Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer

Feng, Sheng; Xia, Tianyi; Yu, Ge; Zhang, Ke; Xuan, Jianhua; Luo, Shanhui; Shen, Yang

doi:10.3389/fimmu.2022.868067

Cited by 32 publications

(37 citation statements)

References 70 publications

(67 reference statements)

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“…Recently, with the development of science and technology, more and more techniques have been used to predict the prognosis of patients with tumors. Recent research has revealed that computed tomography-based radiogenomic biomarkers, 15 long non-coding RNAs (lncRNAs) 16 and single-sample gene set enrichment analysis algorithm 17 have been used to predict overall survival in tumors. Nomogram, as a classic model, is a commonly used model for predicting prognosis, especially in oncology.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

Zhang¹,

Feng²,

Yu³

et al. 2022

IJWH

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Mucinous ovarian cancer (MOC) is a rare histological type of EOC. In order to guide the clinical diagnosis and management of MOC patients, we constructed and verified a nomogram for the estimation of overall survival in patients with MOC. Patients and Methods: We collected 494 patients with MOC diagnosed from 2010 to 2015 in SEER database, and the following main inclusion criteria were used: (1) patients whose MOC was confirmed by pathology; (2) patients without a history of primary other cancer. Subsequently, we performed randomized grouping (6:4) and Cox hazard regression analysis in the training group. Subsequently, the nomogram was established. A variety of indicators were used to validate the prognosis value of nomogram, including the C-index, area under the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Moreover, Kaplan-Meier analysis was used to compare the survival results among different risk subgroups. Results: Cox hazard regression analysis revealed that age, grade, FIGO stage and log odds of positive lymph nodes stage were independent risk factors for patients with MOC. In the training group, the C-index of the nomogram was 0.827 (95% CI: 0.791-0.863) and the areas under the curve (AUC) predicting the 1-, 3-and 5-year survival rate were 0.853 (95% CI: 0.791-0.915), 0.886 (95% CI: 0.852-0.920) and 0.815 (95% CI: 0.766-0.864), respectively. The calibration curve revealed that the nomogram of the 1-, 3-and 5-year survival rate was consistent with the actual fact. Patients with high risk had a poorer prognosis than those with low risk (P < 0.001). DCA revealed that the nomogram had the best clinical value than other classical prognostic markers. Similarly, nomogram had excellent prognostic ability in the testing group. Conclusion:The nomogram was constructed to predict overall survival in patients with MOC, which had the significance for clinical evaluation.

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Section: Discussionmentioning

confidence: 99%

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

Zhang¹,

Feng²,

Yu³

et al. 2022

IJWH

Self Cite

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“…TCGA data resource was utilized to generate mutation along with CNV data. Furthermore, the maftools program was adopted to generate the mutation frequencies of 33 PRGs in PC subjects ( 42 ).…”

Section: Methodsmentioning

confidence: 99%

“…The data of the “clusterProfiler” tool were utilized to conduct gene ontology (GO) assessment along with the Kyoto Encyclopedia of Genes and Genomes (KEGG) assessment. Moreover, GSEA enrichment analysis was done in numerous risk groups differentiated by risk signature ( 42 ).…”

Section: Methodsmentioning

confidence: 99%

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Exploring the Potential of Pyroptosis-Related Genes in Predicting Prognosis and Immunological Characteristics of Pancreatic Cancer From the Perspective of Genome and Transcriptome

Zhang

You

Kang³

et al. 2022

Front. Oncol.

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ObjectiveTo probe into the role of pyroptosis-related genes in pancreatic carcinoma.MethodsHerein, we conducted a comprehensive bioinformatics analysis to evaluate tumor-immune infiltration and tumor mutation burden, the correlations between PRGs, and microsatellite instability and found that 33 PRGS were up- or down-regulated in PC. Then we built the PPI network, which was downloaded from the STRING database. Using TCGA cohort median risk score, PC subjects from the Gene Expression Composite cohort (GEO) data resource were stratified into two risk categories, with the low-PC risk group harboring a higher overall survival (OS) (P = 0.011). We employed the ssGSEA approach to quantify immune cell abundance in separate risk groups separated by risk signature while assessing variations in immune cell invasion. Chemotherapeutic drugs were retrieved from the Genomics of Drug Sensitivity in Cancer (GDSC) data resource.ResultsEight prognostic PRG models (CASP4, GSDMC, IL-18, NLRP1, NLRP2, PLCG1, TIRAP, and TNF) were established via LASSO Cox regression to estimate the OS of PC subjects with medium-to-high accuracy.ConclusionOur study is the first to identify a pyroptotic-related prognostic gene feature for PC, providing more options for the prognostic prediction of PC.

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“…Machine learning is a method for choosing the best model from a collection of alternatives that best fits a set of observations [ 17 ]. Relying on this approach, Feng et al performed machine learning model building, such as Random forest (RF), to identify the hypoxic landscape within ovarian cancer tissues in conjunction with radiogenomics data [ 18 ]. Although a large number of previous studies have reported the association of lncRNAs with the pathogenesis and prognosis of HNSCC, no unique hub biomarkers have been identified and compounded using machine learning based on public datasets.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of LINC01615 in Head and Neck Squamous Cell Carcinoma: A Hub Biomarker Identified by Machine Learning and Experimental Validation

Yin

Wang

Bian

et al. 2022

Journal of Oncology

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Background. Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers, but in clinical practice, the lack of precise biomarkers often results in an advanced diagnosis. Hence, it is crucial to explore novel biomarkers to improve the clinical outcome of HNSCC patients. Methods. We downloaded RNA-seq data consisting of 502 HNSCC tissues and 44 normal tissues from the TCGA database, and lncRNA genomic sequence information was downloaded from the GENECODE database for annotating lncRNA expression profiles. We used Cox regression analysis to screen prognostic lncRNAs, the threshold as HR >1 and p value <0.05. Subsequently, three survival outcomes (overall survival, progress-free interval, and disease-specific survival)-related lncRNAs overlapped to get the common lncRNAs. The hub biomarker was identified using LASSO and random forest models. Subsequently, we used a variety of statistical methods to validate the prognostic ability of the hub marker. In addition, Spearman correlation analysis between the hub marker expression and genomic heterogeneity was conducted, such as instability (MSI), homologous recombination deficiency (HRD), and tumor mutational burden (TMB). Finally, we used enrichment analysis, ssGSEA, and ESTIMATE algorithms to explore the changes in the underlying immune-related pathway and function. Finally, the MTT assay and transwell assay were performed to determine the effect of LINC01615 silencing on tumor cell proliferation, invasion, and migration. Results. Cox regression analysis revealed 133 lncRNAs with multiple prognostic significance. The machine learning algorithm screened out the hub lncRNA with the highest importance in the RF model: LINC01615. Clinical correlation analysis revealed that the LINC01615 increased with increasing the T stage, N stage, pathology grade, and clinical stage. LINC01615 could be used as a predictor of HNSCC prognosis validating by a variety of statistical methods. Subsequently, when clinical indicators were combined with the LINC01615 expression, the visualization model (nomogram) was more applicable to clinical practice. Finally, immune algorithms indicated that LINC01615 may be involved in the regulation of lymphocyte recruitment and immunological infiltration in HNSCC, and the LINC01615 expression represented genomic heterogeneity in pan-cancer. Functionally, silencing of LINC01615 suppresses cell proliferation, invasion, and migration in HEP-2 and TU212 cells. Conclusion. LINC01615 may play an important role in the prostromal cell enrichment and immunosuppressive state and serve as a prognostic biomarker in HNSCC.

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Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer

Cited by 32 publications

References 70 publications

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

Exploring the Potential of Pyroptosis-Related Genes in Predicting Prognosis and Immunological Characteristics of Pancreatic Cancer From the Perspective of Genome and Transcriptome

Comprehensive Analysis of LINC01615 in Head and Neck Squamous Cell Carcinoma: A Hub Biomarker Identified by Machine Learning and Experimental Validation

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