PDF(Pubmed)
Abstract：
BACKGROUND: Adaptive mutagenesis observed in colorectal cancer (CRC) cells upon exposure to EGFR inhibitors contributes to the development of resistance and recurrence. Multiple investigations have indicated a parallel between cancer cells and bacteria in terms of exhibiting adaptive mutagenesis. This phenomenon entails a transient and coordinated escalation of error-prone translesion synthesis polymerases (TLS polymerases), resulting in mutagenesis of a magnitude sufficient to drive the selection of resistant phenotypes.
METHODS: In this study, we conducted a comprehensive pan-transcriptome analysis of the regulatory framework within CRC cells, with the objective of identifying potential transcriptome modules encompassing certain translesion polymerases and the associated transcription factors (TFs) that govern them. Our sampling strategy involved the collection of transcriptomic data from tumors treated with cetuximab, an EGFR inhibitor, untreated CRC tumors, and colorectal-derived cell lines, resulting in a diverse dataset. Subsequently, we identified co-regulated modules using weighted correlation network analysis with a minKMEtostay threshold set at 0.5 to minimize false-positive module identifications and mapped the modules to STRING annotations. Furthermore, we explored the putative TFs influencing these modules using KBoost, a kernel PCA regression model.
RESULTS: Our analysis did not reveal a distinct transcriptional profile specific to cetuximab treatment. Moreover, we elucidated co-expression modules housing genes, for example, POLK, POLI, POLQ, REV1, POLN, and POLM. Specifically, POLK, POLI, and POLQ were assigned to the \"blue\" module, which also encompassed critical DNA damage response enzymes, for example. BRCA1, BRCA2, MSH6, and MSH2. To delineate the transcriptional control of this module, we investigated associated TFs, highlighting the roles of prominent cancer-associated TFs, such as CENPA, HNF1A, and E2F7.
CONCLUSIONS: We found that translesion polymerases are co-regulated with DNA mismatch repair and cell cycle-associated factors. We did not, however, identified any networks specific to cetuximab treatment indicating that the response to EGFR inhibitors relates to a general stress response mechanism.
METHODS: In this study, we conducted a comprehensive pan-transcriptome analysis of the regulatory framework within CRC cells, with the objective of identifying potential transcriptome modules encompassing certain translesion polymerases and the associated transcription factors (TFs) that govern them. Our sampling strategy involved the collection of transcriptomic data from tumors treated with cetuximab, an EGFR inhibitor, untreated CRC tumors, and colorectal-derived cell lines, resulting in a diverse dataset. Subsequently, we identified co-regulated modules using weighted correlation network analysis with a minKMEtostay threshold set at 0.5 to minimize false-positive module identifications and mapped the modules to STRING annotations. Furthermore, we explored the putative TFs influencing these modules using KBoost, a kernel PCA regression model.
RESULTS: Our analysis did not reveal a distinct transcriptional profile specific to cetuximab treatment. Moreover, we elucidated co-expression modules housing genes, for example, POLK, POLI, POLQ, REV1, POLN, and POLM. Specifically, POLK, POLI, and POLQ were assigned to the \"blue\" module, which also encompassed critical DNA damage response enzymes, for example. BRCA1, BRCA2, MSH6, and MSH2. To delineate the transcriptional control of this module, we investigated associated TFs, highlighting the roles of prominent cancer-associated TFs, such as CENPA, HNF1A, and E2F7.
CONCLUSIONS: We found that translesion polymerases are co-regulated with DNA mismatch repair and cell cycle-associated factors. We did not, however, identified any networks specific to cetuximab treatment indicating that the response to EGFR inhibitors relates to a general stress response mechanism.
摘要：
背景：在暴露于EGFR抑制剂的结直肠癌（CRC）细胞中观察到的适应性诱变有助于耐药性和复发的发展。多项研究表明，在表现出适应性诱变方面，癌细胞和细菌之间存在平行关系。这种现象需要易错的跨损伤合成聚合酶（TLS聚合酶）的瞬时和协调升级，导致足以驱动抗性表型选择的幅度的诱变。
方法：在本研究中，我们对CRC细胞内的调控框架进行了全面的泛转录组分析，目的是鉴定潜在的转录组模块，包括某些跨损伤聚合酶和控制它们的相关转录因子（TF）。我们的采样策略涉及从用西妥昔单抗治疗的肿瘤中收集转录组数据，EGFR抑制剂，未经治疗的CRC肿瘤，和结肠直肠来源的细胞系，导致不同的数据集。随后，我们使用加权相关网络分析识别了共调控模块,minKMEtosay阈值设置为0.5,以最小化假阳性模块识别,并将模块映射到STRING注释.此外，我们使用KBoost探索了影响这些模块的假定TFs，核PCA回归模型。
结果：我们的分析未揭示西妥昔单抗治疗特异性的独特转录谱。此外，我们阐明了包含基因的共表达模块，例如,POLK,POLI,POLQ,REV1,POLN,和POLM。具体来说,POLK,POLI,和POLQ被分配给“蓝色”模块，其中还包括关键的DNA损伤反应酶，例如。BRCA1、BRCA2、MSH6和MSH2。为了描述这个模块的转录控制，我们调查了相关的TFs，突出突出的癌症相关TFs的作用，比如CENPA,HNF1A,E2F7
结论：我们发现跨损伤聚合酶与DNA错配修复和细胞周期相关因子共同调节。我们没有,然而,确定了西妥昔单抗治疗特异性的任何网络，表明对EGFR抑制剂的反应与一般应激反应机制有关。
方法：在本研究中，我们对CRC细胞内的调控框架进行了全面的泛转录组分析，目的是鉴定潜在的转录组模块，包括某些跨损伤聚合酶和控制它们的相关转录因子（TF）。我们的采样策略涉及从用西妥昔单抗治疗的肿瘤中收集转录组数据，EGFR抑制剂，未经治疗的CRC肿瘤，和结肠直肠来源的细胞系，导致不同的数据集。随后，我们使用加权相关网络分析识别了共调控模块,minKMEtosay阈值设置为0.5,以最小化假阳性模块识别,并将模块映射到STRING注释.此外，我们使用KBoost探索了影响这些模块的假定TFs，核PCA回归模型。
结果：我们的分析未揭示西妥昔单抗治疗特异性的独特转录谱。此外，我们阐明了包含基因的共表达模块，例如,POLK,POLI,POLQ,REV1,POLN,和POLM。具体来说,POLK,POLI,和POLQ被分配给“蓝色”模块，其中还包括关键的DNA损伤反应酶，例如。BRCA1、BRCA2、MSH6和MSH2。为了描述这个模块的转录控制，我们调查了相关的TFs，突出突出的癌症相关TFs的作用，比如CENPA,HNF1A,E2F7
结论：我们发现跨损伤聚合酶与DNA错配修复和细胞周期相关因子共同调节。我们没有,然而,确定了西妥昔单抗治疗特异性的任何网络，表明对EGFR抑制剂的反应与一般应激反应机制有关。
