Constructing a Novel Signature Based on Immune-Related lncRNA to Improve Prognosis Prediction of Cervical Squamous Cell Carcinoma Patients

Lv, Xuefeng; Liu, Lu; Li, Pengxiang; Yuan, Yingying; Peng, Mengle; Jin, Huifang; Qin, Dongchun

doi:10.1007/s43032-022-00851-z

Cited by 8 publications

(6 citation statements)

References 44 publications

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“…Therefore, it is not surprising that some lncRNAs were selected in the univariate analysis because of their difference in expression between recurrent and non-recurrent samples. Furthermore, some of the lncRNAs present in our best model have been described previously in several cancers, ENSG00000274840, ENSG00000240137, and ENSG00000253622 [34][35][36], and ENSG00000250137 have been associated with increased BMI [37]. Our best model may be reflecting the underlying biological characteristics of the recurrent EEC phenotype.…”

Section: Discussionmentioning

confidence: 62%

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Bosquet

Gabrilovich

McDonald

et al. 2022

IJMS

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Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case–control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone.

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Section: Discussionmentioning

confidence: 62%

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Bosquet

Gabrilovich

McDonald

et al. 2022

IJMS

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“…In this study, we have found that USP30-AS1 influences the activation of macrophage-like cells. Interestingly, USP30-AS1 has been implicated to be a potential prognostic risk factor for cancer and to play a role in cell death (e.g., apoptosis, autophagy, ferroptosis, and necroptosis) [ 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 ]. Furthermore, USP30-AS1 promotes cell viability and inhibits apoptosis in acute myeloid leukemia cell lines, including THP-1 [ 100 ].…”

Section: Discussionmentioning

confidence: 99%

T2DB: A Web Database for Long Non-Coding RNA Genes in Type II Diabetes

Distefano

Ilieva

Madsen

et al. 2023

ncRNA

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Type II diabetes (T2D) is a growing health problem worldwide due to increased levels of obesity and can lead to other life-threatening diseases, such as cardiovascular and kidney diseases. As the number of individuals diagnosed with T2D rises, there is an urgent need to understand the pathogenesis of the disease in order to prevent further harm to the body caused by elevated blood glucose levels. Recent advances in long non-coding RNA (lncRNA) research may provide insights into the pathogenesis of T2D. Although lncRNAs can be readily detected in RNA sequencing (RNA-seq) data, most published datasets of T2D patients compared to healthy donors focus only on protein-coding genes, leaving lncRNAs to be undiscovered and understudied. To address this knowledge gap, we performed a secondary analysis of published RNA-seq data of T2D patients and of patients with related health complications to systematically analyze the expression changes of lncRNA genes in relation to the protein-coding genes. Since immune cells play important roles in T2D, we conducted loss-of-function experiments to provide functional data on the T2D-related lncRNA USP30-AS1, using an in vitro model of pro-inflammatory macrophage activation. To facilitate lncRNA research in T2D, we developed a web application, T2DB, to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in T2D patients compared to healthy donors or subjects without T2D.

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“…It has been reported that LINC00426 was positively correlated with the prognosis of HCC (30), which is consistent with our ndings. In addition, the reported results of bioinformatics analysis showed that LINC00426 was also signi cantly associated with the prognosis of renal clear cell carcinoma (ccRCC) and cervical squamous cell carcinoma (CSCC) (32,33). Therefore, LINC00426 may play a role in the occurrence and development of cancer, but its speci c function in cancer remains to be further con rmed.…”

Section: Discussionmentioning

confidence: 99%

Construction and analysis of a hepatocellular carcinoma prognostic model based on cuproptosis-related lncRNAs

Shi

Yang

Zhao

et al. 2023

Preprint

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Objective To screen cuproptosis-relatedlncRNAs and establish a prognostic model of cuproptosis-related lncRNAs in hepatocellular carcinoma (HCC). Methods Perl and R languages were used to collate the HCC transcriptome and clinical data downloaded from the TCGA database. Cuproptosis-related lncRNAs were obtained through co-expression analysis, and the total samples were randomly divided into the Train group and the Test group. Univariate Cox regression analysis, LASSO regression and multivariate Cox analysis were used to analyze the data of Train group to establish the prognostic model of cuproptosis-related lncRNAs. The accuracy of the model was verified by principal component analysis (PCA), ROC curve, and independent prognostic analysis. GO and KEGG enrichment analysis, tumor mutation burden correlation analysis, immunocorrelation analysis and drug sensitivity analysis were conducted in depth. Results Co-expression analysis showed 258 cuproptosis-related lncRNAs, univariate Cox regression analysis showed 31 lncRNAs were correlated with HCC prognosis, and multivariate Cox analysis showed 5 lncRNAs involved in model construction. They are LINC00426, AL590705.3, SPRY4-AS1, AL031985.3, and AC002091.2. Validation analysis showed that the survival of patients in the low-risk group was significantly better than that in the high-risk group, and the model could be used as an independent prognostic factor for HCC. Immunocorrelation analysis showed that patients in the high and low-risk groups significantly differed in immune-related functions and immune-related therapy, which was consistent with the GO and KEGG enrichment analysis results. Tumor mutation burden correlation and drug sensitivity analysis can further guide the diagnosis and treatment of HCC patients. Conclusion The HCC prognostic model constructed by 5 cuproptosis-related lncRNAs has high accuracy and has a certain reference value for clinical diagnosis and treatment of HCC patients.

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Constructing a Novel Signature Based on Immune-Related lncRNA to Improve Prognosis Prediction of Cervical Squamous Cell Carcinoma Patients

Cited by 8 publications

References 44 publications

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

T2DB: A Web Database for Long Non-Coding RNA Genes in Type II Diabetes

Construction and analysis of a hepatocellular carcinoma prognostic model based on cuproptosis-related lncRNAs

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