic cells was strongly upregulated in the high-IRI groups (p < 0.05). Furthermore, by investigating differential transcription factors (TFs) and functional enrichment analysis, we explored potential mechanisms that may affect IRGs expression in tumor cells.Conclusion: In short, this study used 15 IRGs to build an effective risk prediction model, and demonstrated the significance of IRGs-based personalized immune scores in LUSC prognosis.

Eight-gene prognostic signature associated with hypoxia and ferroptosis for gastric cancer with general applicability

Epigenomics ◽  
2021 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aims: To investigate the prognostic significance of hypoxia- and ferroptosis-related genes for gastric cancer (GC). Materials & methods: We extracted data on 259 hypoxia- and ferroptosis-related genes from The Cancer Genome Atlas and identified the differentially expressed genes between normal (n = 32) and tumor (n = 375) tissues. A risk score was established by univariate Cox regression analysis and LASSO penalized Cox regression analysis. Results: The risk score contained eight genes showed good performance in predicting overall survival and relapse-free survival in GC patients in both the training cohort (The Cancer Genome Atlas, n = 350) and the testing cohorts (GSE84437, n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26253, n = 432). Conclusion: The eight-gene signature may help to the improve the prognostic risk classification of GC.

scholarly journals Development and validation of an immune gene set-based prognostic signature in cutaneous melanoma

2021 ◽  
Author(s):  
Qi Tian ◽  
Huan Gao ◽  
Wen Zhao ◽  
Yan Zhou ◽  
Jin Yang
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

We aimed to fully understand the landscape of the skin cutaneous melanoma (SKCM) microenvironment and develop an immune prognostic signature that can predict the prognosis for SKCM patients. RNA sequencing data and clinical information were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases. The immune-prognostic signature was constructed by LASSO Cox regression analysis. We calculated the relative abundance of 29 immune-related gene sets based on the mRNA expression profiles of 314 SKCM patients in the Cancer Genome Atlas training set. Hierarchical clustering was performed to classify SKCM patients into three clusters: immunity-high, -medium and -low. The values of our prognostic model in predicting disease progression, metastasis and immunotherapeutic responses were also validated. In conclusion, the prognostic model demonstrated a powerful ability to distinguish and predict SKCM patients’ prognosis.

scholarly journals Alternative Splicing Events as Indicators for the Prognosis of Uveal Melanoma

Genes ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 227 ◽  
Author(s):  
Qi Wan ◽  
Xuan Sang ◽  
Lin Jin ◽  
Zhichong Wang
Keyword(s):  
Alternative Splicing ◽  
Uveal Melanoma ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Correlation Network ◽  
Support Vector ◽  
Cancer Genome Atlas ◽  
Alternative Splicing Events ◽  
Genome Atlas

Growing evidence has revealed that abnormal alternative splicing (AS) events are closely related to carcinogenic processes. However, the comprehensive study on the prognostic value of splicing events involved in uveal melanoma (UM) is still lacking. Therefore, splicing data of 80 UM patients were obtained from the Cancer Genome Atlas (TCGA) SpliceSeq and RNA sequence data of UM and patient clinical features were downloaded from the Cancer Genome Atlas (TCGA) database to identify survival related splicing events in UM. As a result, a total of 37996 AS events of 17911 genes in UM were detected, among which 5299 AS events of 3529 genes were significantly associated with UM patients’ survival. Functional enrichment analysis revealed that this survival related splicing genes are corelated with mRNA catabolic process and ribosome pathway. Based on survival related splicing events, seven types of prognostic markers and the final overall prognostic signature could independently predict the overall survival of UM patients. Finally, an 11 spliced gene was identified in the final signature. On the basis of these 11 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. The results showed that the 11 genes could determine short- and long-term survival with a predicted accuracy of 97.5%. Besides, the splicing factors and alternative splicing events correlation network was constructed to serve as therapeutic targets for UM treatment. Thus, our study depicts a comprehensive landscape of alternative splicing events in the prognosis of UM. The correlation network and associated pathways would provide additional potential targets for therapy and prognosis.

scholarly journals Molecular Profiling of Core Immune-Escape Genes Highlights LCK Has Potential to Reshape TME in Melanoma

2021 ◽  
Author(s):  
Duoli Zhang ◽  
Zhuo Zhang ◽  
Shixin Xiang ◽  
Mintao Xiao ◽  
Yao Zhang ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Immune System ◽  
Tumor Microenvironment ◽  
Immune Escape ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Tumor Environment ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract Background: The game between the immune system of the organism and the tumor is a dynamic course of events. Once the escape phase is reached, the tumor will overcome the limitations of the immune system and the tumor will progress. Objective: This study aimed to determine the potential tumor environment-based prognostic biomarkers related to immunotherapy in melanoma. Methods: 471 tumor samples and 398 normal samples were extracted from The Cancer Genome Atlas and The Genotype-Tissue Expression. Furthermore, a core set of immune-escape genes were collected from previous studies and a set of immune-related genes were obtained from IMMPORT database. Through overlapping these two sets of genes, a set of core immune-escape related genes were identified. In the next place, we conducted a systematic analysis of core immune-escape related genes through The Cancer Genome Atlas cohort and identified independent prognostic factors in melanoma. Through CIBERSORT, ssGSEA algorithm and TIMER database, we explored the potential of independent prognostic factors to reshape the tumor microenvironment.Results: Through Kaplan-Meier as well as univariate cox regression analysis of expression profiles and clinical information obtained from the TCGA cohort, we found that high LCK expression was associated with prolonged overall survival. In addition, the expression level of LCK was significantly correlated with the dysregulation of the infiltration level of immune cells in the tumor microenvironment. Conclusions: In this study, we determined LCK as a biomarker that is significantly associated with tumor environment and has significant prognostic significance. Meanwhile, immunotherapeutic approaches targeting LCK in tumor cells may provide a new perspective for the treatment of melanoma.

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