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Mar 04, 2024 141 likes •242 views

Exploring the key genes and signaling transduction pathways related to the survival time of glioblastoma multiforme patients by a novel survival analysis model Department of Computer Science of Southwest University Le Zhang Scientific

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Exploring the key genes and signaling transduction pathways related to the survival time of glioblastoma multiforme patients by a novel survival analysis model

Department of Computer Science of Southwest University Le Zhang

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Scientific Significance

1. Exploring the key genes related to the survival time of GBM
2. Investigating the gene related signaling transduction pathways
3. Solving P>>N problem for survival model

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Review

The classical cox proportional hazards model[Cox et al., 1972] can only P<<N data [Crichton et al., 2002]. Tibshirani et al.,[Tibshirani et al., 1996] integrated the Lasso algorithm into the classical Cox proportional hazards model to select key genes Hong et al., [Hong et al., 2015] proposed a conditional SIS method to increase the screening performance. Developing a systematic approach to identify the target generic drug for the cancer treatment becomes a popular research field[Nelander et al., 2008].

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1. Analyzing

the relation between clinical GBM gene expression and survival time (The Georgetown Database of Cancer G-DOC

https://gdoc.georgetown.edu/gdoc/. )

2. Integrating

the Lasso into classical Cox model to process P>>N type of data and using the SIS algorithm to improve the predictive accuracy.

3. Employing

hypergeometric test to investigate the correlated GBM signaling transduction pathways regarding the explored survival time related key genes.

Innovotion

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Methods

> Implementation

> Combined Cox and Lasso (CoxLasso) strategy（a） > Combined Cox and SIS (CoxSis) strategy (b) > Combined Cox, SIS and Lasso (CoxSisLasso) strategy (c) start Pre-processed GBM data,denoted by Fig. 1.1 Cox+Lasso by Eq. 3 end the final selected predictors start Pre-processed GBM data,denoted by Fig. 1.1

btain the parameter estimate by a

marginal Cox regression model Rank the magnitudes of the parameter estimates, retain the top ranked covariates Implement Lasso with the selected covariates end the final selected predictors A B start Pre-processed GBM data,denoted by Fig. 1.1 Selected covariates from A by CoxLasso,denoted by C0 Selected covariates from A-C0 by Conditional SIS,denoted by C1 Selected covariates from C0 +C1 by CoxLasso end the final selected predictors C

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Results

> We use ROC and AUC to compare the performance of the three strategies

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Results

The key genes and signaling pathways by three strategies

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Conclusion

> Innovatively developing a CoxSisLasso strategy to interrogate the connections between GBM gene expression and GBM patients’ survival time > Employing the hypergeometric test to investigate the incoherent signaling transduction pathways and the survival time of GBM patient. > Lacking theoretically proof for the CoxSisLasso strategy, simulation study for the gene and pathway selection platform and so on.

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THANKS