Prognosis prediction for glioblastoma multiforme patients using machine learning approaches: Development of the clinically applicable model

Kim, Yeseul; Kim, Kyung Hwan; Park, Jun-Young; Yoon, Hong In; Sung, Wonmo

doi:10.1016/j.radonc.2023.109617

Cited by 4 publications

(3 citation statements)

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“…A recent report indicated that MGMT gene promoter methylation status and extent of resection were important factors in affecting the patient outcome of prognostic model for PFS prediction [ 27 ]. By simultaneously considering the identified prognostic index (risk score), MGMT methylation status, and extent of resection, we performed a multivariate Cox regression analysis to evaluate the contribution and independence of these three features for PFS prediction in primary IDH-wt GBM patients.…”

Section: Resultsmentioning

confidence: 99%

“…Covariates encompass a wide range of technical and biological factors, such as data source, library preparation batch, age, gender, WHO grade, and isocitrate dehydrogenase (IDH) status, which often exert varying degrees of influence on gene expression and confound the interpretation of the observed differences between conditions [ 26 ]. Some covariates such as IDH status, age, and gender were demonstrated to considerably impact the GBM patient outcome in prognostic models for survival prediction [ 27 ]. Adjusting for survival-related covariates can improve the statistical power and minimize potential false positives arising from biological bias or technical artifacts [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

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A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels

Ho,

Chen,

Chiang

et al. 2024

acta neuropathol commun

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Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels

Ho,

Chen,

Chiang

et al. 2024

acta neuropathol commun

View full text Add to dashboard Cite

show abstract

“…The grid search was used to generate a list of all possible values of each parameter in the estimation function, after which the values in each list were combined to generate a grid, and each grid was used as training a model. After the fitting function had tried all combination results, it returned the most suitable learner and automatically adjusted to the best parameter combination ( Kim, et al., 2023 ).…”

Section: Methodsmentioning

confidence: 99%