Machine learning discoveries of AURKB-X synergy in ETC-1922159 treated colorectal cancer cells

Machine learning discoveries of AURKB-X synergy in ETC-1922159 treated colorectal cancer cells shriprakash sinha Independent Researcher; Orcid ID : orcid.org/0000-0001-7027-5788 104-Madhurisha Heights Phase 1, Risali, Bhilai-490006, India Abstract Aurora kinase B (AURKB) is one of the components that make up a complex called the chromosome passenger complex (CPC). The major functions of AURKB are to regulate kinetochore-microtubule attachments as well as cytokinesis. In colorectal cancer (CRC) cells treated with ETC-1922159, AURKB was found to be down regulated along with other genes. A recently developed search engine ranked combinations of AURKB-X (X, a particular gene/protein) at 2nd order level after drug administration. Some of these combinations have been tested in wet lab, however many have been pointed out by the search engine that are yet to be explored/tested. These rankings reveal which AURKB-X combinations might be working synergistically in CRC. In this research work, I cover combinations of AURKB with ZW10 interacting kinetochore protein (ZWINT/ZWINT-1), inner centromere protein (INCENP), targeting protein for xenopus kinesin-like protein 2 (TPX2), WNT pathway components, chromatin licensing and DNA replication factor 1 (CDT1), G2 and S-phase expressed 1 (GTSE1), vaccinia related kinase 1 (VRK1), RecQ like helicase 4 (RECQL4), histone H3 associated protein kinase (HASPIN/GSG2), centromere protein (CENP), E2F transcription factor (E2F), cell division cycle (CDC), ubiquitin specific peptidase (USP), Nucleoporin (NUP), gem nuclear organelle associated protein (GEMIN), mitotic arrest deficient 2 like (MAD2L), homeobox (HOX), DExH-box helicase (DHX), polo like kinase (PLK), budding uninhibited by benzimidazoles (BUB), DNA topoisomerase (TOP), cyclin dependent kinase (CDK), alkB homolog lysine demethylase (ALKBH), protein arginine methyltransferase (PRMT), shugoshin (SGO), spindle and kinetochore associated complex subunit (SKA), growth arrest specific (GAS) and kinesin family member (KIF) family. Keywords: AURKB, Porcupine inhibitor ETC-1922159, Sensitivity analysis, Colorectal cancer. IML dicoveries of AURKB-X synergy in ETC-1922159 treated CRC cells Email address: [email protected] (shriprakash sinha) 1Aspects of unpublished work were presented in a poster session at the first Wnt Gordon Conference, from 6-11 August 2017, held in Stowe, VT 05672, USA. Preprint submitted to Preprint April 28, 2025 Contents 1 Significance 3 2 Introduction 3 2.1 Combinatorial search problem and a possible solution . . . . . . . . . 3 2.2 Insight behind the work . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 PORCN-WNT inhibitors . . . . . . . . . . . . . . . . . . . . . . . . 4 2.4 Aurorakinase.............................. 4 3 Tools of study 5 4 Static data by Madan et al. [1] 5 5 Methodology 5 5.1 Revealing higher order biological hypotheses via sensitivity analysis and insilico ranking algorithm . . . . . . . . . . . . . . . . . . . . . 5 5.2 Design for static data from Madan et al. [1] . . . . . . . . . . . . . . 6 6 Results & Discussion 7 6.1 How to interpret the ranking? . . . . . . . . . . . . . . . . . . . . . . 7 6.2 AURKB related synergies . . . . . . . . . . . . . . . . . . . . . . . . 8 6.2.1 AURKB - ZWINT / INCENP / TPX2 / WNT pathway components / CDT1 / GTSE1 / VRK1 / RECQL4 / GSG2 . . . . . . 8 6.2.2 AURKB-CENP ........................ 9 6.2.3 AURKB-E2F ......................... 10 6.2.4 AURKB-CDC......................... 12 6.2.5 AURKB-USP ......................... 14 6.2.6 AURKB - RANBP/NUP . . . . . . . . . . . . . . . . . . . . 14 6.2.7 AURKB-GEMIN ....................... 15 6.2.8 AURKB-MAD2L....................... 17 6.2.9 AURKB-HOX......................... 18 6.2.10 AURKB-DHX......................... 19 6.2.11 AURKB-PLK......................... 21 6.2.12 AURKB-BUB......................... 21 6.2.13 AURKB-TOP......................... 22 6.2.14 AURKB-CDK......................... 23 6.2.15 AURKB-ALKBH....................... 24 6.2.16 AURKB-PRMT........................ 25 6.2.17 AURKB-SGO......................... 26 6.2.18 AURKB-SKA......................... 27 6.2.19 AURKB-GAS......................... 28 6.2.20 AURKB-KIF ......................... 29 7 Conclusion 30 8 Code Availability 31 2 9 Source of Data 31 10 References 31 1. Significance A search engine was used to reveal and prioritise gene combinations, by adapting the code from a recently published work. Rankings of combinations were observed to be conserved across the different sensitivity methods (used to estimate the influence of components of a combination). This points to possible existence of synergy between genes at biological level. Presented here are rankings of experimentally established combinations of AURKB-X for CRC treated with ETC-1922159 and rankings that are unexplored. These point to efficacy and potential of the search engine. The engine is effective for ranking combinations of any gene of choice. 2. Introduction 2.1. Combinatorial search problem and a possible solution A recent design of a machine learning based search engine was published Sinha [2], that ranks combinations of genes that might be working synergistically in cells in various processes. To demostrate its efficacy in real life scenario, the data set containing recordings of up/down regulated genes generated from colorectal cancer (CRC) cells treated with PROCN-WNT inhibitor drug ETC-1922159 was taken Madan et al. [1]. The regulation of the genes were recorded individually, but in many cases, it is still not known which higher (≥2) order gene combinations might be playing a greater role in CRC. Here, I demonstrate that the rankings assigned to gene combinations at 2nd order, by the search engine are conserved across the different sensitivity methods (and kernels/variants). This conservation points to the possible existance of the synergy between genes at the biological level. Readers are requested to go through the adaptation of the above mentioned work for gaining deeper insight into the working of the pipeline and its use of published data set generated after administration of ETC-1922159, Sinha [3]. 2.2. Insight behind the work Across all search engines has the fundamental principle remains the same i.e to capture the pattern available in the data and based on that pattern, rank a list of queries. Different algorithms can be applied, however if the fundamental pattern is captured accurately, then the rankings will remain approximately the same, with slight variations, across the different kinds of search engines used. I use one search engine, however, vary the way the patterns are captured via use of different sensitivity methods. Each sensitivity method uses a different flavour/mathematical formulation to compute the sensitivity indices to estimate the influence of the involved factors. These involved factors are genes that play a role in cell biology, in the above research. The insight 3 is that all methods will capture the sensitivity of the involved factors based on their recorded regulations and the search engine will rank the combination of factors based on these sensitivity indices. Since the role of involved factors are captured properly, the search engine will give appropriate rankings to the combinations, thus capturing which gene combinations might be playing significantly in a biological phenomena. The above work shows rankings for experimentally confirmed combinations as well as unexplored/untested combinations. These rankings are not just numbers. They point to the existence of biological synergy in the form of gene combinations, whether tested in wet lab or unexplored till now. Finally, the findings suggest that the rankings are conserved across the different sensitivity methods used. 2.3. PORCN-WNT inhibitors The regulation of the Wnt pathway is dependent on the production and secretion of the WNT proteins. Thus, the inhibition of a causal factor like PORCN which contributes to the WNT secretion has been proposed to be a way to interfere with the Wnt cascade, which might result in the growth of tumor. Several groups have been engaged in such studies and known PORCN-WNT inhibitors that have been made available till now are IWP-L6 Chen et al. [4] & Wang et al. [5], C59 Proffitt et al. [6], LGK974 Liu et al. [7] and ETC-1922159 Duraiswamy et al. [8]. In this study, the focus of the attention is on the implications of the ETC-1922159, after the drug has been administered. The drug is a enantiomer with a nanomolar activity and excellent bioavailability as claimed in Duraiswamy et al. [8]. 2.4. Aurora kinase Aurora kinases (AURKs) regulate chromosome-microtubule attachments, centrosome duplication and separation, chromosome condensation, bipolar spindle assembly, the spindle checkpoint and cytokinesis (Zhang [9]). Drosophila aurora was first identified by Glover et al. [10] where mutations in aurora prevented centrosome separation. Comprehensive review on AURKs can be found in Carmena and Earnshaw [11] and Willems et al. [12]. Bischoff et al. [13] identified human homologue of drosophila aurora kinase which was amplified in human colorectal cancers. Further, Ota et al. [14] found increased phosphorylation of histone H3 for maintenance of proper chromosome dynamics during mitosis, due to AIM-1/AURKB overexpression, thus leading to chromosome instability. They also observe that there was increased expression of the AIM-1 gene in human colorectal tumors. Recently, the findings of overexpression of AURKB in colorectal cancer has been confirmed in Shah et al. [15] and Li et al. [16]. In this research, I focus on Aurora kinase B (AURKB). AURKB works in tandem with multiple components and some combinations of AURKB have been confrimed in wet lab. However, many of the combinations have not been explored/tested or are known. To reveal these combinations, I use a modification of a recently published machine learning based search engine, details of which are given in the next section. 4 3. Tools of study In a recently published work by Sinha [2], a frame work of a search engine was developed which can rank combinations of factors (genes/proteins) in a signaling pathway. Readers are requested to go through the adaptation of the above mentioned work for gaining deeper insight into the working of the pipeline and its use of published data set generated after administration of ETC-1922159, Sinha [3]. Further, sensitivity analysis and its relevance in systems biology have been covered in a recently published article Sinha [17], which forms the foundation for this work. In this work, the sensitivity indices are computed for all factors or combination of factors affecting the pathway. The sensitivity package by Pujol et al. [18] was used to develop the search engine pipeline. Ranking using support vector machines (SVM) are then employed using these sensitivity indices. The work uses SVM package by Joachims [19] in https://www.cs.cornell.edu/people/tj/svm_light/svm_rank.html. I use the adaptation of the above engine to rank 2nd order gene combinations. 4. Static data by Madan et al. [1] Data used in this research work was released in a publication by Madan et al. [1]. The ETC-1922159 was released in Singapore in July 2015 under the flagship of the Agency for Science, Technology and Research (A*STAR) and Duke-National University of Singapore Graduate Medical School (Duke-NUS). Note that the ETC-1922159 data show numerical point measurements that is as Madan et al. [1] quote - ”List of differentially expressed genes identified at three days after the start of ETC-159 treatment of colorectal tumors. Log2 fold-changes between untreated (vehicle, VEH) and ETC-159 treated (ETC) tumors are reported.” The numerical point measurements of differentially expressed genes were recorded using the following formulation of fold changes in equation 1 (see Tusher et al. [20], Choe et al. [21] and Witten and Tibshirani [22]). log2 VEHavg ETCavg (1) 5. Methodology 5.1. Revealing higher order biological hypotheses via sensitivity analysis and insilico ranking algorithm In the trial experiments on ETC-1922159 Madan et al. [1], a list of genes (2500±) have been reported to be up and down regulated after the drug treatment and a time buffer of 3 days. Some of the transcript levels of these genes have been recorded and the experimental design is explained elaborately in the same manuscript. In the list are also available unknown or uncharacterised proteins that have been recorded after the drug was administered. These have been marked as ”- -” in the list (Note - In this manuscript these uncharacterised proteins have been marked as ”XXM”, were 5 M=1,2,3, ...). The aim of this work is to reveal unknown/unexplored/untested biological hypotheses that form higher order combinations. For example, it is known that the combinations of WNT-FZD or RSPO-LGR-RNF play significant roles in the Wnt pathway. But the n≥2,3, ...-order combinations out of N(>n)genes forms a vast combinatorial search forest that is extremely tough to investigate due to the humongous amount of combinations. Currently, a major problem in biology is to cherry pick the combinations based on expert advice, literature survey or random choices to investigate a particular combinatorial hypothesis. The current work aims to reveal these unknown/unexplored/untested combinations by prioritising these combinations using a potent support vector ranking algorithm Joachims [19]. This cuts down the cost in time/energy/investment for any investigation concerning a biological hypothesis in a vast search space. The pipleline works by computing sensitivity indicies for each of these combinations and then vectorising these indices to connote and form discriminative feature vector for each combination. The ranking algorithm is then applied to a set of combinations/sensitivity index vectors and a ranking score is generated. Sorting these scores leads to prioritization of the combinations. Note that these combinations are now ranked and give the biologists a chance to narrow down their focus on crucial biological hypotheses in the form of combinations which the biologists might want to test. Analogous to the webpage search engine, where the click of a button for a few key-words leads to a ranked list of web links, the pipeline uses sensitivity indices as an indicator of the strength of the influence of factors or their combinations, as a criteria to rank the combinations. 5.2. Design for static data from Madan et al. [1] The procedure begins with the listing of all Cn kcombinations for knumber of genes from a total of ngenes. Here ncan be the choice of the biologist. kis ≥2 and ≤(n− 1). Each of the combination of order krepresent a unique set of interaction between the involved genetic factors. Note that the ETC-1922159 data show numerical point measurements that is as Madan et al. [1] quote ”List of differentially expressed genes identified at three days after the start of ETC-159 treatment of colorectal tumors. Log2 fold-changes between untreated (vehicle, VEH) and ETC-159 treated (ETC) tumors are reported.” Since the sensitivity analysis methods require a sample for a particular observation, a steep gaussian distribution was generated with a jitter (noise) added to the deviation from the reported point measurement of 0.005. In this experiment, the distribution contained 10 measurements (including the point of measurement under consideration). This is repeated for each point of measurement. To have an averaged ranking, the experiment was designed to run for 50 iterations. In each iteration, the datasets are combined in a specifed format which go as input as per the requirement of a particular sensitivity analysis method. Thus for each pth combination in Cn kcombinations, the dataset is prepared in a required format (See .R code in mainscript-2-2.R). Details of formatting the data have not been presented in the article to maintain the fluidity and brevity. Interested readers can find examples of formating the data in the sensitivity analysis package in R. After the data has been transformed, vectorized programming is employed for density based sensitivity analysis and looping 6 is employed for variance based sensitivity analysis to compute the required sensitivity indices for each of the pcombinations. After the above sensitivity indices have been stored for each of the pth combination, for a chosen sensitivity analysis method, the next step in the design of experiment is conducted. Here, the indices are averaged per combination to have a mean index value. These index values form the discriminative features for a particular combination. For a kth order combination, a vector of kelements or indices forms a feature vector. Thus for Cn kcombinations there will be Cn kvectors, each containing kelements. Next, SVMRank learn Joachims [19] is used to generate a model on default value Cvalue of 20. In the current experiment on toy model Cvalue has not been tunned. The training set helps in the generation of the model as the different gene combinations are numbered in order which are used as rank indices. The model is then used to generate score on the observations in the testing set using the SVMRank classi f y Joachims [19]. This is followed by sorting of these scores along with the rank indices already assigned to the gene combinations. The end result is a sorted order of the gene combinations based on the ranking score learned by the SVMRank algorithm. Note that the following is the order in which the files should be executed in R, in order, for obtaining the desired results (Note that the code will not be explained here) - • use source(”extractETCdata.R”) •use source(”mainScript-2-2.R”) •use source(”SVMRankResults-S-mean.R”). 6. Results & Discussion 6.1. How to interpret the ranking? In each of the sections below, one will find two tables. The first table lists the rankings of a particular gene combination based on these kernels. Based on majority voting, a combination is decided to be low ranked or high ranked. So, for example, if majority rankings point to low numerical value (i.e below the half way mark of approximately 2500 gene combinations), then the combination is possibly not highly ranked in colorectal cancer cells AFTER the ETC-1922159 treatment. Looking at it in another way, this low ranking suggests that the combination might have been up regulated in colorectal cancer cells BEFORE the ETC-1922159 treatment. This points to the inference that the combination of the two genes/proteins was working synergistically in colorectal cancer, while being up regulated and before ETC-1922159 treatment. The ETC-1922159 administration had caused a down regulation of genes in colorectal cancer cells and what is available as data by Madan et al. [1] points to down regulated recordings of genes/proteins taken individually. The second table uses the majority voting mentioned above, to filter out which combinations need to be further tests in the wet lab. These combinations recorded in the second table are inferences/pointers to existence of possible synergistic combinations that might be working in the cell, in a particular scenario (here colorectal cancer cells). Additionally, one will see two inferences - •based on the experimentally tested and established synergies in any other pathological/normal cell, if recorded in colorectal cancer cells treated with ETC-1922159, these combinations will ranked by the engine 7 appropriately (or note - there might be a possibility that the experimentally tested combination established in a different scenario, might not get an approprate rank by the search engine in the colorectal cancer cells treated with ETC-1922159.) and •based on the cues from previous point, there will be combinations ranked by the engine, that point to new synergies that have not been explored/tested in wet lab. Further, in a list of approximately 2500 genes that were up/down regulated after ETC-1922159 treatment, for the second order combinations there will be 2499 combinations. The engine generates the ranking for all these 2499 combinations. However, it is not possible to report the ranking of all 2499 combinations in a single article, for a particular gene under investigation. The full set of rankings reveal a prioritized list of new combinations that emerge as plausible biological hypotheses that might be working synergistically in colorectal cancer cells. These require further tests. For transperancy and reproducibility, one can download the code of search engine in R language and run it on the data made available by Madan et al. [1], to get a full list of some 2499, 2nd order combinations for a particular gene of choice. Higher order combinations can also be generated using this engine. Finally, we also see how the rankings behave across the different sensitivity methods and how they are conserved across the same. This conservation points to existence of biological synergy between the components of a combination (i.e either experimentally established or is unexplored/untested). 6.2. AURKB related synergies 6.2.1. AURKB - ZWINT / INCENP / TPX2 / WNT pathway components / CDT1 / GTSE1 / VRK1 / RECQL4 / GSG2 Kasuboski et al. [23] identified Zwint-1 (ZWITN) as a novel Aurora B substrate required for regulation of the spindle assembly checkpoint. Abdul Azeez et al. [24] studied the structural mechanism of AURKB activation that binds to the C-terminal domain of INCENP (full activation of which requires phosphorylation of two serine residues of INCENP). TPX2 is a co-activator protein of AURKB which forms the core of the chromosomal passenger complex, as shown by Iyer and Tsai [25]. Luo et al. [26] demonstrate an atypical function of a centrosomal module in WNT signalling as WNT-PCP protein DVL2 (as studied in Luga et al. [27]) associated with CEP192PLK4/AURKB complex. Agarwal et al. [28] provides mechanistic insight into how CDT1 stabilizes kinetochoremicrotubule attachments via an AURKB phosphorylation. GTSE1 regulates AURKB activity via spindle microtubule dynamics, as shown by Tipton et al. [29]. Moura et al. [30] propose formation of a complex between VRK1 and AURKB in the phosphorylation of Histone H3 and progression of mitosis. Fang et al. [31] demonstrate that RECQL4 interacts with AURKB, thus forming an axis which is essential for cell cycle progression, cellular proliferation and mitotic integrity. Zhang and Huang [32] found that one of the factors for promotion of thyroid cancer was stabilization of AURKB by GSG2. All these combinations have been established in wet lab experiments and in colorectal cancer cells treated with ETC-1922159, these components taken individually and AURKB, were found to be down regulated and their regulation was recorded independently. I was able to rank 2nd order combination of 8 these INDIVIDUAL member along with AURKB. Table 1 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 2 generated from analysis of the ranks in table 1. The table 1 shows rankings of individual members w.r.t AURKB. ZWINT - AURKB shows low ranking of 1 (laplace) , 38 (linear) and 18 (rbf). INCENP - AURKB shows low ranking of 678 (laplace) , 941 (linear) and 571 (rbf). TPX2 - AURKB shows low ranking of 10 (laplace) , 10 (linear) , and (rbf). WNT10B - AURKB shows low ranking of 583 (laplace) , 491 (linear) and 578 (rbf). CDT1 - AURKB shows low ranking of 318 (laplace) , 135 (linear) and 290 (rbf). GTSE1 - AURKB shows low ranking of 537 (laplace) , 283 (linear) and 137 (rbf). VRK1 - AURKB shows low ranking of 181 (laplace) , 848 (linear) and 229 (rbf). RECQL4 - AURKB shows low ranking of 897 (laplace) , 439 (linear) and 508 (rbf). GSG2 - AURKB shows low ranking of 74 (laplace) , 189 (linear) and 375 (rbf). These rankings point to the synergy existing between the two components, which have been down regulated after the drug treatment. RANKING INDIVIDUAL FAMILY VS AURKB RANKING OF INDIVIDUAL FAMILY W.R.TAURKB laplace linear rbf ZWINT - AURKB 1 38 18 INCENP - AURKB 678 941 571 TPX2 - AURKB 10 10 7 WNT10B - AURKB 583 491 578 CDT1 - AURKB 318 135 290 GTSE1 - AURKB 537 283 137 VRK1 - AURKB 181 848 229 RECQL4 - AURKB 897 439 508 GSG2 - AURKB 74 189 375 Table 1: 2nd order interaction ranking between AURKB VS INDIVIDUAL family members. One can also interpret the results of the table 1 graphically, with the following influences - •INDIVIDUAL family w.r.t AURKB with AURKB −>ZWINT / INCENP / TPX2 / WNT10B / CDT1 / GTSE1 / VRK1 / RECQL4 / GSG2 . 6.2.2. AURKB - CENP Kong et al. [33] demonstrate that CENPC-MIS12C interaction helps in recruiting AURKB, thus forming a regulatory loop which is important for chromosome segregation. Further, Liu et al. [34] show that AURKB phosphorylates CENPW thus enhancing the interaction between CENPW and CENPT to ensure chromosome segregation. In col9 RANKING RANBP/NUP FAMILY VS AURKB RANKING OF RANBP/NUP FAMILY W.R.TAURKB laplace linear rbf RANBP1 - AURKB 448 1179 638 RANBP17 - AURKB 2185 2291 925 NUP35 - AURKB 761 421 34 NUP155 - AURKB 886 1471 1554 NUP210 - AURKB 937 520 1725 NUP43 - AURKB 1033 405 959 NUP37 - AURKB 1160 863 1346 NUP160 - AURKB 1181 742 1412 NUP107 - AURKB 1194 1527 883 NUP93 - AURKB 1319 1943 1959 NUP133 - AURKB 2195 2049 1324 NUP85 - AURKB 2272 2534 2674 NUP88 - AURKB 2343 2449 1742 NUP205 - AURKB 2456 1676 2234 NUP54 - AURKB 2586 2617 2735 NUP188 - AURKB 2652 1224 2442 NUP62 - AURKB 2682 2742 2712 Table 11: 2nd order interaction ranking between AURKB VS NUP family. UNEXPLORED COMBINATORIAL HYPOTHESES NUP family w.r.t AURKB RANBP1 AURKB NUP-35/155/210/43/37/160/107 AURKB Table 12: 2nd order combinatorial hypotheses between AURKB and NUP family. Further, GEMIN4 and GEMIN7 showed high ranking with AURKB, thus indicating that they might not be working synergistically with AURKB, before the drug 16 treatment. RANKING GEMIN FAMILY VS AURKB RANKING OF GEMIN FAMILY W.R.TAURKB laplace linear rbf GEMIN6 - AURKB 367 971 523 GEMIN5 - AURKB 595 841 345 GEMIN2 - AURKB 954 687 2150 GEMIN4 - AURKB 1556 659 2082 GEMIN7 - AURKB 2447 1968 2434 Table 13: 2nd order interaction ranking between AURKB VS GEMIN family. One can also interpret the results of the table 13 graphically, with the following influences - •GEMIN family w.r.t AURKB with AURKB −>GEMIN-6/5/2. UNEXPLORED COMBINATORIAL HYPOTHESES GEMIN family w.r.t AURKB GEMIN-6/5/2 AURKB Table 14: 2nd order combinatorial hypotheses between AURKB and GEMIN family. 6.2.8. AURKB - MAD2L Marima et al. [38] show that overexpression of AURKB augments the expression of MAD2L2 and both work synergistically in tumorigenesis and DNA damage response. In colorectal cancer cells treated with ETC-1922159, MAD2L family members and AURKB, were found to be down regulated and their regulation was recorded independently. I was able to rank 2nd order combination of these MAD2L member along with AURKB. Table 15 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 16 generated from analysis of the ranks in table 15. The table 15 shows rankings of individual members w.r.t AURKB. MAD2L1 - AURKB shows low ranking of 114 (laplace), 109 (linear) and 477 (rbf). MAD2L2 - AURKB shows low ranking of 852 (laplace) and 678 (rbf). These rankings point to the synergy existing between the two components, which have been down regulated after the drug treatment. 17 RANKING MAD2L FAMILY VS AURKB RANKING OF MAD2L FAMILY W.R.TAURKB laplace linear rbf MAD2L1 - AURKB 114 109 477 MAD2L2 - AURKB 852 1872 678 Table 15: 2nd order interaction ranking between AURKB VS MAD2L family. One can also interpret the results of the table 15 graphically, with the following influences - •MAD2L family w.r.t AURKB with AURKB −>MAD2L-1/2. UNEXPLORED COMBINATORIAL HYPOTHESES MAD2L family w.r.t AURKB MAD2L-1/2 AURKB Table 16: 2nd order combinatorial hypotheses between AURKB and MAD2L family. 6.2.9. AURKB - HOX Kim et al. [39] observe that castration-resistant prostate cancer deploy the bromodomain and BRD4 to epigenetically regulate HOXB13 gene expression which activates AURKA/AURKB. In colorectal cancer cells treated with ETC-1922159, HOX family members and AURKB, were found to be down regulated and their regulation was recorded independently. I was able to rank 2nd order combination of these HOX member along with AURKB. Table 17 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 18 generated from analysis of the ranks in table 17. The table 17 shows rankings of individual members w.r.t AURKB. HOXB9 - AURKB shows low ranking of 447 (laplace), 1286 (linear) and 1240 (rbf). HOXB8 - AURKB shows low ranking of 689 (laplace), 236 (linear) and 128 (rbf). HOXB5 - AURKB shows low ranking of 1297 (laplace), 1415 (linear) and 1006 (rbf). HOXA9 - AURKB shows low ranking of 1430 (linear) and 1415 (rbf). These rankings point to the synergy existing between the two components, which have been down regulated after the drug treatment. Further, HOXB4, HOXA11, HOXB13, HOXB3 and HOXB7 showed high ranking with AURKB, thus indicating that they might not be working synergistically with AURKB, before the drug treatment. 18 RANKING HOX FAMILY VS AURKB RANKING OF HOX FAMILY W.R.TAURKB laplace linear rbf HOXB9 - AURKB 447 1286 1240 HOXB8 - AURKB 689 236 128 HOXB4 - AURKB 1101 1939 1711 HOXB5 - AURKB 1297 1415 1006 HOXA11 - AURKB 1505 2329 1721 HOXB13 - AURKB 2314 2670 2228 HOXB3 - AURKB 2380 2593 2026 HOXB7 - AURKB 2435 2518 2668 HOXA9 - AURKB 2643 1430 1415 Table 17: 2nd order interaction ranking between AURKB VS HOX family. One can also interpret the results of the table 17 graphically, with the following influences - •HOX family w.r.t AURKB with AURKB −>HOX-B9/B8/B5/A9. UNEXPLORED COMBINATORIAL HYPOTHESES HOX family w.r.t AURKB HOX-B9/B8/B5/A9 AURKB Table 18: 2nd order combinatorial hypotheses between AURKB and HOX family. 6.2.10. AURKB - DHX Zhu et al. [40] show that AURKB targets DHX9 to promote hepatocellular carcinoma progression. In colorectal cancer cells treated with ETC-1922159, DHX family members and AURKB, were found to be down regulated and their regulation was recorded independently. I was able to rank 2nd order combination of these DHX member along with AURKB. Table 19 shows rankings of these combinations. Followed by this is the unexplored 19 combinatorial hypotheses in table 20 generated from analysis of the ranks in table 19. The table 19 shows rankings of individual members w.r.t AURKB. DHX33 - AURKB shows low ranking of 606 (laplace), 947 (linear) and 1374 (rbf). DHX57 - AURKB shows low ranking of 1113 (laplace), 754 (linear) and 642 (rbf). DHX37 - AURKB shows low ranking of 1432 (linear) and 1430 (rbf). These rankings point to the synergy existing between the two components, which have been down regulated after the drug treatment. Further, DHX40, DHX35, DHX30 and DHX9 showed high ranking with AURKB, thus indicating that they might not be working synergistically with AURKB, before the drug treatment. RANKING DHX FAMILY VS AURKB RANKING OF DHX FAMILY W.R.TAURKB laplace linear rbf DHX33 - AURKB 606 947 1374 DHX57 - AURKB 1113 754 642 DHX40 - AURKB 1316 1679 1595 DHX35 - AURKB 1448 2012 2312 DHX30 - AURKB 1658 2235 2205 DHX37 - AURKB 1715 1432 1430 DHX9 - AURKB 2560 2434 2065 Table 19: 2nd order interaction ranking between AURKB VS DHX family. One can also interpret the results of the table 19 graphically, with the following influences - •DHX family w.r.t AURKB with AURKB −>DHX-33/57/37. UNEXPLORED COMBINATORIAL HYPOTHESES DHX family w.r.t AURKB DHX-33/57/37 AURKB Table 20: 2nd order combinatorial hypotheses between AURKB and DHX family. 20 6.2.11. AURKB - PLK Chu et al. [41] show that AURKB activation requires Survivin priming phosphorylation, which is catalyzed by PLK1. Inhibition of PLK1 prevents AURKB activation and correct spindle microtubule attachment. In colorectal cancer cells treated with ETC1922159, PLK family members and AURKB, were found to be down regulated and their regulation was recorded independently. I was able to rank 2nd order combination of these PLK member along with AURKB. Table 21 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 22 generated from analysis of the ranks in table 21. The table 21 shows rankings of individual members w.r.t AURKB. PLK1 - AURKB shows low ranking of 732 (laplace), 755 (linear) and 522 (rbf). PLK4 - AURKB shows low ranking of 958 (laplace), 454 (linear) and 37 (rbf). These rankings point to the synergy existing between the two components, which have been down regulated after the drug treatment. RANKING PLK FAMILY VS AURKB RANKING OF PLK FAMILY W.R.TAURKB laplace linear rbf PLK1 - AURKB 732 755 522 PLK4 - AURKB 958 454 37 Table 21: 2nd order interaction ranking between AURKB VS PLK family. One can also interpret the results of the table 21 graphically, with the following influences - •PLK family w.r.t AURKB with AURKB −>PLK-1/4. UNEXPLORED COMBINATORIAL HYPOTHESES PLK family w.r.t AURKB PLK-1/4 AURKB Table 22: 2nd order combinatorial hypotheses between AURKB and PLK family. 6.2.12. AURKB - BUB Roy et al. [42] show evidence that AURKB phosphorylates BUB1 which maintains spindle assembly checkpoint signaling. In colorectal cancer cells treated with ETC1922159, BUB family members and AURKB, were found to be down regulated and 21 their regulation was recorded independently. I was able to rank 2nd order combination of these BUB member along with AURKB. Table 23 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 24 generated from analysis of the ranks in table 23. The table 23 shows rankings of individual members w.r.t AURKB. BUB1 - AURKB shows low ranking of 58 (laplace), 239 (linear) and 476 (rbf). BUB1B - AURKB shows low ranking of 100 (laplace), 103 (linear) and 537 (rbf). These rankings point to the synergy existing between the two components, which have been down regulated after the drug treatment. Further, BUB3 showed high ranking with AURKB, thus indicating that they might not be working synergistically with AURKB, before the drug treatment. RANKING BUB FAMILY VS AURKB RANKING OF BUB FAMILY W.R.TAURKB laplace linear rbf BUB1 - AURKB 58 239 476 BUB1B - AURKB 100 103 537 BUB3 - AURKB 1773 1836 747 Table 23: 2nd order interaction ranking between AURKB VS BUB family. One can also interpret the results of the table 23 graphically, with the following influences - •BUB family w.r.t AURKB with AURKB −>BUB-1/1B. UNEXPLORED COMBINATORIAL HYPOTHESES BUB family w.r.t AURKB BUB-1/1B AURKB Table 24: 2nd order combinatorial hypotheses between AURKB and BUB family. 6.2.13. AURKB - TOP Via proteomic analysis Morrison et al. [43] identified phosphorylated TOP2A as a potential AURKB substrate. In colorectal cancer cells treated with ETC-1922159, TOP family members and AURKB, were found to be down regulated and their regulation was recorded independently. I was able to rank 2nd order combination of these TOP member along with AURKB. 22 Table 25 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 26 generated from analysis of the ranks in table 25. The table 25 shows rankings of individual members w.r.t AURKB. TOP2A - AURKB shows low ranking of 23 (laplace), 19 (linear) and 36 (rbf). TOP1MT - AURKB shows low ranking of 563 (laplace), 618 (linear) and 418 (rbf). These rankings point to the synergy existing between the two components, which have been down regulated after the drug treatment. Further, TOPBP1 and TOP2B showed high ranking with AURKB, thus indicating that they might not be working synergistically with AURKB, before the drug treatment. RANKING TOP FAMILY VS AURKB RANKING OF TOP FAMILY W.R.TAURKB laplace linear rbf TOP2A - AURKB 23 19 36 TOP1MT - AURKB 563 618 418 TOPBP1 - AURKB 1950 1973 2279 TOP2B - AURKB 2322 1739 2033 Table 25: 2nd order interaction ranking between AURKB VS TOP family. One can also interpret the results of the table 25 graphically, with the following influences - •TOP family w.r.t AURKB with AURKB −>TOP-2A/1MT. UNEXPLORED COMBINATORIAL HYPOTHESES TOP family w.r.t AURKB TOP-2A/1MT AURKB Table 26: 2nd order combinatorial hypotheses between AURKB and TOP family. 6.2.14. AURKB - CDK Lee et al. [44] demonstrate that CDK4 could occupy the promoter region of genes like AURKB and CENPP. Further, gainand lossof function experiments showed that CDK4 regulated expressiong of AURKB and CENPP. In colorectal cancer cells treated with ETC-1922159, CDK family members and AURKB, were found to be down regulated and their regulation was recorded independently. I was able to rank 2nd order combination of these CDK member along with AURKB. 23 Table 27 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 28 generated from analysis of the ranks in table 27. The table 27 shows rankings of individual members w.r.t AURKB. CDK1 - AURKB shows low ranking of 30 (laplace), 4 (linear) and 206 (rbf). CDK20 - AURKB shows low ranking of 653 (laplace), 832 (linear) and 1002 (rbf). CDK4 - AURKB shows low ranking of 1453 (laplace) and 1338 (rbf). These rankings point to the synergy existing between the two components, which have been down regulated after the drug treatment. Further, CDK6 showed high ranking with AURKB, thus indicating that they might not be working synergistically with AURKB, before the drug treatment. RANKING CDK FAMILY VS AURKB RANKING OF CDK FAMILY W.R.TAURKB laplace linear rbf CDK1 - AURKB 30 4 206 CDK20 - AURKB 653 832 1002 CDK4 - AURKB 1453 2213 1338 CDK6 - AURKB 2263 2388 2424 Table 27: 2nd order interaction ranking between AURKB VS CDK family. One can also interpret the results of the table 27 graphically, with the following influences - •CDK family w.r.t AURKB with AURKB −>CDK-1/20/4. UNEXPLORED COMBINATORIAL HYPOTHESES CDK family w.r.t AURKB CDK-1/20/4 AURKB Table 28: 2nd order combinatorial hypotheses between AURKB and CDK family. 6.2.15. AURKB - ALKBH Zhang et al. [45] suggest that ALKBH5 may proliferate renal cell carcinoma by stabilizing AURKB. In colorectal cancer cells treated with ETC-1922159, ALKBH family members and AURKB, were found to be down regulated and their regulation was recorded independently. I was able to rank 2nd order combination of these ALKBH member along with AURKB. 24 Table 29 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 30 generated from analysis of the ranks in table 29. The table 29 shows rankings of individual members w.r.t AURKB. ALKBH2 - AURKB shows low ranking of 494 (laplace), 758 (linear) and 438 (rbf). ALKBH8 - AURKB shows low ranking of 1013 (laplace), 942 (linear) and 632 (rbf). These rankings point to the synergy existing between the two components, which have been down regulated after the drug treatment. Further, ALKBH4 showed high ranking with AURKB, thus indicating that they might not be working synergistically with AURKB, before the drug treatment. RANKING ALKBH FAMILY VS AURKB RANKING OF ALKBH FAMILY W.R.TAURKB laplace linear rbf ALKBH2 - AURKB 494 758 438 ALKBH8 - AURKB 1013 942 632 ALKBH4 - AURKB 1599 2324 2640 Table 29: 2nd order interaction ranking between AURKB VS ALKBH family. One can also interpret the results of the table 29 graphically, with the following influences - •ALKBH family w.r.t AURKB with AURKB −>ALKBH-2/8. UNEXPLORED COMBINATORIAL HYPOTHESES ALKBH family w.r.t AURKB ALKBH-2/8 AURKB Table 30: 2nd order combinatorial hypotheses between AURKB and ALKBH family. 6.2.16. AURKB - PRMT Kim et al. [46] show that asymmetric dimethylation on histone H3 by PRMT6 recruits the chromosomal passenger complex to chromosome arms and facilitates histone H3S10 phosphorylation by AURKB. In colorectal cancer cells treated with ETC1922159, PRMT family members and AURKB, were found to be down regulated and their regulation was recorded independently. I was able to rank 2nd order combination of these PRMT member along with AURKB. Table 31 shows rankings of these combinations. Followed by this is the unexplored combinatorial hypotheses in table 32 generated from analysis of the ranks in table 31. 25 [5] X. Wang, J. Moon, M. E. Dodge, X. Pan, L. Zhang, J. M. Hanson, R. Tuladhar, Z. Ma, H. Shi, N. S. Williams, et al., The development of highly potent inhibitors for porcupine, Journal of medicinal chemistry 56 (2013) 2700–2704. [6] K. D. Proffitt, B. Madan, Z. Ke, V. Pendharkar, L. Ding, M. A. Lee, R. N. Hannoush, D. M. Virshup, Pharmacological inhibition of the wnt acyltransferase porcn prevents growth of wnt-driven mammary cancer, Cancer research 73 (2013) 502–507. [7] J. Liu, S. Pan, M. H. Hsieh, N. Ng, F. Sun, T. Wang, S. Kasibhatla, A. G. Schuller, A. G. Li, D. Cheng, et al., Targeting wnt-driven cancer through the inhibition of porcupine by lgk974, Proceedings of the National Academy of Sciences 110 (2013) 20224–20229. [8] A. J. Duraiswamy, M. A. Lee, B. Madan, S. H. Ang, E. S. W. Tan, W. W. V. Cheong, Z. Ke, V. Pendharkar, L. J. Ding, Y. S. Chew, et al., Discovery and optimization of a porcupine inhibitor, Journal of medicinal chemistry 58 (2015) 5889–5899. [9] X. Zhang, Aurora kinases, Current Biology 18 (2008) R146–R148. [10] D. M. Glover, M. H. Leibowitz, D. A. McLean, H. Parry, Mutations in aurora prevent centrosome separation leading to the formation of monopolar spindles, Cell 81 (1995) 95–105. [11] M. Carmena, W. C. Earnshaw, The cellular geography of aurora kinases, Nature reviews Molecular cell biology 4 (2003) 842–854. [12] E. Willems, M. Dedobbeleer, M. Digregorio, A. Lombard, P. N. Lumapat, B. Rogister, The functional diversity of aurora kinases: a comprehensive review, Cell division 13 (2018) 1–17. [13] J. R. Bischoff, L. Anderson, Y. Zhu, K. Mossie, L. Ng, B. Souza, B. Schryver, P. Flanagan, F. Clairvoyant, C. Ginther, et al., A homologue of drosophila aurora kinase is oncogenic and amplified in human colorectal cancers, The EMBO journal (1998). [14] T. Ota, S. Suto, H. Katayama, Z.-B. Han, F. Suzuki, M. Maeda, M. Tanino, Y. Terada, M. Tatsuka, Increased mitotic phosphorylation of histone h3 attributable to aim-1/aurora-b overexpression contributes to chromosome number instability, Cancer research 62 (2002) 5168–5177. [15] E. T. Shah, C. Molloy, M. Gough, T. Kryza, S. G. Samuel, A. Tucker, M. Bhatia, G. Ferguson, R. Heyman, S. Vora, et al., Inhibition of aurora b kinase (aurkb) enhances the effectiveness of 5-fluorouracil chemotherapy against colorectal cancer cells, British Journal of Cancer 130 (2024) 1196–1205. [16] L. Li, K. Xie, H. Xie, L. Wang, Z. Li, Q. Lu, J. Feng, Aurkb promotes colorectal cancer progression by triggering the phosphorylation of histone h3 at serine 10 to activate ccne1 expression, Aging (Albany NY) 16 (2024) 8019. [17] S. Sinha, Hilbert-schmidt and sobol sensitivity indices for static and time series wnt signaling measurements in colorectal cancer-part a, BMC systems biology 11 (2017) 120. [18] G. Pujol, B. Iooss, A. Janon, K. Boumhaout, S. Da Veiga, J. Fruth, L. Gilquin, J. Guillaume, L. Le Gratiet, P. Lemaitre, et al., Sensitivity: global sensitivity analysis of model outputs, R package version 1 (2017). [19] T. Joachims, Training linear svms in linear time, in: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2006, pp. 217–226. [20] V. G. Tusher, R. Tibshirani, G. Chu, Significance analysis of microarrays applied to the ionizing radiation response, Proceedings of the National Academy of Sciences 98 (2001) 5116–5121. [21] S. E. Choe, M. Boutros, A. M. Michelson, G. M. Church, M. S. Halfon, Preferred analysis methods for affymetrix genechips revealed by a wholly defined control dataset, Genome biology 6 (2005) R16. [22] D. Witten, R. Tibshirani, A comparison of fold-change and the t-statistic for microarray data analysis, Analysis 1776 (2007) 58–85. [23] J. M. Kasuboski, J. R. Bader, P. S. Vaughan, S. B. Tauhata, M. Winding, M. A. Morrissey, M. V. Joyce, W. Boggess, L. Vos, G. K. Chan, et al., Zwint-1 is a novel aurora b substrate required for the assembly of a dynein-binding platform on kinetochores, Molecular biology of the cell 22 (2011) 3318–3330. 32 [24] K. R. Abdul Azeez, S. Chatterjee, C. Yu, T. R. Golub, F. Sobott, J. M. Elkins, Structural mechanism of synergistic activation of aurora kinase b/c by phosphorylated incenp, Nature communications 10 (2019) 3166. [25] J. Iyer, M.-Y. Tsai, A novel role for tpx2 as a scaffold and co-activator protein of the chromosomal passenger complex, Cellular signalling 24 (2012) 1677–1689. [26] Y. Luo, M. Barrios-Rodiles, G. D. Gupta, Y. Y. Zhang, A. A. Ogunjimi, M. Bashkurov, J. M. Tkach, A. Q. Underhill, L. Zhang, M. Bourmoum, et al., Atypical function of a centrosomal module in wnt signalling drives contextual cancer cell motility, Nature communications 10 (2019) 2356. [27] V. Luga, L. Zhang, A. M. Viloria-Petit, A. A. Ogunjimi, M. R. Inanlou, E. Chiu, M. Buchanan, A. N. Hosein, M. Basik, J. L. Wrana, Exosomes mediate stromal mobilization of autocrine wnt-pcp signaling in breast cancer cell migration, Cell 151 (2012) 1542–1556. [28] S. Agarwal, K. P. Smith, Y. Zhou, A. Suzuki, R. J. McKenney, D. Varma, Cdt1 stabilizes kinetochore–microtubule attachments via an aurora b kinase–dependent mechanism, Journal of Cell Biology 217 (2018) 3446–3463. [29] A. R. Tipton, J. D. Wren, J. R. Daum, J. C. Siefert, G. J. Gorbsky, Gtse1 regulates spindle microtubule dynamics to control aurora b kinase and kif4a chromokinesin on chromosome arms, Journal of Cell Biology 216 (2017) 3117–3132. [30] D. S. Moura, I. Campillo-Marcos, M. V´ azquez-Cedeira, P. A. Lazo, Vrk1 and aurkb form a complex that cross inhibit their kinase activity and the phosphorylation of histone h3 in the progression of mitosis, Cellular and Molecular Life Sciences 75 (2018) 2591–2611. [31] H. Fang, K. Niu, D. Mo, Y. Zhu, Q. Tan, D. Wei, Y. Li, Z. Chen, S. Yang, A. S. Balajee, et al., Recql4-aurora b kinase axis is essential for cellular proliferation, cell cycle progression, and mitotic integrity, Oncogenesis 7 (2018) 68. [32] F. Zhang, C. Huang, Gsg2 promotes thyroid cancer via stabilizing aurkb and activating akt pathway, Aging (Albany NY) 16 (2024) 5091. [33] W. Kong, M. Hara, Y. Tokunaga, K. Okumura, Y. Hirano, J. Miao, Y. Takenoshita, M. Hashimoto, H. Sasaki, T. Fujimori, et al., Cenp-c-mis12 complex establishes a regulatory loop through aurora b for chromosome segregation, Life Science Alliance 8 (2025). [34] W. Liu, Z. Dou, C. Wang, G. Zhao, F. Wu, C. Wang, F. Aikhionbare, M. Ye, D. M. Sedzro, Z. Yang, et al., Aurora b promotes the cenp-t–cenp-w interaction to guide accurate chromosome segregation in mitosis, Journal of Molecular Cell Biology (2024) mjae001. [35] R. Tu, Y. Kang, Y. Pan, Y. Da, D. Ren, R. Zhang, Z. Cai, Y. Liu, J. Xu, J. Ma, et al., Usp29 activation mediated by fubp1 promotes aurkb stability and oncogenic functions in gastric cancer, Cancer Cell International 24 (2024) 33. [36] E. Di Cesare, S. Moroni, J. Bartoli, M. Damizia, M. Giubettini, C. Koerner, V. Krenn, A. Musacchio, P. Lavia, Aurora b sumoylation is restricted to centromeres in early mitosis and requires ranbp2, Cells 12 (2023) 372. [37] J. Lin, B. Liu, Y. Zhang, L. Lv, D. Cheng, W. Zhang, Y. Shi, X. Jiang, L. Tang, Y. Yuan, et al., Gemin6 promotes c-myc stabilisation and non-small cell lung cancer progression via accelerating aurkb mrna maturation, Clinical and Translational Medicine 12 (2022). [38] R. Marima, R. Hull, C. Penny, Z. Dlamini, Mitotic syndicates aurora kinase b (aurkb) and mitotic arrest deficient 2 like 2 (mad2l2) in cohorts of dna damage response (ddr) and tumorigenesis, Mutation Research/Reviews in Mutation Research 787 (2021) 108376. [39] E. H. Kim, D. Cao, N. P. Mahajan, G. L. Andriole, K. Mahajan, Ack1–ar and ar–hoxb13 signaling axes: Epigenetic regulation of lethal prostate cancers, Nar Cancer 2 (2020) zcaa018. [40] G. Zhu, L. Luo, Y. He, Y. Xiao, Z. Cai, W. Tong, W. Deng, J. Xie, Y. Zhong, Z. Hu, et al., Aurkb targets dhx9 to promote hepatocellular carcinoma progression via pi3k/akt/mtor pathway, Molecular Carcinogenesis (2024). [41] Y. Chu, P. Y. Yao, W. Wang, D. Wang, Z. Wang, L. Zhang, Y. Huang, Y. Ke, X. Ding, X. Yao, Aurora b kinase activation requires survivin priming phosphorylation by plk1, Journal of molecular cell biology 3 (2011) 260–267. 33 [42] B. Roy, S. J. Han, A. N. Fontan, S. Jema, A. P. Joglekar, Aurora b phosphorylates bub1 to promote spindle assembly checkpoint signaling, Current Biology 32 (2022) 237–247. [43] C. Morrison, A. J. Henzing, O. N. Jensen, N. Osheroff, H. Dodson, S. E. Kandels-Lewis, R. R. Adams, W. C. Earnshaw, Proteomic analysis of human metaphase chromosomes reveals topoisomerase ii alpha as an aurora b substrate, Nucleic acids research 30 (2002) 5318–5327. [44] S. H. Lee, L. R. Rodriguez, R. Majumdar, P. L. M. De Marval, M. L. Rodriguez-Puebla, Cdk4 has the ability to regulate aurora b and cenpp expression in mouse keratinocytes, Oncology Letters 22 (2021) 732. [45] X. Zhang, F. Wang, Z. Wang, X. Yang, H. Yu, S. Si, J. Lu, Z. Zhou, Q. Lu, Z. Wang, et al., Alkbh5 promotes the proliferation of renal cell carcinoma by regulating aurkb expression in an m6a-dependent manner, Annals of translational medicine 8 (2020). [46] S. Kim, N. H. Kim, J. E. Park, J. W. Hwang, N. Myung, K.-T. Hwang, Y. A. Kim, C.-Y. Jang, Y. K. Kim, Prmt6mediated h3r2me2a guides aurora b to chromosome arms for proper chromosome segregation, Nature communications 11 (2020) 612. [47] Y. Asai, K. Fukuchi, Y. Tanno, S. Koitabashi-Kiyozuka, T. Kiyozuka, Y. Noda, R. Matsumura, T. Koizumi, A. Watanabe, K. Nagata, et al., Aurora b kinase activity is regulated by set/taf1 on sgo2 at the inner centromere, Journal of Cell Biology 218 (2019) 3223–3236. [48] P. M. Redli, I. Gasic, P. Meraldi, E. A. Nigg, A. Santamaria, The ska complex promotes aurora b activity to ensure chromosome biorientation, Journal of Cell Biology 215 (2016) 77–93. [49] Y. W. Chan, A. A. Jeyaprakash, E. A. Nigg, A. Santamaria, Aurora b controls kinetochore–microtubule attachments by inhibiting ska complex–kmn network interaction, Journal of Cell Biology 196 (2012) 563–571. [50] Q. Zhang, S. Sivakumar, Y. Chen, H. Gao, L. Yang, Z. Yuan, H. Yu, H. Liu, Ska3 phosphorylated by cdk1 binds ndc80 and recruits ska to kinetochores to promote mitotic progression, Current Biology 27 (2017) 1477–1484. [51] M. Fackler, P. Wolter, S. Gaubatz, The gar domain of gas 2l3 mediates binding to the chromosomal passenger complex and is required for localization of gas2l3 to the constriction zone during abscission, The FEBS Journal 281 (2014) 2123–2135. [52] R. Uehara, Y. Tsukada, T. Kamasaki, I. Poser, K. Yoda, D. W. Gerlich, G. Goshima, Aurora b and kif2a control microtubule length for assembly of a functional central spindle during anaphase, Journal of Cell Biology 202 (2013) 623–636. 34