1 A robust approach for identification of cancer 2 biomarkers and candidate drugs 3

Background: Identification of cancer biomarkers that are differentially expressed (DE) 15 under two biological conditions is an important task in many microarray studies. There exist 16 several methods in the literature in this regards and most of these methods designed especially for 17 unpaired samples, which does not satisfy the requirements of paired samples where the gene 18 expressions are taken from the same patients before and after treatment. Furthermore, the 19 traditional biomarker identification methods based on either p-values or fold change (FC) values. 20 However, sometimes, p-value based results do not comply with FC based results due to the 21 smaller variance of gene expressions. There are some methods that combine both p-values and FC 22 values to solve this problem. But, these methods also show weak performance for small-sample 23 case in presence of outlying expressions. To overcome this problem, in this paper an attempt is 24 made to develop a hybrid robust SAM-FC approach by combining rank of FC values and rank of 25 p-values based on SAM statistic using minimum β-divergence method, which is designed for 26 paired samples. This method introduces a weight function known as β-weight function. This 27 weight function produces larger weights corresponding to usual/normal expressions and smaller 28 weights for unusual/outlying expressions. The β-weight function plays the significant role on the 29 performance of the proposed method. Results: The proposed method uses β-weight function as a 30 measure of outlier detection by setting β=0.2. We unify both classical and robust estimates using β31 weight function such that maximum likelihood estimators (MLEs) are used in absence of outliers 32 and minimum β-divergence estimators are used in presence of outliers to obtain reasonable p33 values and FC values in the proposed method. We examined the performance of proposed 34 method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP and 35 FCROS) using both simulated and real gene expression profiles for both small-and large-sample 36 cases. From the simulation and a real spike in data analysis results we observed that the proposed 37 method outperforms other methods for small-sample case in presence of outliers and it keeps 38 almost equal performance with other robust methods (Wilcoxon, RP and FCROS) otherwise. From 39 a head-and-neck cancer (HNC) dataset the proposed method identified 2 genes (CYP3A4, 40 NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid 41 hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan-Meier curve 42 revealed that combined effect of these 2 genes has prognostic capability and they might be 43 promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene 44 interaction from glad4u and drug bank databases. 45 Conclusion The identified drugs showed statistical significance and critical role of the proteins 46 indicate that these proteins might be therapeutic target in cancer. Thus, elucidating the 47 associations between the drugs identified in the present study require further investigations. 48 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 December 2018 © 2018 by the author(s). Distributed under a Creative Commons CC BY license. doi:10.20944/preprints201812.0356. v1 Peer-reviewed version available at Medicina 2019, 55, 269; doi:10.3390/medicina55060269