基于TCGA 数据库构建三阴性乳腺癌预后相关的ceRNA 调控网络及分析

目的 通过分析癌症基因组图谱（the cancer genome atlas,TCGA）数据库构建三阴性乳腺癌(triple negativebreast cancer,TNBC）预后相关的竞争性内源性核糖核酸（competitive endogenous RNA, ceRNA）调控网络。方法 从TCGA 数据库中下载TNBC lncRNA 表达RNAseq 数据,对TNBC 患者的 mRNA,miRNA 和lncRNA 进行差异表达分析,并进一步行生存分析,得到与乳腺癌有明显差异表达同时也对生存有相关性的mRNA,miRNA 和lncRNA。同时构建 lncRNA- miRNA - mRNA 相关 ceRNA 调控网,再对生存相关 lncRNA 所相关的 mRNA 进一步功能富集和注释,并构建蛋白质互作网络最终用关键基因通过人类蛋白质图谱（the human protein atlas,HPA）数据库进行验证。结果 在TCGA 中共找到TNBC 差异表达mRNA 2 331 个、差异miRNA 100 个和差异lncRNA 1 269 个。ceRNA 调控网中的 mRNA 在细胞黏附、唾液分泌和血小板活化、用于IgA 产生的肠道免疫网络、补体和凝血级联反应等信号通路中明显富集。生存分析中,1 个差异mRNA(NMUR1）,1 个差异表达miRNA(hsa-miR-6832-3p),2 个差异表达的lncRNA（AC104809,LINC01297）的表达量均与TNBC 患者的预后相关,差异具有统计学意义（P ＜ 0.05）。最后利用HPA数据库对NMUR1 蛋白水平和生存分析验证,NMUR1 的高表达患者的总生存期显著高于NMUR1 低表达组,差异有统计学意义（P ＜ 0.05）。结论 成功构建了促进TNBC 发生发展的 lncRNA-miRNA-mRNA 调控网络,筛选得到生存相关的差异mRNA,miRNA 和lncRNA 为TNBC 发病机制的研究和诊疗生物标志物的探索提供参考依据。     

基于ceRNA调节网络结直肠癌预后关键基因的筛选与分析

目的 本研究利用癌症基因组图谱（TCGA）数据库分别构建结肠癌和直肠癌的lncRNA-miRNA-mRNA的ceRNA调控网络,寻找网络中与患者总体生存率相关的基因,为结直肠癌提供新型预后分子标志物。方法 在TCGA数据库分别下载结肠癌和直肠癌的原始转录组测序数据和临床数据,利用R 4.0.4软件的edgeR程序包进行差异基因的分析。根据差异基因靶向调控关系,构建lncRNA-miRNA-mRNA调节网络。利用Cytoscape 3.7.1软件绘制网络调节图。结合患者的临床资料,利用Survival程序包寻找调控网络中与患者总体生存率相关的基因。结果 在结肠癌的ceRNA网络中,共涉及29个miRNA、128个lncRNA和53个mRNA,有9个lncRNA(AC002511.1、AL590483.1、AP004609.1、GAS6-AS1、HOTAIR、ITCH-IT1、KCNQ1OT1、LINC00491、PVT1),4个mRNA(FJX1、SERPINE1、TPM2、ULBP2),4个miRNA(miR-145、miR-193b、miR-216a、miR-375)与患者预后相关（...     

基于lncRNA-miRNA-mRNA的ceRNA网络构建筛选老年胶质母细胞瘤相关lncRNAs

目的:识别老年胶质母细胞瘤患者相关lncRNA(Long non-coding RNA,长链非编码RNA)。方法:从TCGA数据库下载TCGA-GBM RNA-seq、miRNA-seq、clinical数据。将样本按照年龄大于70岁和小于70岁进行分组。利用edgeR软件进行lncRNA、miRNA、mRNA的差异表达分析。利用miRanda软件分别预测miRNA的mRNA靶基因以及miRNA的lncRNA靶基因。针对筛选出的差异lncRNA和差异mRNA,进行表达相关性分析。根据lncRNA-mRNA相关性分析以及miRNA靶基因分析结果,构建ceRNA网络。结果:根据本文的筛选条件共筛选得到48个lncRNA-miRNA-mRNA调控网络。结论:筛选得到7个lncRNA及48个ceRNA网络可作为老年胶质母细胞瘤患者的生物学标志物进行研究。     

基于ceRNA网络筛选卵巢癌铂类药物的耐药基因及临床验证

目的 探讨竞争性内源RNA(ceRNA)网络筛选卵巢癌铂类药物的耐药基因并进行临床验证。方法 通过PubMed数据库检索卵巢癌长链非编码RNA(lncRNA),构建卵巢癌铂类耐药内源竞争RNA(ceRNA)调控网络。收集2018年4月至2020年4月在西北妇女儿童医院治疗的64例铂类药物耐药卵巢癌患者为耐药组,另纳入同期64例铂类药物敏感卵巢癌患者为对照组。采用荧光定量PCR法检测患者癌组织中lncRNA表达水平,分析lncRNA对卵巢癌铂类药物耐药和患者预后的判断价值。结果 亚细胞定位分析显示共有6个lncRNA位于细胞质,其中HOX转录反义RNA(HOTAIR)和Y框蛋白2(SOX2)为关键ceRNA。lncRNA HOTAIR与miRNA(miR-519d、miR-148b-3p)及mRNA(XIAP、OCT4)构成ceRNA调控网络,lncRNA SOX2与miRNA(miR-302、miR-429、miR-140-3p)及mRNA(EGFR、ABHD2)构成ceRNA。耐药组癌组织lncRNA HOTAIR和lncRNA SOX2水平分别为2.72±0.58和1.45±0.3...     

Circ＿0032821/miR-936/CEP55调控轴在胃癌组织中的表达及与临床特征之间的关系

目的 通过生物信息学技术构建胃癌相关circRNA的内源竞争性RNA(ceRNA)调控网络并探讨其临床意义。方法 挖掘基因表达图谱(GEO)数据库中差异表达的环状RNA(circRNA)、微小RNA(miRNA)、信使RNA(mRNA),Cytoscpe软件构建circRNA、miRNA、mRNA之间的ceRNA调控网络,并对差异表达的mRNA进行基因本体论和京都基因与基因组百科全书数据库通路富集分析,最后利用癌症基因组图谱(TCGA)数据库的343个胃癌组织标本和30个正常组织标本的临床资料对ceRNA网络中的mRNA进行生存分析和表达验证,筛选其中重要的circRNA-miRNA-mRNA调控网络。在胃癌组织中对ceRNA的表达进行初步验证,并分析其与临床特征之间的关系。结果 构建的circRNA-miRNA-mRNA调控网络中包含6个circRNA、4个miRNA和21个mRNA。根据TCGA数据库中胃癌患者临床资料分析和表达验证,ceRNA网络中CEP55和CCDC80与患者生存预后相关,CEP55在胃癌肿瘤标本中的表达显著高于正常组织标本中的表达,差异有统计学意义(P<...     

微小RNA在成年人急性髓细胞白血病中的研究进展

微小RNA（miRNA）为非编码单链RNA分子,于转录后水平调控靶基因mRNA与蛋白表达.miRNA特征性表达在成年人急性髓细胞白血病（AML）的发生、发展过程中起着重要作用,与AML的细胞形态学亚型、细胞遗传学、分子生物学特征及临床预后相关.深入了解AML相关miRNA表达谱,将为成年人AML的诊断、治疗及预后判断提供新理论依据及应用方案.     

基于GEO 数据库筛选慢性胰腺炎恶变过程中关键miRNA 和ceRNA 网络构建

目的 筛选慢性胰腺炎（chronic pancreatitis, CP）进展到胰腺癌（pancreatic cancer, PC）过程中发挥潜在作用的 miRNA及其调控网络。方法 从 GEO数据库中下载芯片数据 GSE24279和 GSE25820,筛选出在 CP和 PC中差异表达的 miRNA（differential expression miRNA,DEM）。预测 DEM的靶基因和长链非编码 RNA（long non-coding RNA）,随后进行靶基因富集分析,构建蛋白互作网络（protein-protein interaction, PPI）并筛选出枢纽基因和具有特殊生物学功能的模块,通过综合分析 DEM,枢纽基因和 LncRNA的表达和预后,基于竞争性内源 RNA（competing endogenous RNAs, ceRNA）的理论,构建 miRNA的调控网络。结果 筛选出 16个 DEM,其靶基因参与共生过程,细胞器组织的正调控,细胞质的核周区域和双链 RNA结合。从 PPI网络中,筛选出 17个枢纽基因和 3个模块。综合分析后,将 hsa-miR-221-3p,hsa-miR-222-3p,hsa-miR-210-3p及 RNPS1,MGRN1作为 CP进展为 PC中关键节点。 41个 LncRNA结合关键 DEM,其中 MIAT,DANT2,TTN-AS1,PAXIP1-AS2和 LINC00473具有预后价值。综合以上结果,构建出包含 3个 DEM,2个基因和 5个 LncRNA的 ceRNA调控网络。结论 研究采用的整合分析方法有助于揭示 CP恶变的机制,构建的 LncRNA-miRNA-基因调控网络,为预测和治疗由 CP进展为 PC的患者提供了新的生物学靶点。     

长链非编码RNA AC002454.1在急性白血病患儿中的表达及临床意义

目的:研究长链非编码RNA(long non-coding RNA, lncRNA)在急性白血病(acute leukemia, AL)患儿和骨髓正常的其它血液病患儿(作为对照)标本中的表达谱的差异,筛选与AL患儿相关的lnc RNA,探讨lnc RNA AC002454.1在AL患儿中的表达及临床意义。方法:采用Microarray基因芯片技术对初诊AL儿童和对照儿童骨髓细胞的lnc RNA进行统计分析。选取差异表达显著的lnc RNA 97个,对急性淋巴细胞白血病(acute lymphoblastic leukemia, ALL)患儿(21例)、急性髓细胞白血病(acute myelocytic leukemia, AML)患儿(22例)和对照儿童(21例)样本采用实时荧光定量PCR(qRT-PCR)技术进行验证并比较。并选择其中差异最大的lncRNA AC002454.1分析其相对表达量与临床指标的关系。结果:经Microarray基因芯片检测,ALL患儿差异表达的lncRNA 1 884个、AML患儿差异表达的lncRNA 4 289个。采用qRT-PCR对异常表达lncRNA进行验证的结果显示, ALL组中表达显著上调9例、AML组中表达显著上调12例,其中AC002454.1在ALL、AML患儿表达差异最显著(P0.05,P0.01)。AC002454.1的相对表达量在初诊ALL免疫分型T系与B系中有显著差异(P0.01)、ALL和AML组有显著差异(P0.05), AC002454.1相对表达量在患儿性别、年龄、初诊时WBC计数、染色体、融合基因、危险度分层之间无显著差异(P值均 0.05)。结论:用长链非编码RNA芯片分析获得了AL患儿的lncRNA表达谱,AC002454.1在AL患儿中异常高表达,其表达量与AL患儿的免疫分型及判断患者预后有一定相关性。     

竞争性内源性RNA在急性髓系白血病中的研究进展

急性髓系白血病 (acute myeloid leukemia, AML) 是多种因素引起的造血系统恶性疾病,具有很高的复发率及死亡率。非编码 RNA在 AML的发生发展过程中发挥着重要作用。竞争性内源性 RNA(competing endogenous RNA, ceRNA)观点指出,不同的非编码 RNA可通过作用于相同的 miRNA反应元件 (MRE)从而调控基因的表达。目前越来越多的研究表明,非编码 RNA作为 ceRNA在调控 AML的增殖、凋亡、侵袭和耐药等生物学过程中发挥关键作用。该文主要对 ceRNA在 AML生物学过程中的调控作用,以及治疗和预后中的临床意义作一综述。     

长链非编码RNA LINC00672与膀胱癌患者预后相关性及作用机制研究

目的 探讨长链非编码RNA LINC00672与膀胱癌（BLCA）患者预后的相关性及作用机制。方法 在TCGA数据库中得到差异表达基因LINC00672,并在TCGA数据库中验证LINC00672的表达以及与BLCA患者预后情况的相关性。用Cox回归法进行多因素分析,在此基础上构建列线图,进行内部验证。通过CTD数据库查询与LINC00672相关的治疗BLCA的化合物,并在PubChem数据库中探索这些相关化合物的结构。在TCGA数据库中分析LINC00672与免疫细胞之间的相关性,探讨可能的作用机制。结果 LINC00672在BLCA肿瘤组织中的表达低于癌旁组织,而且LINC00672高表达的BLCA患者有更好的总生存期（OS）、疾病特异性生存期（DSS）和无铂化疗间期（PFI）。列线图C指数为0.649,预测模型具有一定的准确性。CTD数据库和PubChem数据库中得到9种与LINC00672相关的治疗BLCA的化合物。在TCGA数据库中发现LINC00672表达与大部分的免疫细胞的浸润水平均相关,同时,与PDCD1、CTLA4和CD274均有一定的负相关性。LINC00672可能...     

Screening of molecular targets and construction of a ceRNA network for oxaliplatin resistance in colorectal cancer

Oxaliplatin resistance reduces the efficacy of chemotherapy for colorectal cancer (CRC). This study aimed to screen molecular targets of oxaliplatin resistance in CRC to construct a ceRNA network. The differentially expressed mRNA and lncRNA between the oxaliplatin-resistant and oxaliplatin-sensitive colon cancer cell lines was determined using RNA sequencing data (no. {"type":"entrez-geo","attrs":{"text":"GSE42387","term_id":"42387"}}GSE42387) from the NCBI GEO database. Gene Ontology BP (biological process) and KEGG pathway enrichment analyses were used to analyze the function and pathway enrichment of the differentially expressed mRNA and lncRNA. The lnCeDB and starBase v2.0 were used to predict miRNA, and Cytoscape software was used to build a ceRNA network. The top 5 mRNA, miRNAs, and lncRNAs with high degrees of connectivity in the ceRNA network were validated by qPCR. TCGA colon cancer clinical data was used to perform a survival analysis of patients with differential mRNA and lncRNA expression. Between the two groups, 2515 mRNAs and 23 lncRNAs were differentially expressed. We constructed a ceRNA network containing 503 lncRNA–miRNA–mRNA regulatory pairs, 210 lncRNA–miRNA pairs, 382 miRNA–mRNA pairs, and 212 mRNA co-expression pairs. The differentially expressed lncRNA, miRNA and mRNA were verified by qPCR. One lncRNA (HOTAIR) and 14 mRNAs significantly correlated with patient prognosis. The discovery of differentially expressed genes and the construction of ceRNA networks will provide important resources for the search for therapeutic targets of oxaliplatin resistance. Moreover, this resource will aid the discovery of the mechanisms behind this type of drug resistance.

Oxaliplatin resistance reduces the efficacy of chemotherapy for colorectal cancer (CRC).     

Identification of lncRNA‐associated competing endogenous RNA networks for occurrence and prognosis of gastric carcinoma

BackgroundGastric cancer (GC) is one of the common digestive malignancies worldwide and causes a severe public health issue. So far, the underlying mechanisms of GC are largely unclear. Thus, we aim to identify the long non‐coding RNA (lncRNA)‐associated competing endogenous RNA (ceRNA) for GC.MethodsTCGA database was downloaded and used for the identification of differentially expressed (DE) lncRNAs, miRNAs, and mRNAs, respectively. Then, the ceRNA network was constructed via multiple online datasets and approaches. In addition, various in vitro assays were carried out to validate the effect of certain hub lncRNAs.ResultsWe constructed a ceRNA network, including 76 lncRNAs, 18 miRNAs, and 159 mRNAs, which involved multiple critical pathways. Next, univariate and multivariate analysis demonstrated 11 lncRNAs, including LINC02731, MIR99AHG, INHBA‐AS1, CCDC144NL‐AS1, VLDLR‐AS1, LIFR‐AS1, A2M‐AS1, LINC01537, and LINC00702, and were associated with OS, and nine of those lncRNAs were considered as hub lncRNAs involved in the sub‐ceRNA network. The in vitro assay indicated two lncRNAs, INHBA‐AS1 and CCDC144NL‐AS1, which were positively related to the GC aggressive features, including proliferation, invasion, and migration.ConclusionsWe identified nine hub lncRNAs and the associated ceRNA network related to the prognosis of GC, and then validated two out of them as promising oncogenes in GC.     

Construction of lncRNA‐miRNA‐mRNA network based on ceRNA mechanism reveals the function of lncRNA in the pathogenesis of gout

ObjectiveTo identify differentially expressed lncRNA, miRNA, and mRNA during the pathogenesis of gout, explore the ceRNA network regulatory mechanism of gout, and seek potential therapeutic targets.MethodFirst, gout‐related chips were retrieved by GEO database. Then, the analysis of differentially expressed lncRNAs and mRNAs was conducted by R language and other software. Besides, miRNA and its regulated mRNA were predicted based on public databases, the intersection of differentially expressed mRNA and predicated mRNA was taken, and the lncRNA‐miRNA‐mRNA regulatory relationships were obtained to construct the ceRNA regulatory network. Subsequently, hub genes were screened by the STRING database and Cytoscape software. Then the DAVID database was used to illustrate the gene functions and related pathways of hub genes and to mine key ceRNA networks.ResultsThree hundred and eighty‐eight lncRNAs and 758 mRNAs were identified with significant differential expression in gout patient, which regulates hub genes in the ceRNA network, such as JUN, FOS, PTGS2, NR4A2, and TNFAIP3. In the ceRNA network, lncRNA competes with mRNA for miRNA, thus affecting the IL‐17 signaling pathway, TNF signaling pathway, Oxytocin signaling pathway, and NF‐κB signaling pathway through regulating the cell''s response to chemical stress. The research indicates that five miRNAs (miR‐429, miR‐137, miR‐139‐5p, miR‐217, miR‐23b‐3p) and five lncRNAs (SNHG1, FAM182A, SPAG5‐AS1, HNF1A‐AS1, UCA1) play an important role in the formation and development of gout.ConclusionThe interaction in the ceRNA network can affect the formation and development of gout by regulating the body''s inflammatory response as well as proliferation, differentiation, and apoptosis of chondrocytes and osteoclasts. The identification of potential therapeutic targets and signaling pathways through ceRNA network can provide a reference for further research on the pathogenesis of gout.     

LncRNA PVT1 is increased in renal cell carcinoma and affects viability and migration in vitro

BackgroundRenal cell carcinoma is difficult to diagnose and unpredictable in disease course and severity. There are no specific biomarkers for diagnosis and prognosis estimation feasible in clinical practice. Long non‐coding RNAs (lncRNAs) have emerged as potent regulators of gene expression in recent years. Aside from their cellular role, their expression patterns could be used as a biomarker of ongoing pathology.MethodsIn this work, we used next‐generation sequencing for global lncRNA expression profiling in tumor and non‐tumor tissue of RCC patients. The four candidate lncRNAs have been further validated on an independent cohort. PVT1, as the most promising lncRNA, has also been studied using functional in vitro tests.ResultsNext‐generation sequencing showed significant dysregulation of 1163 lncRNAs; among them top 20 dysregulated lncRNAs were AC061975.7, {"type":"entrez-nucleotide","attrs":{"text":"AC124017.1","term_id":"21327652","term_text":"AC124017.1"}}AC124017.1, {"type":"entrez-nucleotide","attrs":{"text":"AP000696.1","term_id":"6705905","term_text":"AP000696.1"}}AP000696.1, AC148477.4, LINC02437, GATA3‐AS, LINC01762, LINC01230, LINC01271, LINC01187, LINC00472, {"type":"entrez-nucleotide","attrs":{"text":"AC007849.1","term_id":"5091570","term_text":"AC007849.1"}}AC007849.1, LINC00982, LINC01543, {"type":"entrez-nucleotide","attrs":{"text":"AL031710.1","term_id":"3821014","term_text":"AL031710.1"}}AL031710.1, and {"type":"entrez-nucleotide","attrs":{"text":"AC019197.1","term_id":"6648325","term_text":"AC019197.1"}}AC019197.1 as down‐regulated lncRNAs; and SLC16A1‐AS1, PVT1, LINC0887, and LUCAT1 as up‐regulated lncRNAs. We observed statistically significant dysregulation of PVT1, LUCAT1, and LINC00982. Moreover, we studied the effect of artificial PVT1 decrease in renal cell line 786–0 and observed an effect on cell viability and migration.ConclusionOur results show not only the diagnostic but also the therapeutic potential of PVT1 in renal cell carcinoma.     

原发性肝癌GPC3基因异常表达和相关miRNA与lncRNA预测

目的分析GPC3对原发性肝细胞癌(HCC)的诊断价值,并预测其非编码RNA调控机制。方法用组织芯片和免疫组化方法检测GPC3蛋白表达,并用在线工具和TCGA数据库分析、预测GPC3相关的mi RNA和lnc RNA。结果 HCC中GPC3 m RNA和蛋白表达均高于癌旁组织,且和HCC肿瘤病理分级相关,GPC3蛋白表达和患者临床分期相关。预测到和GPC3存在调控关系的mi RNA 10个、lnc RNA 2个。结论 GPC3 m RNA和蛋白可作为HCC的诊断标志物,预测到与GPC3相互作用的非编码RNA。     

基于长链非编码RNA的表达谱特征构建乳腺癌患者预后的风险模型

目的构建长链非编码RNA(long non-coding RNA,LncRNA)表达特征的乳腺癌患者预后的预测模型。方法分析癌症基因组图谱(the cancer genome atlas,TCGA)数据库1081例乳腺癌患者的转录组测序数据中LncRNA表达图谱及临床特征,对TCGA数据库中112对配对的乳腺癌及正常乳腺组织的转录组测序数据进行差异表达分析和单因素分析筛选得到差异表达且与乳腺癌患者预后显著相关的LncRNA(DELncRNA),利用DEseq2包进行差异表达分析(为减弱批次效应,测序数据已用DESeq函数标准化)。1081例乳腺癌患者被分成两组:训练集(541例)和验证集(540例)。将DELncRNA纳入Cox比例风险回归模型,在训练集中筛选和建立多LncRNA预后模型并对模型进行比例风险假定检验(proportional hazards assumption,PH假定检验),计算多基因风险评分,并基于此将患者分为高风险组和低风险组,采用Kaplan-Meier方法进行生存分析,并用验证集540例患者的数据进行验证。评价该模型在TCGA数据库肺鳞癌和肝细胞肝癌等患者中的预后评估价值。基因集富集分析(gene set enrichment analysis,GSEA)分析LncRNA影响患者生存的具体机制。结果转录组测序分析筛选得到2815个差异表达基因,其中与乳腺癌患者预后显著相关的LncRNA共91个(P<0.05)。利用541例训练集乳腺癌患者的91个DELncRNA表达数据进行Cox回归分析,构建了基于5个LncRNA的Cox比例风险回归模型(训练集AUC=0.746,验证集AUC=0.650):AC004551.1、MTOR-AS1、KCNAB1-AS2、FAM230G和LINC01283,并进行PH假定检验(P=0.388)。K-M生存分析发现,训练集中高风险组的生存明显差于低风险组(中位生存时间:7.049年与12.21年,HR 0.367,95%CI 0.228~0.597,P<0.001),在验证集中高风险组患者生存时间也明显短于低风险组(中位生存时间:7.57年与10.85年,HR 0.412,95%CI 0.214~0.793,P<0.001)。在TCGA其他癌种中也得到相似的预测结果:肺鳞癌(HR 0.604,95%CI 0.383~0.951,P=0.007)及肝细胞肝癌(HR 0.551,95%CI 0.307~0.987,P=0.011)。GSEA结果提示,上述5个LncRNA的表达模式与肿瘤细胞的细胞周期调控有关。结论基于AC004551.1、MTOR-AS1、KCNAB1-AS2、FAM230G和LINC01283表达谱构建的预后模型可用于预测乳腺癌患者的预后,有利于进一步指导临床治疗。     

Construction of an algorithm based on oncosis‐related LncRNAs comprising the molecular subtypes and a risk assessment model in lung adenocarcinoma

BackgroundAs an important non‐apoptotic cell death method, oncosis has been reported to be closely associated with tumors in recent years. However, few research reported the relationship between oncosis and lung cancer.MethodsIn this study, we established an oncosis‐based algorithm comprised of cluster grouping and a risk assessment model to predict the survival outcomes and related tumor immunity of patients with lung adenocarcinomas (LUAD). We selected 11 oncosis‐related lncRNAs associated with the prognosis (CARD8‐AS1, LINC00941, LINC01137, LINC01116, {"type":"entrez-nucleotide","attrs":{"text":"AC010980.2","term_id":"6524328","term_text":"AC010980.2"}}AC010980.2, LINC00324, {"type":"entrez-nucleotide","attrs":{"text":"AL365203.2","term_id":"9743577","term_text":"AL365203.2"}}AL365203.2, {"type":"entrez-nucleotide","attrs":{"text":"AL606489.1","term_id":"15591859","term_text":"AL606489.1"}}AL606489.1, {"type":"entrez-nucleotide","attrs":{"text":"AC004687.1","term_id":"3258613","term_text":"AC004687.1"}}AC004687.1, HLA‐DQB1‐AS1, and {"type":"entrez-nucleotide","attrs":{"text":"AL590226.1","term_id":"13446586","term_text":"AL590226.1"}}AL590226.1) to divide the LUAD patients into different clusters and different risk groups. Compared with patients in clsuter1, patients in cluster2 had a survival advantage and had a relatively more active tumor immunity. Subsequently, we constructed a risk assessment model to distinguish between patients into different risk groups, in which low‐risk patients tend to have a better prognosis. GO enrichment analysis revealed that the risk assessment model was closely related to immune activities. In addition, low‐risk patients tended to have a higher content of immune cells and stromal cells in tumor microenvironment, higher expression of PD‐1, CTLA‐4, HAVCR2, and were more sensitive to immune checkpoint inhibitors (ICIs), including PD‐1/CTLA‐4 inhibitors. The risk score had a significantly positive correlation with tumor mutation burden (TMB). The survival curve of the novel oncosis‐based algorithm suggested that low‐risk patients in cluster2 have the most obvious survival advantage.ConclusionThe novel oncosis‐based algorithm investigated the prognosis and the related tumor immunity of patients with LUAD, which could provide theoretical support for customized individual treatment for LUAD patients.