&lt;p&gt;Identification and Clinical Validation of 4-lncRNA Signature for Predicting Survival in Head and Neck Squamous Cell Carcinoma&lt;/p&gt;

Ji, Yanping; Xue, Yu

doi:10.2147/ott.s257200

Cited by 14 publications

(13 citation statements)

References 39 publications

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“…We next compared the predictive performances of our prognostic model and two lncRNAs signatures previously developed based on the same TCGA-HNSC cohort. Jiang et al (2021) , and Ji and Xue (2020) generated signatures based on three and four novel lncRNAs, respectively. As depicted in Figure 7B , the AUC for the 1-year survival rate of our genomic instability-associated lncRNA prognostic model was 0.656, which was significantly higher than Jiang’s LncSig (AUC = 0.639) and Ji’s LncSig (AUC = 0.572).…”

Section: Resultsmentioning

confidence: 99%

Identification and Validation of a Novel Genomic Instability-Associated Long Non-Coding RNA Prognostic Signature in Head and Neck Squamous Cell Carcinoma

Chen

Zhao

et al. 2022

Front. Cell Dev. Biol.

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Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most aggressive malignant cancers worldwide, and accurate prognostic models are urgently needed. Emerging evidence revealed that long non-coding RNAs (lncRNAs) are related to genomic instability. We sought to identify and validate a genomic instability-associated lncRNA prognostic signature to assess HNSCC patient survival outcomes.Methods: RNA-sequencing data, somatic mutation files, and patient clinical data were downloaded from The Cancer Genome Atlas database. A total of 491 patients with completely clinical files were randomly divided into training and testing sets. In the training set, genomic instability-associated lncRNAs were screened through univariate Cox regression analyses and least absolute shrinkage and selection operator regression analyses to build a genomic instability-associated lncRNA signature (GILncSig). In addition, time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier survival curve, and clinical stratification analyses were used to evaluate the signature’s reliability. Finally, in situ hybridization experiments were performed to validate GILncSig expression levels between adjacent non-tumor tissues and tumor tissues from HNSCC patients.Results: Four genomic instability-associated lncRNAs (AC023310.4, AC091729.1, LINC01564, and MIR3142HG) were selected for the prognostic signature. The model was successfully validated using the testing cohort. ROC analysis demonstrated its strong predictive ability for HNSCC prognosis. Univariate and multivariate Cox analyses revealed that the GILncSig was an independent predictor of prognosis. HNSCC patients with a low-risk score showed a substantially better prognosis than the high-risk groups. The in situ hybridization experiments using human HNSCC tissue revealed high GILncSig expression in HNSCC tissues compared with adjacent non-tumor tissues.Conclusion: We developed a novel GILncSig for prognosis prediction in HNSCC patients, and the components of that signature might be therapeutic targets for HNSCC.

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Section: Resultsmentioning

confidence: 99%

Identification and Validation of a Novel Genomic Instability-Associated Long Non-Coding RNA Prognostic Signature in Head and Neck Squamous Cell Carcinoma

Chen

Zhao

et al. 2022

Front. Cell Dev. Biol.

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“…The predictive performance of the machine learning models was determined by the receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated. The “RMS” package was used to create the nomogram ( 19 ). All statistical analyses of this study were performed using R 3.5.1 and Python 3.5.6.…”

Section: Methodsmentioning

confidence: 99%

“…Specific gene mutation information is confirmed by performing genetic testing on tumor tissue samples obtained by surgical resection or biopsy by an experienced physician. The mutation sites of four exons (exon [18][19][20][21] in the coding region of the EGFR gene were detected by real-time PCR. If any exon mutation was identified, the tumor was classified as EGFR-MT, otherwise considered as EGFR-WT.…”

Section: Egfr Mutation Detectionmentioning

confidence: 99%

PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs

Yang

et al. 2022

Front. Oncol.

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BackgroundsEpidermal growth factor receptor (EGFR) mutation profiles play a vital role in treatment strategy decisions for non–small cell lung cancer (NSCLC). The purpose of this study was to evaluate the predictive efficacy of baseline 18F-FDG PET/CT-based radiomics analysis for EGFR mutation status, mutation site, and the survival benefit of targeted therapy.MethodsA sum of 313 NSCLC patients with pre-treatment 18F-FDG PET/CT scans and genetic mutations detection were retrospectively studied. Clinical and PET metabolic parameters were incorporated into independent predictors of determining mutation status and mutation site. The dataset was randomly allocated into the training and the validation sets in a 7:3 ratio. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with EGFR mutation profiles is built by feature selection. Three different prediction models based on support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were established. Furthermore, nomograms for estimation of overall survival (OS) and progression-free survival (PFS) were established by integrating PET/CT radiomics score (Rad-score), metabolic parameters, and clinical factors. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis and the calibration curve analysis. The decision curve analysis (DCA) was applied to estimate and compare the clinical usefulness of nomograms.ResultsThree hundred thirteen NSCLC patients were classified into a training set (n=218) and a validation set (n=95). Multivariate analysis demonstrated that SUVmax and sex were independent indicators of EGFR mutation status and mutation site. Eight CT-derived RS, six PET-derived RS, and two clinical factors were retained to develop integrated models, which exhibited excellent ability to distinguish between EGFR wild type (EGFR-WT), EGFR 19 mutation type (EGFR-19-MT), and EGFR 21 mutation type (EGFR-21-MT). The SVM model outperformed the RF model and the DT model, yielding training area under the curves (AUC) of EGFR-WT, EGFR-19-WT, and EGFR-21-WT, with 0.881, 0.851, and 0.849, respectively, and validation AUCs of 0.926, 0.805 and 0.859, respectively. For prediction of OS, the integrated nomogram is superior to the clinical nomogram and the radiomics nomogram, with C-indexes of 0.80 in the training set and 0.83 in the validation set, respectively.ConclusionsThe PET/CT-based radiomics analysis might provide a novel approach to predict EGFR mutation status and mutation site in NSCLC patients and could serve as useful predictors for the patients’ survival outcome of targeted therapy in clinical practice.

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“…Based on the in-depth advance of high-throughput sequencing technologies, an emerging number of long non-coding RNAs (lncRNAs) has been identified over recent decades (1)(2)(3)(4)(5). LncRNA is a novel type of non-coding RNA molecules with over 200 bp (6,7), accounting for the largest proportion of non-coding RNAs (ncRNA) (8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

“…LncRNA forkhead box D3 antisense 1 (FOXD3-AS1), an antisense transcript of the proteincoding gene FOXD3, is a recently discovered lncRNA located in chromosome 1p31. 3. Growing evidence reports that FOXD3-AS1 is abnormally expressed in many disease types and its expression seems to be closely associated with significant clinical features.…”

Section: Introductionmentioning

confidence: 99%

Emerging Roles and Mechanisms of lncRNA FOXD3-AS1 in Human Diseases

2022

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Numerous long noncoding RNAs (lncRNAs) have been identified as powerful regulators of human diseases. The lncRNA FOXD3-AS1 is a novel lncRNA that was recently shown to exert imperative roles in the initialization and progression of several diseases. Emerging studies have shown aberrant expression of FOXD3-AS1 and close correlation with pathophysiological traits of numerous diseases, particularly cancers. More importantly, FOXD3-AS1 was also found to ubiquitously impact a range of biological functions. This study aims to summarize the expression, associated clinicopathological features, major functions and molecular mechanisms of FOXD3-AS1 in human diseases and to explore its possible clinical applications.

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<p>Identification and Clinical Validation of 4-lncRNA Signature for Predicting Survival in Head and Neck Squamous Cell Carcinoma</p>

Cited by 14 publications

References 39 publications

Identification and Validation of a Novel Genomic Instability-Associated Long Non-Coding RNA Prognostic Signature in Head and Neck Squamous Cell Carcinoma

Identification and Validation of a Novel Genomic Instability-Associated Long Non-Coding RNA Prognostic Signature in Head and Neck Squamous Cell Carcinoma

PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs

Emerging Roles and Mechanisms of lncRNA FOXD3-AS1 in Human Diseases

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