Alterations in Immune-Related Genes as Potential Marker of Prognosis in Breast Cancer

Li, Bei; Geng, Rongxin; Wu, Qi; Yang, Qian; Sun, Si; Zhu, Shan; Xu, Zhiliang

doi:10.3389/fonc.2020.00333

Cited by 26 publications

(27 citation statements)

References 56 publications

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“…Firstly, we observed the statistically significant difference between two age groups with regard to patient outcomes in the IntCl1, possibly owing to immunerelated genes. Indeed, many previous studies were of interest to the interaction between the immune system and age factor in BRCA (Fuentes et al, 2017;Li et al, 2020;Xu et al, 2020). Especially, Li et al (2020) indicated that there was a positive correlation of aging with i) the lower median immune/stromal scores and ii) the lower OS of BRCA patients.…”

Section: Prognostic Factor Identificationmentioning

confidence: 99%

“…Indeed, many previous studies were of interest to the interaction between the immune system and age factor in BRCA (Fuentes et al, 2017;Li et al, 2020;Xu et al, 2020). Especially, Li et al (2020) indicated that there was a positive correlation of aging with i) the lower median immune/stromal scores and ii) the lower OS of BRCA patients. In contrast, an insignificant correlation of the IntCl2's patient survival with age factor was monitored.…”

Section: Prognostic Factor Identificationmentioning

confidence: 99%

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Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Nguyen¹,

Nguyen

et al. 2020

Front. Genet.

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The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed. In this study, we performed an integrative analysis of three-omics profiles, CNA, MET, and mRNA expression, in two BRCA patient cohorts (one for discovery and another for validation)-to elucidate those complicated relationships. To this purpose, we first established a set of CNAcor and METcor genes, which had CNA and MET levels significantly correlated (and anti-correlated) with their corresponding expression levels, respectively. Next, to revisit the current classification of BRCA, we performed single and integrated clustering analyses using our clustering method PINSPlus. We then discovered two biologically distinct subgroups that could be an improved and refined classification system for breast cancer patients, which can be validated by a third-party data. Further studies were then performed and realized each-subgroup-specific genes and different interactions between each of the two identified subgroups with the age factor. These findings can show promise as diagnostic and prognostic values in BRCA, and a potential alternative to the PAM50 intrinsic subtypes in the future.

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Section: Prognostic Factor Identificationmentioning

confidence: 99%

Section: Prognostic Factor Identificationmentioning

confidence: 99%

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Nguyen¹,

Nguyen

et al. 2020

Front. Genet.

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“…lncRNAs are receiving increasing attention because of their close association with tumorigenesis [15,16]. Moreover, the immune system plays a vital role in tumor development and progression [17,18]. Thus, immune-related lncRNAs may prove to be useful not only as potential prognostic biomarkers but could also provide novel therapeutic options.…”

Section: Discussionmentioning

confidence: 99%

Immune-Related LncRNAs as Predictors of Survival in Breast Cancer: A Prognostic Signature

Zhao

et al. 2020

Preprint

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Background: Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancers development and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Methods: We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separatedinto training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate and multivariate Cox regression analyses. Results: A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group( p= 1.215e−06 in the training set; p =0.0069 in the validation set; p =1.233e−07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR= 1.432; 95% CI 1.204−1.702, p <0.001), validation set (HR= 1.162; 95% CI 1.004−1.345, p = 0.044), and whole set (HR=1.240; 95% CI 1.128−1.362, p <0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. Conclusions: We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

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“…With the in-depth researches on lncRNAs and the immune system [21][22][23][24], scholars have realized that immune-related lncRNAs may prove to be useful not only as potential prognostic biomarkers but could also provide novel therapeutic options. Nevertheless, the lack of validation cohorts is a limitation for the proper evaluation of the prognostic merit of potential biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature

Zhao

et al. 2020

Preprint

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Background: Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer.Methods: We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses.Results:A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group(p=1.215e−06 in the training set; p=0.0069 in the validation set; p=1.233e−07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set,0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR= 1.432; 95% CI 1.204−1.702, p<0.001), validation set (HR= 1.162; 95% CI 1.004−1.345, p = 0.044), and whole set (HR=1.240; 95% CI 1.128−1.362, p<0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways.Conclusions:We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

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Alterations in Immune-Related Genes as Potential Marker of Prognosis in Breast Cancer

Cited by 26 publications

References 56 publications

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Immune-Related LncRNAs as Predictors of Survival in Breast Cancer: A Prognostic Signature

Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature

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