A comprehensive prognostic signature based on interaction of immune cell infiltration with stromal composition for resected pancreatic ductal adenocarcinoma

2017 ◽  
Author(s):  
UM Mahajan ◽  
E Langhoff ◽  
E Costello ◽  
W Greenhalf ◽  
C Holloran ◽  
...  
Pancreatology ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. S34
Author(s):  
Ujjwal Mukund Mahajan ◽  
Enno Langhoff ◽  
Eithne Costello ◽  
William Greenhalf ◽  
Christopher Halloran ◽  
...  

2020 ◽  
Vol 11 (9) ◽  
pp. 2371-2381 ◽  
Author(s):  
Ronghua Zhang ◽  
Qiaofei Liu ◽  
Junya Peng ◽  
Mengyi Wang ◽  
Tong Li ◽  
...  

2021 ◽  
Vol 14 (8) ◽  
pp. 1151-1159
Author(s):  
Chen-Lu Liao ◽  
◽  
Xing-Yu Sun ◽  
Qi Zhou ◽  
Min Tian ◽  
...  

AIM: To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis. METHODS: UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package ‘edgeR’. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM. RESULTS: A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib. CONCLUSION: The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.


2021 ◽  
Author(s):  
Yi He ◽  
Haiyang Zhang ◽  
Yan Zhang ◽  
Peiyun Wang ◽  
Kegan Zhu ◽  
...  

Abstract Background: Stomach adenocarcinoma (STAD) is the common cancer and ranks third leading cause of cancer death worldwide. TGF‑β receptor 1 (TGFBR1), serving important roles in the TGF‑β family, the mechanisms whereby TGFβ2 governs tumor progression, immune cell infiltration and its correlation with tumor microenvironment (TME) in STAD remains unintelligible. Methods: First, we used the data in the TCGA, GEPIA, and HPA databases to explore the expression level of TGFBR1 in STAD, the correlation between TGFBR1 expression and the clinical features of STAD, its impact on the survival of STAD. Subsequently, a receiver operating characteristic (ROC) curve and nomogram were constructed and LASSO (the Least Absolute Shrinkage and Selection Operator)-selected features were used to build the TGFBR1 prognostic signature. Moreover, GSEA enrichment analysis is used to find the potential molecular mechanism of TGFBR1 to promote the malignant process of STAD. Finally, we further explored the influence of theTGFBR1 expression on the immune microenvironment of STAD patients through the TIMER2.0 and GEPIA database.Results: In our study, TGFBR1 expression was significantly elevated in patients with STAD and positively co-expression with pathologic stage, lymph node metastases (LNM) stage and histopathological grade of STAD. LASSO-selected features were used to build the TGFBR1 prognostic signature. 9 factors with non-zero coefficients were identified. The corresponding risk scores were computed, according to the following formula: Risk score = (-0.2914) *DIXDC1+ (0.1113) *STON1-GTF2A1L+(0.3092) *FERMT2+(-0.0146) *BHMT2+(0.1798) *ABCC9+(0.068) *MSRB3+(-0.1007) *SYNC+(-0.0891) *SORBS1+(0.0828) *TGFBR1.Survival analysis revealed that patients with high TGFBR1 had shorter OS, FP, and PPS. Multivariate Cox analysis revealed TGFBR1 was an independent prognostic factor for OS in STAD. The receiver operating characteristic (ROC) analysis suggested high diagnostic value with the area under curve (AUC) of TGFBR1 was 0.739, and a prognostic nomogram involving age, T, N, M classification, pathologic stage, primary therapy outcome, histologic grade and TGFBR1 to predict the 1, 3, 5-year OS was constructed. GSEA revealed that high TGFBR1 expression was correlated with pathway in cancer, MAPK signaling pathway, NOTCH signaling pathway, focal adhesion and VEGF-C production. ssGSEA showed that TGFBR1 is correlated with NK cells, Tem and Th17 cells. Furthermore, elevated TGFBR1 expression was found to be significantly correlated with several immune checkpoint and immune markers associated with immune cell subsets. Conclusion: In summary, TGFBR1 could be a prognostic biomarker and an important regulator of immune cell infiltration in STAD. The present study revealed the probable underlying molecular mechanisms of TGFBR1 in STAD and provided a potential target for improving the prognosis.


Author(s):  
Nian Liu ◽  
Zijian Liu ◽  
Xinxin Liu ◽  
Xiaoru Duan ◽  
Yuqiong Huang ◽  
...  

Abstract Background: Melanoma is the leading cause of cancer-related death among skin tumors, with an increasing incidence worldwide. Few studies have effectively investigated the significance of an immune-related genes (IRGs) signature for melanoma prognosis. Methods: Here, we constructed an IRGs prognostic signature using bioinformatics methods and evaluated and validated its predictive capability. Then, immune cell infiltration and tumor mutation burden (TMB) landscapes associated with this signature in melanoma were analyzed comprehensively. Results: With the 10-IRG prognostic signature, melanoma patients in the low-risk group showed better survival with distinct features of high immune cell infiltration and TMB. Importantly, melanoma patients in this subgroup were significantly responsive to MAGE-A3 in the validation cohort. Conclusions: This immune-related prognostic signature is thus a reliable tool to predict melanoma prognosis; as the underlying mechanism of this signature is associated with immune infiltration and mutation burden, it might reflect the benefit of immunotherapy to patients.


Author(s):  
Jinguo Zhang ◽  
Benjie Shan ◽  
Lin Lin ◽  
Jie Dong ◽  
Qingqing Sun ◽  
...  

Breast cancer (BC) represents a molecularly and clinically heterogeneous disease. Recent progress in immunotherapy has provided a glimmer of hope for several BC subtypes. The relationship between N6-methyladenosine (m6A) modification and long non-coding RNAs (LncRNAs) is still largely unexplored in BC. Here, with the intention to dissect the landscape of m6A-related lncRNAs and explore the immunotherapeutic value of the m6A-related lncRNA signature, we identified m6A-related lncRNAs by co-expression analysis from The Cancer Genome Atlas (TCGA) and stratified BC patients into different subgroups. Furthermore, we generated an m6A-related lncRNA prognostic signature. Four molecular subtypes were identified by consensus clustering. Cluster 3 preferentially had favorable prognosis, upregulated immune checkpoint expression, and high level of immune cell infiltration. Twenty-one m6A-related lncRNAs were applied to construct the m6A-related lncRNA model (m6A-LncRM). Survival analysis and receiver operating characteristic (ROC) curves further confirmed the prognostic value and prediction performance of m6A-LncRM. Finally, high- and low-risk BC subgroups displayed significantly different clinical features and immune cell infiltration status. Overall, our study systematically explored the prognostic value of the m6A-related LncRNAs and identified a high immunogenicity BC subtype. The proposed m6A-related LncRNA model might serve as a robust prognostic signature and attractive immunotherapeutic targets for BC treatment.


2021 ◽  
Vol 12 ◽  
Author(s):  
Yudong Cao ◽  
Hecheng Zhu ◽  
Jun Tan ◽  
Wen Yin ◽  
Quanwei Zhou ◽  
...  

IntroductionGlioma is the most common primary cancer of the central nervous system with dismal prognosis. Long noncoding RNAs (lncRNAs) have been discovered to play key roles in tumorigenesis in various cancers, including glioma. Because of the relevance between immune infiltrating and clinical outcome of glioma, identifying immune-related lncRNAs is urgent for better personalized management.Materials and methodsSingle-sample gene set enrichment analysis (ssGSEA) was applied to estimate immune infiltration, and glioma samples were divided into high immune cell infiltration group and low immune cell infiltration group. After screening differentially expressed lncRNAs in two immune groups, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune-related prognostic signature. Additionally, we explored the correlation between immune infiltration and the prognostic signature.ResultsA total of 653 samples were appropriate for further analyses, and 10 lncRNAs were identified as immune-related lncRNAs in glioma. After univariate Cox regression and LASSO Cox regression analysis, six lncRNAs were identified to construct a prognostic signature for glioma, which could be taken as independent prognostic factors in both univariate and multivariate Cox regression analyses. Moreover, risk score was significantly correlated with all the 29 immune-related checkpoint expression (p < 0.05) in ssGSEA except neutrophils (p = 0.43).ConclusionThe study constructed an immune-related prognostic signature for glioma, which contributed to improve clinical outcome prediction and guide immunotherapy.


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