衰老是癌症主要的内在触发因素，然而，在癌症动物模型和癌症患者中研究衰老风险因素的研究很少，且不足以代表癌症临床试验。为了更好地理解衰老和癌症的复杂调控网络，从健康衰老模型、百岁老人及其后代中鉴定出8个候选衰老相关长链非编码RNA（CarLncs），通过支持向量机（SVM）和随机森林（RF）算法等一系列前沿分析手段，探索它们与肾透明细胞癌（KIRC）的关系。利用从TCGA和GTEx数据库下载的数据构建了CarLncs-miRNA-mRNA的调控网络，并从网络中筛选出了5个与衰老相关的特征基因，用于开发KIRC预后模型。在建模过程中进行了严格的筛选流程，得到了一个使用特征基因的百万转录本（TPM）值计算的公式：“LncAging_score = 0.008* MMP11 + 0.066* THBS1-IT1 + (-0.014)* DYNLL2 + (-0.030)* RMND5A+ 0.008* PEG10”。ROC分析和预测图表表明，我们的模型在KIRC预后方面表现良好。在这8个CarLncs中，我们发现THBS1-IT1在12种癌症类型中明显失调。全面的泛癌症分析表明，THBS1-IT1不仅是KIRC的潜在预后生物标志物，还是多种癌症（如LUSC、BLCA、GBM、LGG、MESO、PAAD、STAD和THCA）的潜在预后生物标志物，它与肿瘤微环境（TME）和肿瘤免疫细胞浸润（TICI）相关，其高表达与不良生存相关。

Aging is responsible for the main intrinsic triggers of cancers; however, the studies of aging risk factors in cancer animal models and cancer patients are rare and insufficient to be represented in cancer clinical trials. For a better understanding of the complex regulatory networks of aging and cancers, 8 candidate aging related long noncoding RNAs (CarLncs) identified from the healthy aging models, centenarians and their offsprings, were selected and their association with kidney renal clear cell carcinoma (KIRC) was explored by series of cutting edge analyses such as support vector machine (SVM) and random forest (RF) algorithms. Using data downloaded from TCGA and GTEx databases, a regulatory network of CarLncs-miRNA-mRNA was constructed and five genes within the network were screened out as aging related feature genes for developing KIRC prognostic models. After a strict filtering pipeline for modeling, a formula using the transcript per million (TPM) values of feature genes "LncAging_score = 0.008* MMP11 + 0.066* THBS1-IT1 + (-0.014)* DYNLL2 + (-0.030)* RMND5A+ 0.008* PEG10" was developed. ROC analysis and nomogram suggest our model achieves a great performance in KIRC prognosis. Among the 8 CarLncs, we found that THBS1-IT1 was significantly dysregulated in 12 cancer types. A comprehensive pan-cancer analysis demonstrated that THBS1-IT1 is a potential prognostic biomarker in not only KIRC but also multiple cancers, such as LUSC, BLCA, GBM, LGG, MESO, PAAD, STAD and THCA, it was correlated with tumor microenvironment (TME) and tumor immune cell infiltration (TICI) and its high expression was related with poor survival.