Data mining on identifying diagnosis and prognosis biomarkers in head and neck squamous carcinoma

Ju, Guoyuan; Yao, Zhangyu; Zhao, Yanbin; Zhao, Xiaotong; Liu, Fangzhou

doi:10.1038/s41598-023-37216-8

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…In an earlier study, protein-protein interaction network-based analyses similarly identified ACTN2, MYL1, MYL2 , and MYH7 as hub genes associated with PTEN -associated prostate cancer ( Sun et al 2019 ). The dysregulation of ACTN2 and MYH7 has been described in the context of other cancers ( Ju et al 2023 ). Our results suggest that candidate genes in the identified module can be further examined in the context of prostate cancer.…”

Section: Resultsmentioning

confidence: 99%

Inference of differential gene regulatory networks using boosted differential trees

Galindez,

List,

Baumbach

et al. 2024

Bioinformatics Advances

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Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel non-parametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn’s disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses. BoostDiff is available at https://github.com/scibiome/boostdiff_inference.

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Section: Resultsmentioning

confidence: 99%

Inference of differential gene regulatory networks using boosted differential trees

Galindez,

List,

Baumbach

et al. 2024

Bioinformatics Advances

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show abstract

“…In contrast, the higher mortality of subtype C1 could indicate a more invasive disease trajectory and greater expression of genes from the MYH family, ACTB, ACTN2, and FLNC. Several studies have reported the prognostic value of the MYH family genes MYL1, MYL2, MYH1, MYH2, and MYH7, which are unfavorable prognostic markers in HNSCC and might promote CD4 + T-cell activation (Li et al 2023 ; Ju et al 2023 ). The level of phosphorylated MYH2-Y1381 is reportedly significantly lower in recurrent HNSCC patients than in primary patients (Kaneko et al 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Disulfidptosis features and prognosis in head and neck squamous cell carcinoma patients: unveiling and validating the prognostic signature across cohorts

Xue,

Sun,

Zhang

et al. 2024

J Cancer Res Clin Oncol

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Background Head and neck squamous cell carcinoma (HNSCC) is a significant health concern with a variable global incidence and is linked to regional lifestyle factors and HPV infections. Despite treatment advances, patient prognosis remains variable, necessitating an understanding of its molecular mechanisms and the identification of reliable prognostic biomarkers. Methods We analyzed 959 HNSCC samples and employed batch correction to obtain consistent transcriptomic data across cohorts. We examined 79 disulfidptosis-related genes to determine consensus clusters and utilized high-throughput sequencing to identify genetic heterogeneity within tumors. We established a disulfidptosis prognostic signature (DSPS) using least absolute shrinkage and selection operator (LASSO) regression and developed a prognostic nomogram integrating the DSPS with clinical factors. Personalized chemotherapy prediction was performed using the "pRRophetic" R package. Results Batch corrections were used to harmonize gene expression data, revealing two distinct disulfidptosis subtypes, C1 and C2, with differential gene expression and survival outcomes. Subtype C1, characterized by increased expression of the MYH family genes ACTB, ACTN2, and FLNC, had a mortality rate of 48.4%, while subtype C2 had a mortality rate of 38.7% (HR = 0.77, 95% CI: 0.633–0.934, P = 0.008). LASSO regression identified 15 genes that composed the DSPS prognostic model, which independently predicted survival (HR = 2.055, 95% CI: 1.420–2.975, P < 0.001). The prognostic nomogram, which included the DSPS, age, and tumor stage, predicted survival with AUC values of 0.686, 0.704, and 0.789 at 3, 5, and 8 years, respectively, indicating strong predictive capability. In the external validation cohort (cohort B), the DSPS successfully identified patients at greater risk, with worse overall survival outcomes in the high-DSPS subgroup (HR = 1.54, 95% CI: 1.17–2.023, P = 0.002) and AUC values of 0.601, 0.644, 0.636, and 0.748 at 3, 5, 8, and 10 years, respectively, confirming the model's robustness. Conclusion The DSPS provides a robust prognostic tool for HNSCC, underscoring the complexity of this disease and the potential for tailored treatment strategies. This study highlights the importance of molecular signatures in oncology, offering a step toward personalized medicine and improved patient outcomes in HNSCC management.

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“…[ 30 ] Additionally, lower expression of ACTN2 and MYH1 has been associated with longer OS time in patients with head and neck squamous carcinoma. [ 31 ]…”

Section: Discussionmentioning

confidence: 99%

Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model

Xiang,

Zhao,

Tang

et al. 2024

Medicine

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The dysregulation of protein-coding genes involved in various biological functions is closely associated with the progression of thyroid cancer. This study aimed to investigate the effects of dysregulated gene expressions on the prognosis of classical papillary thyroid carcinoma (cPTC). Using expression profiling datasets from the Cancer Genome Atlas (TCGA) database, we performed differential expression analysis to identify differentially expressed genes (DEGs). Cox regression and Kaplan–Meier analysis were used to identify DEGs, which were used to construct a risk model to predict the prognosis of cPTC patients. Functional enrichment analysis unveiled the potential significance of co-expressed protein-encoding genes in tumors. We identified 4 DEGs (SALL3, PPBP, MYH1, and SYNDIG1), which were used to construct a risk model to predict the prognosis of cPTC patients. These 4 genes were independent of clinical parameters and could be functional in cPTC carcinogenesis. Furthermore, PPBP exhibited a strong correlation with poorer overall survival (OS) in the advanced stage of the disease. This study suggests that the 4-gene signature could be an independent prognostic biomarker to improve prognosis prediction in cPTC patients older than 46.

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Data mining on identifying diagnosis and prognosis biomarkers in head and neck squamous carcinoma

Cited by 8 publications

References 33 publications

Inference of differential gene regulatory networks using boosted differential trees

Inference of differential gene regulatory networks using boosted differential trees

Disulfidptosis features and prognosis in head and neck squamous cell carcinoma patients: unveiling and validating the prognostic signature across cohorts

Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model

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