肿瘤细胞治疗研究
英文原题：Deciphering the Dysregulated Pathways and Candidate Therapeutic Compounds for Primary Ovarian Cancer Using Whole Transcriptomics Data and Next Generation Knowledge Discovery Strategies.
RNA-seq与前沿NGKD方法的结合在识别关键细胞和分子通路及OC治疗方面具有重大前景。
卵巢癌（OC）是一种妇科癌症，由于确诊时多处于晚期且治疗选择有限，其死亡率较高。本研究旨在利用转录组数据，识别特异性靶向原发性浸润性上皮性卵巢癌（EOC）的细胞和分子通路以及潜在抗癌化合物。通过采用下一代知识发现（NGKD）方法，我们试图利用 RNA 测序（RNA-seq）数据揭示原发性浸润性 EOC 复杂的分子图景，并解析针对这一衰弱性疾病的潜在治疗药物。我们对来自全 RNA-seq 实验的 Gene Expression Omnibus（GEO）数据集 GSE1295399 进行了 NGKD 分析。利用来自 GEO 的原始计数和过滤后的元数据，我们使用 ExpressAnalyst 平台，在原发性浸润性 EOC 与良性 EOC 之间，基于 Log 2 fold change（≤ ±0.6）和 p 值 cutoff < 0.05，识别出 2123 个差异表达基因（DEGs）。随后，我们使用 ExpressAnalyst 和 WebGestalt 工具进一步分析这些 DEGs，以确定差异调控的细胞和分子通路以及基因本体（GOs），包括生物学过程（GO-BP）、分子功能（GO-MF）和细胞组分（GO-CC）。我们使用 L1000 Fire Works Display（L1000FWD）和 L1000 Characteristic Direction Signature Search Engine（L1000CDS2）工具，解析具有逆转 OC 相关基因特征潜力的合成或天然化学化合物。参与关键细胞和分子通路的DEGs呈正富集，这些通路包括氧化磷酸化、细胞周期、蛋白酶体、程序性细胞死亡蛋白-1 (PD-1) 信号传导、核因子kappa B (NF-kB) 信号传导、细胞因子和趋化因子信号传导、NK 细胞介导的细胞毒性以及癌症中的microRNAs。在原发性侵袭性EOC中，核糖体、翻译、翻译起始和延伸以及转化生长因子-beta (TGF-β) 信号传导呈负富集。基于NGKD分析，我们鉴定了约50种合成或天然化合物，包括naproxol、palbociclib、etoposide、wortmannin、PP-110、AZD-8055、amsacrine和BRD-K6595526。本研究的结果可有助于根据每种肿瘤类型的独特特征制定个性化治疗方案，从而促进个性化或精准治疗方案的开发，并提高临床中的诊断和预后能力。总之，RNA-seq与前沿NGKD方法的结合在识别关键细胞和分子通路以及OC治疗方面具有重大前景。
Ovarian cancer (OC) is a type of gynaecological cancer with a higher mortality rate due to diagnosis at an advanced stage and limited treatment options. This study aimed to leverage transcriptomic data to identify cellular and molecular pathways and potential anti-cancer compounds that specifically target primary invasive epithelial ovarian cancer (EOC). By employing next-generation knowledge discovery (NGKD) methodologies, we sought to unravel the intricate molecular landscape of primary invasive EOC using RNA sequencing (RNA-seq) data and decipher potential therapeutics for this debilitating disease. We performed NGKD analysis of the Gene Expression Omnibus (GEO) dataset GSE1295399 obtained from whole RNA-seq experiments. Using the raw counts and filtered metadata from GEO, we identified 2123 differentially expressed genes (DEGs) based on a Log 2 fold change (≤ ±0.6) and a p-value cutoff of < 0.05, between primary invasive EOC and benign EOC using the ExpressAnalyst platform. The DEGs were further analysed using both ExpressAnalyst and WebGestalt tools for differentially regulated cellular and molecular pathways and gene ontology (GOs), including biological process (GO-BP), molecular function (GO-MF) and cellular components (GO-CC). Both L1000 Fire Works Display (L1000FWD) and L1000 Characteristic Direction Signature Search Engine (L1000CDS2) tools were used to decipher synthetic or natural chemical compounds with the potential to reverse OC-associated gene signatures. DEGs implicated in key cellular and molecular pathways, such as oxidative phosphorylation, cell cycle, proteasome, programmed cell death protein-1 (PD-1) signalling, nuclear factor kappa B (NF-kB) signalling, cytokine and chemokine signalling, natural killer cell-mediated cytotoxicity and microRNAs in cancer, were positively enriched. The ribosome, translation, translational initiation and elongation and transforming growth factor-beta (TGF-β) signalling were negatively enriched in primary invasive EOC. Based on NGKD analysis, we identified approximately 50 synthetic or natural compounds, including naproxol, palbociclib, etoposide, wortmannin, PP-110, AZD-8055, amsacrine and BRD-K6595526. The results of this study could aid in the development of personalized treatment plans based on the unique profile of each tumour type, thus facilitating the development of personalized or precision treatment plans and improving diagnostic and prognostic capabilities in the clinic. In conclusion, the combination of RNA-seq and cutting-edge NGKD methodologies holds significant promise for identifying key cellular and molecular pathways and OC therapeutics.
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