<i>RTNsurvival</i>: an R/Bioconductor package for regulatory network survival analysis

Groeneveld, Clarice S.; Chagas, Vinicius S.; Jones, Steven J.M.; Robertson, A. Gordon; Ponder, Bruce A.J.; Meyer, Kerstin B.; Castro, Mauro A. A.

doi:10.1093/bioinformatics/btz229

Cited by 28 publications

(29 citation statements)

References 7 publications

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“…The regulon’s activity heatmap was also performed using RTN package. Ranked differential Enrichment Score (dES) plot for the BRCA cohort and status of key attributes plot were constructed using RTN Survival package [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Mathias

Groeneveld

Trefflich

et al. 2021

IJMS

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Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.

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Section: Methodsmentioning

confidence: 99%

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Mathias

Groeneveld

Trefflich

et al. 2021

IJMS

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“…Survival analysis of all the differentially expressed IRLs was obtained by using the survival package of R software (Groeneveld et al, 2019), and the survival-related IRLs were dependent on univariate Cox analysis (P < 0.05). To define the prognostic value of survival-related IRLs, a forest plot of these lncRNAs was created by using the hazard ratio (HR) as an indicator, relied on univariate Cox analysis.…”

Section: Screening Of Survival-related Irls and Evaluation Of The Prognostic Valuementioning

confidence: 99%

A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer

Qin

Wei

et al. 2021

Front. Genet.

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BackgroundColorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients.MethodsThe clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor.ResultsA total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration.ConclusionEight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.

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“…Lasso regression analysis was utilized to minimize over tting using the "glmnet" package [31] (P<0.05). Afterward, multivariate Cox regression was employed to develop the optimal prognostic risk model and leveraged "coxph" and "direction=both" functions of the R language "survival" package [32] (P<0.05). Then, the prognostic lncRNA signature's risk score constituting multiple lncRNAs was developed by summing up the product of each lncRNA with its corresponding coe cient.…”

Section: Development Veri Cation and Assessment Of Prognostic Biosimentioning

confidence: 99%

Identification of a nine ferroptosis-related lncRNA prognostic signature for lung adenocarcinoma

Tong¹,

Zhang

Yang

et al. 2021

Preprint

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Background: Recently, mounting of studies has shown that lncRNA affects tumor progression through the regulation of ferroptosis. The current study aims to construct a robust ferroptosis-related lncRNAs signature to increase the predicted value of lung adenocarcinoma (LUAD) by bioinformatics analysis. Methods: The transcriptome data were abstracted from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened by comparing 535 LAUD tissues with 59 adjacent non-LAUD tissues. Univariate Cox regression, lasso regression, multivariate Cox regression were conducted to design a ferroptosis-related lncRNA signature. This signature’s prognosis was verified by the log-rank test of Kaplan-Meier curve and the area under curve (AUC) of receiver operating characteristic (ROC) in train set, test set, and entire set. Furthermore, univariate and multivariate Cox regression were used to analyze its independent prognostic ability. The relationship of the ferroptosis-linked lncRNAs' expression and clinical variables was demonstrated by Wilcoxon rank-sum test and Kruskal-Wallis test. Gene set enrichment analysis (GSEA) was performed to signaling pathways it may involve.Results: 1224 differentially expressed lncRNAs were identified, of which 195 are ferroptosis-related lncRNAs. A nine ferroptosis-related lncRNAs (AC099850.3, NAALADL2-AS2, AL844908.1, AL365181.2, SMIM25, FAM83A-AS1, LINC01116, AL049836.1, C20orf197) prognostic signature was constructed. This model's prognosis in the high-risk group is obviously worse than that of the low-risk group in train set, test set, and entire set. The AUC of ROC predicting the three years survival in the train set, test set, and entire set was 0.754, 0.716, and 0.738, respectively. Moreover, the designed molecular signature was found to be an independent prognostic variable. The expression of these lncRNAs and the lncRNA signature are related to clinical stage, T stage, Lymph-node status, distant metastasis. Finally, GSEA analysis results show that the signature is involved in eight tumor-related and metabolism-related signaling pathwaysConclusion: The current study constructed, validated, and evaluated a nine ferroptosis-related lncRNA signature which can independently be used to predict the prognosis of LAUD patients, and may become a new therapeutic target.

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RTNsurvival: an R/Bioconductor package for regulatory network survival analysis

Cited by 28 publications

References 7 publications

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer

Identification of a nine ferroptosis-related lncRNA prognostic signature for lung adenocarcinoma

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