Censoring-robust time-dependent receiver operating characteristic curve estimators

An inflammatory gene-related prognostic risk score model for prognosis and immune infiltration in glioblastoma

This study aimed to screen for key genes related to the prognosis of patients with glioblastoma (GBM). First, bioinformatics analysis was performed based on databases such as TCGA and MSigDB. Inflammatory-related genes were obtained from the MSigDB database. The TCGA-tumor samples were divided into cluster A and B groups based on consensus clustering. Multivariate Cox regression was applied to construct the risk score model of inflammatory-related genes based on the TCGA database. Second, to understand the effects of model characteristic genes on GBM cells, U-87 MG cells were used for knockdown experiments, which are important means for studying gene function. PLAUR is an unfavorable prognostic biomarker for patients with glioma. Therefore, the model characteristic gene PLAUR was selected for knockdown experiments. The prognosis of cluster A was significantly better than that of cluster B. The verification results also demonstrate that the risk score could predict overall survival. Although the immune cells in cluster B and high-risk groups increased, no matching survival advantage was observed. It may be that stromal activation inhibits the antitumor effect of immune cells. PLAUR knockdown inhibits tumor cell proliferation, migration, and invasion, and promoted tumor cell apoptosis. In conclusion, a prognostic prediction model for GBM composed of inflammatory-related genes was successfully constructed. Increased immune cell expression may be linked to a poor prognosis for GBM, as stromal activation decreased the antitumor activity of immune cells in cluster B and high-risk groups. PLAUR may play an important role in tumor cell proliferation, migration, invasion, and apoptosis.

Long-term survival and second malignant tumor prediction in pediatric, adolescent, and young adult cancer survivors using Random Survival Forests: a SEER analysis

Survival and second malignancy prediction models can aid clinical decision making. Most commonly, survival analysis studies are performed using traditional proportional hazards models, which require strong assumptions and can lead to biased estimates if violated. Therefore, this study aims to implement an alternative, machine learning (ML) model for survival analysis: Random Survival Forest (RSF). In this study, RSFs were built using the U.S. Surveillance Epidemiology and End Results to (1) predict 30-year survival in pediatric, adolescent, and young adult cancer survivors; and (2) predict risk and site of a second tumor within 30 years of the first tumor diagnosis in these age groups. The final RSF model for pediatric, adolescent, and young adult survival has an average Concordance index (C-index) of 92.9%, 94.2%, and 94.4% and average time-dependent area under the receiver operating characteristic curve (AUC) at 30-years since first diagnosis of 90.8%, 93.6%, 96.1% respectively. The final RSF model for pediatric, adolescent, and young adult second malignancy has an average C-index of 86.8%, 85.2%, and 88.6% and average time-dependent AUC at 30-years since first diagnosis of 76.5%, 88.1%, and 99.0% respectively. This study suggests the robustness and potential clinical value of ML models to alleviate physician burden by quickly identifying highest risk individuals.

Cell senescence-associated genes predict the malignant characteristics of glioblastoma

BACKGROUND

Glioblastoma (GBM) is the most malignant, aggressive and recurrent primary brain tumor. Cell senescence can cause irreversible cessation of cell division in normally proliferating cells. According to studies, senescence is a primary anti-tumor mechanism that may be seen in a variety of tumor types. It halts the growth and spread of tumors. Tumor suppressive functions held by cellular senescence provide new directions and pathways to promote cancer therapy.

METHODS

We comprehensively analyzed the cell senescence-associated genes expression patterns. The potential molecular subtypes were acquired based on unsupervised cluster analysis. The tumor immune microenvironment (TME) variations, immune cell infiltration, and stemness index between 3 subtypes were analyzed. To identify genes linked with GBM prognosis and build a risk score model, we used weighted gene co-expression network analysis (WGCNA), univariate Cox regression, Least absolute shrinkage and selection operator regression (LASSO), and multivariate Cox regression analysis. And the correlation between risk scores and clinical traits, TME, GBM subtypes, as well as immunotherapy responses were estimated. Immunohistochemistry (IHC) and cellular experiments were performed to evaluate the expression and function of representative genes. Then the 2 risk scoring models were constructed based on the same method of calculation whose samples were acquired from the CGGA dataset and TCGA datasets to verify the rationality and the reliability of the risk scoring model. Finally, we conducted a pan-cancer analysis of the risk score, assessed drug sensitivity based on risk scores, and analyzed the pathways of sensitive drug action.

RESULTS

The 3 potential molecular subtypes were acquired based on cell senescence-associated genes expression. The Log-rank test showed the difference in GBM patient survival between 3 potential molecular subtypes (P = 0.0027). Then, 11 cell senescence-associated genes were obtained to construct a risk-scoring model, which was systematically randomized to distinguish the train set (n = 293) and the test set (n = 292). The Kaplan-Meier (K-M) analyses indicated that the high-risk score in the train set (P < 0.0001), as well as the test set (P = 0.0053), corresponded with poorer survival. In addition, the high-risk score group showed a poor response to immunotherapy. The reliability and credibility of the risk scoring model were confirmed according to the CGGA dataset, TCGA datasets, and Pan-cancer analysis. According to drug sensitivity analysis, it was discovered that LJI308, a potent selective inhibitor of RSK pathways, has the highest drug sensitivity. Moreover, the GBM patients with higher risk scores may potentially be more beneficial from drugs that target cell cycle, mitosis, microtubule, DNA replication and apoptosis regulation signaling.

CONCLUSION

We identified potential associations between clinical characteristics, TME, stemness, subtypes, and immunotherapy, and we clarified the therapeutic usefulness of cell senescence-associated genes.