基于竞争性内源RNA网络探讨豆甾醇干预肺鳞癌的作用机制

目的：利用生物信息学和网络药理学方法,对肿瘤基因组图谱（TCGA）数据库进行分析,探究豆甾醇与肺鳞癌（LUSC）发生发展相关的调控网络及药物作用机制。方法：综合TCGA RNA-seq数据,利用R 4.0.2进行加权基因共表达网络分析（WGCNA）、差异表达分析、基因本体论（GO）分析和蛋白相互作用网络（PPI）分析筛选核心基因,构建竞争性内源RNA（ceRNA）网络,基于网络药理学进行分子对接,分析豆甾醇作用于LUSC的机制。结果：WGCNA联合差异表达分析共确定801个信度高的基因,它们与染色体分离、有丝分裂核分裂、染色体着丝粒区域等功能密切相关。基于PPI分析得出前10个关键基因（CDC20、BUB1、CCNB2、BUB1B、CDK1、CCNB1、KIF2C、NDC80、CDCA8、CENPF）,且均与生存率密切相关,最终构建了CDCA8与上游miRNA hsa-let-7b-5p及与之关联的14个lncRNAs的ceRNA网络。豆甾醇与CDCA8对接良好。结论：14个lncRNAs与hsa-let-7b-5p竞争性调控CDCA8,可能在肺鳞癌发生发展过程中发挥重要作用,可能是豆甾醇干预LUSC的潜在机制。     

基于竞争性内源RNA网络探讨豆甾醇干预肺鳞癌的作用机制

目的：利用生物信息学和网络药理学方法,对肿瘤基因组图谱（TCGA）数据库进行分析,探究豆甾醇与肺鳞癌（LUSC）发生发展相关的调控网络及药物作用机制。方法：综合TCGA RNA-seq数据,利用R 4.0.2进行加权基因共表达网络分析（WGCNA）、差异表达分析、基因本体论（GO）分析和蛋白相互作用网络（PPI）分析筛选核心基因,构建竞争性内源RNA（ceRNA）网络,基于网络药理学进行分子对接,分析豆甾醇作用于LUSC的机制。结果：WGCNA联合差异表达分析共确定801个信度高的基因,它们与染色体分离、有丝分裂核分裂、染色体着丝粒区域等功能密切相关。基于PPI分析得出前10个关键基因（CDC20、BUB1、CCNB2、BUB1B、CDK1、CCNB1、KIF2C、NDC80、CDCA8、CENPF）,且均与生存率密切相关,最终构建了CDCA8与上游miRNA hsa-let-7b-5p及与之关联的14个lncRNAs的ceRNA网络。豆甾醇与CDCA8对接良好。结论：14个lncRNAs与hsa-let-7b-5p竞争性调控CDCA8,可能在肺鳞癌发生发展过程中发挥重要作用,可能是豆甾醇干预LUSC的潜在机制。     

基于生物信息学对食管鳞状细胞癌新辅助放化疗敏感性生物标志物的发掘

目的：通过生物信息学方法筛选能预测食管鳞状细胞癌（esophageal squamous cell carcinoma,ESCC）对新辅助放化疗（neoadjuvant chemoradiotherapy,NCRT）具有敏感性的生物标志物。方法：利用GEO数据库中的数据集GSE45670,采用多种生物信息学方法,包括蛋白互作（protein-protein interaction,PPI）网络、差异表达基因（differentially expressed genes,DEGs）分析和加权基因共表达网络分析（weighted gene co-expression network analysis,WGCNA）,筛选出与食管鳞癌放化疗敏感性相关的生物标志物。同时,收集2017年至2021年期间在四川省肿瘤医院诊断为ESCC的26例患者的活检样本,采用免疫组化方法进行生物标志物的初步验证;通过Kaplan-Meier法利用TCGA数据库中ESCC相关数据分析生物标志物与ESCC预后相关性。结果：筛选出350个DEGs,与WGCNA中紫色模块的159个基因进行交叉获取了重叠的5个基因（AKR...     

食管鳞癌新辅助放化疗后复发风险分层分析

目的 探讨胸段食管鳞癌新辅助放化疗联合手术治疗后的复发风险模式,并分析术后病理分期与复发风险之间的关系。方法 回顾分析2002-2015年郑州大学附属肿瘤医院及中山大学肿瘤防治中心收治的174例局部晚期胸段食管鳞癌患者的病历资料。全组患者均采用术前同期放化疗联合手术治疗,化疗采用以铂类为基础的化疗方案,放疗剂量为40.0~50.4 Gy,常规分割。采用Kaplan-Meier法计算生存率,Logrank检验差异,Cox模型多因素分析。结果 中位随访时间为53.9个月,新辅助放化疗后病理完全缓解率为44.8%,其中59例(33.9%)患者复发。术后病理分期为0/Ⅰ、Ⅱ、Ⅲ期患者复发率分别为22.2%、38.7%、68.2%(P=0.000),疗后5年无复发生存率分别为74.7%、61.4%、20.9%(P=0.000)。20.5%的0/Ⅰ期或Ⅱ期患者的复发时间在术后3年以上,而Ⅲ期患者的复发时间均在2年以内。多因素分析结果显示年龄、临床分期、化疗方案、放化疗相关病理反应是影响无复发生存的因素(P=0.027、0.047、0.010、0.005)。结论 胸段食管鳞癌新辅助放化疗后的病理分期与复发风险密切相关,临床医生可根据不同的病理分期制定个体化的随访监测策略。     

膀胱癌诊断与预后相关基因TPM1和CALD1的筛选及验证

目的 筛选及验证膀胱癌诊断与预后相关基因TPM1和CALD1。方法 利用TCGA和GEO数据库中的数据集分别得到414例和188例膀胱癌基因芯片表达数据，通过构建加权基因共表达网络（WGCNA）及识别肿瘤组织与正常组织间差异表达基因的方法，得到与膀胱癌高度相关的枢纽基因。使用STRING数据库构建蛋白互作网络，筛选出预后相关的枢纽基因。将郑州人民医院29例膀胱癌样本及HPA数据库中的免疫组织化学结果作为外部验证结果。结果 TCGA数据库中共筛选出915个差异表达基因，GSE13507中筛选出464个差异表达基因。通过WGCNA得到两个相关性最强的模块：蓝色模块（相关系数为0.60，P=1E-44）和青色模块（相关系数为0.52，P=7E-19），得到156个交集基因。通过蛋白质互作网络分析筛选出10个枢纽基因，其中TPM1和CALD1基因与膀胱癌生存的相关性最大，并得到了外部验证组验证。 结论 TPM1和CALD1基因与膀胱癌预后紧密相关，也是促进肿瘤进展的枢纽基因。     

基于Cf DNA检测探索局部晚期食管鳞癌同期放化疗敏感性相关基因的初步研究

目的 探索局部晚期食管鳞癌同期放化疗敏感性相关基因及分子标志物。方法 收集2017—2018年间 31例在中国医学科学院肿瘤医院采用根治性同期放化疗的局部晚期食管鳞癌患者治疗前外周血并提取血浆Cf DNA,采用基于NovaSeq6000高通量测序平台的目标基因捕获测序技术检测靶基因与肿瘤突变负荷(TMB)变化。根据放化疗近期疗效将患者分为放化疗敏感组(CR+PR)和放化疗抗拒组(SD+PD),综合生物信息学和临床资料分析两组间的基因突变和TMB差异。结果 31例患者测序数据中突变频率>10%的肿瘤相关基因为Tp53、NOTCH1、BRAF、FGFR4、CDKN2A、ATRX和AXIN2,他们在放化疗敏感组和放化疗抗拒组均有分布且相近。高频突变基因主要与7条信号通路相关,主要涉及凋亡信号通路和细胞周期信号通路等,它们主要参与的是RTK-RAS信号通路。放化疗敏感组患者TMB值高于放化疗抵抗组(P=0.04),但GXYLT1和KRT18基因在放化疗抵抗组患者的突变率高于放化疗敏感组(P<0.05)。结论 Tp53、NOTCH1和CDKN2A可能是与食管鳞癌发生发展相关的高频突变基因,而KRT18、GXYLT1和TMB与局部晚期食管鳞癌患者同期放化疗敏感性密切相关。     

直肠癌同步放化疗患者营养状态与放化疗不良反应相关性分析

目的 分析直肠癌同步放化疗患者营养状态与放化疗近期不良反应的相关性。方法 收集2018-2019年间浙江省肿瘤医院收治的115例行同步放化疗的直肠癌患者,同时采用欧洲营养风险筛查工具(NRS 2002)和患者主观整体评估量表(PG-SGA)评估患者放疗期间的营养风险状况,采用美国RTOG及不良反应常见术语标准评估急性放化疗不良反应。Spearman′s分析营养状态与放化疗急性不良反应相关性。结果 从放化疗开始前到放化疗第4周患者的营养风险呈逐步增加趋势,随后营养风险又逐步下降。NRS 2002评分和PG-SGA评分均与直肠癌放化疗患者血液学不良反应(r=0.26,P<0.05;r=0.31,P<0.01)、上消化道反应(r=0.51,P<0.01;r=0.63,P<0.01)、下消化道反应(r=0.23,P<0.05;r=0.45,P<0.01)、疲劳(r=0.47,P<0.01;r=0.64,P<0.01)均呈正相关,并且PG-SGA和不同不良反应之间的相关性系数大于NRS 2002。分层分析显示Ⅱ-ⅢB期、<65岁及术后辅助放化疗患者,营养状况和不良反应程度显著相关(均P<0.05)。结论 直肠癌患者同步放化疗期间存在较高的营养不良风险,营养不良风险越高患者放化疗急性不良反应通常越大,建议加强直肠癌放化疗期间的动态营养评估及支持。     

术前同期放化疗治疗局部进展期中低位直肠癌疗效分析

目的 评价术前同期放化疗用于局部进展期中低位直肠癌的有效性和耐受性。方法 2007—2013年 共入组 51例 T₃、T₄期或N (+)的病理证实初治直肠癌患者。全盆腔三维放疗技术, 45.0~ 50.4 Gy分 25~ 28次;化疗采用FOLFOX4或XELOX方案,化疗在放疗开始第1、4周进行共2个周期;放化疗结束后 4~ 8周手术,术后1个月内开始行同方案巩固化疗。Kaplan-Meier 法计算生存率并 Logrank 检验和单因素分析及 Cox 模型行多因素预后分析。结果 49例 患者完成术前放化疗及手术治疗,中位随访时间2.9年 ,总保肛率为65%,总降期率为59%。pCR为20%。总≥3级不良反应发生率为25%,总并发症发生率为31%。3、5年 样本数分别为24、12例 ,3、5年 OS分别为81%、69%, DFS分别为76%、60%, LRFS分别为78%、70%,DMFS分别为82%、74%。多因素分析显示肿瘤降期为 5年 DFS、LRFS的独立预后因素。 结论 局部进展期中低位直肠癌术前放疗同期FOLFOX4或XELOX方案化疗能提高病理降期率和pCR率及保肛率,降期者可能有更好的生存优势。     

环状RNA circATL2通过miR‐205调控直肠癌放射敏感性的研究

目的 探讨环状RNA对直肠癌放射敏感性的影响及其作用机制。方法 通过基因测序筛选直肠癌放射敏感和放射抵抗组织（放化疗前活检组织）中的差异circRNAs,后通过细胞实验验证circRNAs对肠癌细胞的放射敏感性的影响。结果 通过对直肠癌组织标本进行基因测序,发现64个在直肠癌放射敏感组织中高表达的circRNAs,36个在放射敏感组织中低表达的circRNAs。选取10个差异circRNAs,经qRT‐PCR验证,发现circATL2在直肠癌放射敏感组织中高表达。后在细胞实验中,上调circATL2表达,能显著提高肠癌的放射敏感性。随后分析在直肠癌放射敏感组织中低表达的8个miRNA,通过双荧光素酶实验,验证了miR‐205与circATL2是直接结合的关系。进一步的挽救实验,证实了直肠癌中circATL2通过miR‐205调控直肠癌的放射敏感性。结论 circATL2通过靶向吸附miR‐205调控直肠癌的放射敏感性。     

甲状腺乳头状癌潜在预后生物标志物的识别北大核心

目的:探讨甲状腺乳头状癌的潜在发病机制、治疗靶点及预后生物标志物。方法:使用加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)基于来自Gene Expression Omnibus数据库的数据集GSE27155和GSE58545构建共表达网络,从而识别与甲状腺乳头状癌密切相关的模块和基因。使用来自于Gene Expression Profiling Interactive Analysis的数据来进行验证。结果:本研究发现棕色模块表明与该疾病密切相关,且该模块中的基因被富集到Ras信号通路、MAPK信号通路及Wnt信号通路等(P<0.05)。基于生存分析发现:4个枢纽基因LMOD1、GHR、GPM6A和ZMAT4与患者预后相关。来自GEPIA的数据显示枢纽基因的差异表达具有显著性意义。结论:本次研究证实棕色模块的枢纽基因LMOD1、GHR、GPM6A和ZMAT4可能为甲状腺乳头状癌潜在发病机制提供了新的切入点,对该疾病临床治疗提供新的见解,对于完善个体化治疗提供一定的帮助。     

基于加权基因共表达网络分析构建胃癌转移预测模型

背景与目的：通过对高通量功能基因组数据库（Gene Expression Omnibus,GEO）中一组含有转移和非转移性胃癌以及癌旁组织的基因芯片进行加权基因共表达网络分析（weighted gene co-expression network analysis,WGCNA）,筛选出与胃癌发生和转移显著相关的分子,为胃癌的治疗和生存期延长的研究提供参考。方法：采用WGCNA方法对19例胃癌患者基因表达进行差异分析;结合临床数据,选取与临床信息高度相关的基因模块构建网络。结果：利用WGCNA我们筛选出了Lightsteelblue模块与胃癌转移明确相关,同时对模块中的基因进一步进行分析,筛选出4个基因：C5AR1、AP3M2、TYMP、ANXA2P1作为核心靶基因。通过表达分析和受试者工作特征（receiver operating characteristic,ROC）曲线分析验证上述基因与胃癌发生、转移明确相关。同时,通过外部ONCOMINE和Kaplan-Meier plot数据库验证上述基因在胃癌中高表达,高表达这些基因的患者有着更差的预后。并利用GSE14210数据集构建基于这些基因的预测患者预后和疾病进展模型。结果提示我们所筛选的4个基因具有成为潜在胃癌转移和治疗生物标志物的可能。结论：鉴定筛选出与胃癌发生和转移相关的4个基因,可为胃癌发生、转移和治疗的研究提供参考。     

头颈部鳞状细胞癌中与p53突变相关基因的鉴定

目的:探讨在头颈部鳞状细胞癌（HNSCC）中与p53突变相关的潜在关键基因。方法:从GEO数据库中下载芯片数据GSE107591,用R语言加权重基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)包构建基因共表达网络并划分模块。选择与p53变异相关的基因模块进行GO分析和KEGG通路分析,并结合cytoscape筛选中枢基因。结果:基因共表达网络包括7个模块。其中蓝色（blue）模块与p53变异呈正相关,GO富集分析结果为细胞外基质组织等,KEGG通路为ECM受体相互作用等。蓝色模块的中枢基因为TPX2,CCNB2和DLGAP5。绿松石（turquoise）模块与p53变异呈负相关,GO富集分析结果为转录、DNA模板化等,KEGG通路为细胞黏附分子等。绿松石模块的中枢基因包括VWA3A,ARMC4,CFAP46和C11orf88（并列第三）。结论:通过WGCNA的方法能够从转录组数据中挖掘到头颈部鳞状细胞癌中与p53变异相关的重要基因,为疾病研究提供新的候选基因和分子机制。     

基因表达谱结合共表达网络分析发现胰腺导管腺癌预后分子机制

目的 结合胰腺导管癌（PDAC）表达谱数据、临床资料以及分析基因共表达网络,定义PDAC预后相关的基因,并挖掘其预后相关的分子调控机制.方法 通过Cox生存分析寻找表达水平与生存时间相关的基因（P＜ 0.000 1）.计算得到的相关基因之间的共表达关系,建立共表达网络,进一步寻找网络中核心模块.结果 Cox生存分析得到273个与患者的生存数据相关的候选基因,构建共表达网络[Pearson相关系数（r）＞ 0.9,P＜0.05]寻找核心模块（MCODE算法,得分＞2）,最后发现1个包含6个基因（PTMAP7、FTSJ3、TLK2、GOLGA3、PTTG1IP、ZBTB37） 14个共表达关系的核心模块.其中GOLGA3基因已经被发现与PDAC的转移侵染有关;其他基因也都被研究发现属于已知癌基因.结论 构建的PDAC相关基因的共表达模块与PDAC预后相关,且可能作为PDAC预后相关标志分子.     

基于数据挖掘分析PTGER3在肠型胃癌中的表达及临床意义

目的:探讨PTGER3在肠型胃癌中的表达及临床意义。方法:从GEO数据库的GSE29272数据集中下载58对肠型胃癌组织和癌旁组织的基因表达谱数据,筛选出差异表达基因,并利用DAVID数据库进行基因的功能富集分析。采用WGCNA软件对差异表达基因进行权重基因共表达网络分析,以挖掘出肠型胃癌发生发展过程中的关键调控基因。分析关键调控基因表达水平与肠型胃癌预后的相关性,并在Kaplan-Meier Plotter数据库中验证其预后意义。结果:共获得393个差异表达基因,DAVID 功能富集分析显示它们主要涉及ECM受体相互作用、p53信号通路、PI3K-Akt信号通路和癌症信号通路等。PTGER3是癌症信号通路上的一个重要成员,在肠型胃癌组织中的表达明显上调。差异基因的共表达网络分析发现PTGER3是模块枢纽基因,与胃癌的发生发展关系密切。GSE29272数据集和Kaplan-Meier Plotter数据库的生存分析均显示PTGER3高表达与预后不良显著相关（P=0.034;P<0.001）。结论:PTGER3表达上调可能参与了肠型胃癌的发生发展,而且其高表达提示患者预后不良。     

Potential of the cell-free blood-based biomarker uroplakin 2 RNA to detect recurrence after surgical resection of lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, and ~30% of patients with LUAD develop cancer recurrence after surgery. The present study aimed to identify and validate biomarkers that may be used to monitor recurrence following LUAD surgery. Data from patients with LUAD were downloaded from The Cancer Genome Atlas database and postoperative recurrence samples were selected. Subsequently, weighted gene co-expression network analysis (WGCNA) was subsequently performed to identify key co-expression gene modules. Additionally, enrichment analysis of the key gene modules was performed using the Database for Annotation, Visualization and Integrated Discovery. Furthermore, survival analysis was performed on the most notable biomarker, uroplakin 2 (UPK2), which was downloaded from the Oncomine database, and its effect on prognosis was assessed. WGCNA identified 39 gene modules, of which one was most associated with recurrence. Among them, UPK2, kelch domain containing 3, galanin receptor 2 and tyrosinase-related protein 1 served a central role in the co-expression network and were significantly associated with the survival of patients. A total of 132 blood samples were collected from patients with LUAD with free UPK2 in the plasma. The expression levels of UPK2 relative to GADPH were 0.1623 and 0.2763 in non-relapsed and relapsed patients, respectively. Receiver operating characteristic curve analysis was used to detect free UPK2 mRNA in the blood in order to monitor postoperative recurrence, resulting in an area under the curve of 0.767 and a 95% CI of 0.675–0.858. Patients with high free UPK2 mRNA expression had unfavorable survival outcomes compared with those with low UPK2 expression. Therefore, free UPK2 mRNA expression in the plasma may have the potential to act as an indicator of postoperative recurrence in patients with early stage LUAD.     

The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data

Liver cancer is a serious threat to public health and has fairly complicated pathogenesis. Therefore, the identification of key genes and pathways is of much importance for clarifying molecular mechanism of hepatocellular carcinoma (HCC) initiation and progression. HCC-associated gene expression dataset was downloaded from Gene Expression Omnibus database. Statistical software R was used for significance analysis of differentially expressed genes (DEGs) between liver cancer samples and normal samples. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, based on R software, were applied for the identification of pathways in which DEGs significantly enriched. Cytoscape software was for the construction of protein–protein interaction (PPI) network and module analysis to find the hub genes and key pathways. Finally, weighted correlation network analysis (WGCNA) was conducted to further screen critical gene modules with similar expression pattern and explore their biological significance. Significance analysis identified 1230 DEGs with fold change >2, including 632 significantly down-regulated DEGs and 598 significantly up-regulated DEGs. GO term enrichment analysis suggested that up-regulated DEG significantly enriched in immune response, cell adhesion, cell migration, type I interferon signaling pathway, and cell proliferation, and the down-regulated DEG mainly enriched in response to endoplasmic reticulum stress and endoplasmic reticulum unfolded protein response. KEGG pathway analysis found DEGs significantly enriched in five pathways including complement and coagulation cascades, focal adhesion, ECM–receptor interaction, antigen processing and presentation, and protein processing in endoplasmic reticulum. The top 10 hub genes in HCC were separately GMPS, ACACA, ALB, TGFB1, KRAS, ERBB2, BCL2, EGFR, STAT3, and CD8A, which resulted from PPI network. The top 3 gene interaction modules in PPI network enriched in immune response, organ development, and response to other organism, respectively. WGCNA revealed that the confirmed eight gene modules significantly enriched in monooxygenase and oxidoreductase activity, response to endoplasmic reticulum stress, type I interferon signaling pathway, processing, presentation and binding of peptide antigen, cellular response to cadmium and zinc ion, cell locomotion and differentiation, ribonucleoprotein complex and RNA processing, and immune system process, respectively. In conclusion, we identified some key genes and pathways closely related with HCC initiation and progression by a series of bioinformatics analysis on DEGs. These screened genes and pathways provided for a more detailed molecular mechanism underlying HCC occurrence and progression, holding promise for acting as biomarkers and potential therapeutic targets.     

加权共表达网络分析确定甲状腺乳头状癌淋巴结转移和远处转移相关核心基因

目的:筛选甲状腺乳头状癌(papillary thyroid carcinoma,PTC)淋巴结转移和远处转移相关核心基因.方法:对TCGA数据库中PTC的数据样本进行加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA),筛选出与PTC发生淋巴结...     

Identification of Potential Gene Network Associated with HCV-Related Hepatocellular Carcinoma Using Microarray Analysis

In order to identify potential specific gene networks of Hepatitis C virus (HCV) related hepatocellular carcinoma (HCC), weighted gene co-expression network analysis (WGCNA) was performed, which may provide an insight into the potential mechanism of the HCC development. HCV-related HCC and normal sample data were downloaded from GEO, T test of limma package was used to screen different expression genes (DEGs); KEGG pathway was used to analyze related biochemical pathways, and WGCNA was used to construct clustering trees and screen hub genes in the HCC-specific modules. A total of 1151 DEGs were authenticated between the HCC and normal liver tissue samples, including 433 upregulated and 718 downregulated genes. Among these genes, three specific modules of HCC were constructed, including Tan, Yellow and Cyan, but only Yellow module had a significant enrichment score in substance combination module with three hub genes: SLA2547, EFNA4 and MME. Although Tan and Cyan separately had four and three hub genes, but the bio-functions of them did not have significant enrichment scores (score < 2). SLA2547, EFNA4 and MME may play important roles in the substance combination of HCV-related HCC, so studying the function of this gene network may provide us a deeper understanding of HCV-related HCC.     

Correlating Transcriptional Networks to Papillary Renal Cell Carcinoma Survival: A Large-Scale Coexpression Analysis and Clinical Validation

We aimed to investigate the potential mechanisms of progression and identify novel prognosis-related biomarkers for papillary renal cell carcinoma (PRCC) patients. The related data were derived from The Cancer Genome Atlas (TCGA) and then analyzed by weighted gene coexpression network analysis (WGCNA). The correlation between each module and the clinical traits were analyzed by Pearson’s correlation analysis. Pathway analysis was conducted to reveal potential mechanisms. Hub genes within each module were screened by intramodule analysis, and visualized by Cytoscape software. Furthermore, important hub genes were validated in an external dataset and clinical samples. A total of 5,839 differentially expressed genes were identified. By using WGCNA, we identified 21 coregulatory gene clusters based on 289 PRCC samples. We found many modules were significantly associated with clinicopathological characteristics. The gray, pink, light yellow, and salmon modules served as prognosis indicators for PRCC patients. Pathway enrichment analyses found that the hub genes were significantly enriched in the cancer-related pathways. With the external Gene Expression Omnibus (GEO) validation dataset, we found that PCDH12, GPR4, and KIF18A in the pink and yellow modules were continually associated with the survival status of PRCC, and their expressions were positively correlated with pathological grade. Notably, we randomly chose PCDH12 for validation, and the results suggested that the PRCC patients with higher pathological grades (II + III) mostly had higher PCDH12 protein expression levels compared with those patients in grade I. These validated hub genes play critical roles in the prognosis prediction of PRCC and serve as potential biomarkers for future personalized treatment.