Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma

A ferroptosis-related gene signature for graft loss prediction following renal allograft

Allogeneic kidney transplantation (renal allograft) is the most effective treatment for advanced kidney disease. Previous studies have indicated that ferroptosis participates in the progression of acute kidney injury and renal transplant failure. However, few studies have evaluated the prognostic value of ferroptosis on renal transplantation outcomes. In this study, a total of 22 differentially expressed ferroptosis-related genes (DFGs) were identified, which were mainly enriched in infection-related pathways. Next, a ferroptosis-related gene signature, including GA-binding protein transcription factor subunit beta 1 (GABPB1), cyclin-dependent kinase inhibitor 1A (CDKN1A), Toll-like receptor 4 (TLR4), C-X-C motif chemokine ligand 2 (CXCL2), caveolin 1 (CAV1), and ribonucleotide reductase subunit M2 (RRM2), was constructed to predict graft loss following renal allograft. Moreover, receiver operating characteristic (ROC) curves (area under the ROC curve [AUC] > 0.8) demonstrated the accuracy of the gene signature and univariate Cox analysis suggested that the gene signature could play an independent role in graft loss (p < 0.05). Furthermore, the nomogram and calibration plots also indicated the good prognostic capability of the gene signature. Finally, immune-related and cytokine signaling pathways were mostly enriched in renal allograft patients with poor outcomes. Considered together, a ferroptosis-related gene signature and nomogram based on DFGs were created to predict the 1-, 2- and 3- year graft loss probability of renal allograft patients.The gene signature could serve as a valuable biomarker for predicting graft loss, contributing to improving the outcome of allogeneic kidney transplantation.

Combining Genetic Mutation and Expression Profiles Identifies Novel Prognostic Biomarkers of Lung Adenocarcinoma

Motivation:

Although several prognostic signatures for lung adenocarcinoma (LUAD) have been developed, they are mainly based on a single-omics data set. This article aims to develop a novel set of prognostic signatures by combining genetic mutation and expression profiles of LUAD patients.

Methods:

The genetic mutation and expression profiles, together with the clinical profiles of a cohort of LUAD patients from The Cancer Genome Atlas (TCGA), were downloaded. Patients were separated into 2 groups, namely, the high-risk and low-risk groups, according to their overall survivals. Then, differential analysis was performed to determine differentially expressed genes (DEGs) and mutated genes (DMGs) in the expression and mutation profiles, respectively, between the 2 groups. Finally, a prognostic model based on the support vector machine (SVM) algorithm was developed by combining the expression values of the DEGs and the mutation times of the DMGs.

Results:

A total of 13 DEGs and 7 DMGs were recognized between the 2 groups. Their prognostic values were validated using independent cohorts. Compared with several existing signatures, the proposed prognostic signatures exhibited better prediction performance in the testing set. In addition, it is found that 1 of the 7 DMGs, GRIN2B, is mutated much more frequently in the high-risk group, showing a potential value as a therapy target.

Conclusions:

Combining multi-omics data sets is an applicable manner to identify novel prognostic signatures and to improve the prognostic prediction for LUAD, which will be heuristic to other types of cancers.

Multi-omics integrative analysis and survival risk model construction of non-small cell lung cancer based on The Cancer Genome Atlas datasets

Lung cancer is a major cause of cancer-associated mortality worldwide. However, the association between multi-omics data and survival in lung cancer is not fully understood. The present study investigated the performance of the methylation survival risk model in multi-platform integrative molecular subtypes and aimed to identify copy number (CN) variations and mutations that are associated with survival risk. The present study analyzed 439 lung adenocarcinoma cases based on DNA methylation, RNA, microRNA (miRNA), DNA copy number and mutations from The Cancer Genome Atlas datasets. First, six cancer subtypes were identified using integrating DNA methylation, RNA, miRNA and DNA copy number data. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to extract methylation sites of survival model and calculate the methylation-based survival risk indices for all patients. Survival for patients in the high-risk group was significantly lower compared with that for patients in the low-risk group (P<0.05). The present study also assessed methylation-based survival risks of the six subtypes and analyzed the association between survival risk and non-silent mutation rate, number of segments, fraction of segments altered, aneuploidy score, number of segments with loss of heterozygosity (LOH), fraction of segments with LOH and homologous repair deficiency. Finally, the specific copy number regions and mutant genes associated with the different subtypes were identified (P<0.01). Chromosome regions 17q24.3 and 11p15.5 were identified as those with the most survival risk-associated copy number variation regions, while a total of 29 mutant genes were significantly associated with survival (P<0.01).

Circ-MMP2 (circ-0039411) induced by FOXM1 promotes the proliferation and migration of lung adenocarcinoma cells in vitro and in vivo

Numerous reports have stated the significance of cellular events such as proliferation, migration and EMT (epithelial-mesenchymal transition) for cancer development, but the related molecular mechanism remains elusive. FOXM1 (forkhead box transcription M1) is a nuclear co-activator participating in lung adenocarcinoma (LUAD). Thus, this study tried to explain the function of FOXM1 and its downstream molecular mechanism in LUAD. We uncovered FOXM1 upregulation in LUAD and demonstrated that FOXM1 facilitated β-catenin nuclear translocation to activate the transcription of downstream genes. Moreover, we discovered that FOXM1 transcriptionally activated circ0039411 which derived from matrix metallopeptidase 2 (MMP2) (also named as circ-MMP2), while MMP2 is a known downstream target of β-catenin. As for functional investigation, knockdown of circ-0039411 suppressed the proliferation, migration and EMT in LUAD cells and also hindered in vivo growth and metastasis of LUAD tumor. Mechanistically, circ-0039411 enhanced the stability of FOXM1 mRNA by recruiting IGF2BP3 (insulin like growth factor 2 mRNA binding protein 3), thus forming a positive feedback loop. In conclusion, this study revealed that FOXM1-induced circ-MMP2 (circ-0039411) contributes to malignant behaviors of LUAD cells via relying on FOXM1, potentially infusing inspirations for the search of new molecular targets for LUAD treatment.

A five-long non-coding RNA signature with the ability to predict overall survival of patients with lung adenocarcinoma

An increasing number of studies have indicated that the abnormal expression of certain long non-coding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with lung adenocarcinoma (LUAD). The aim of the present study was to establish an lncRNA signature to predict the survival of patients with LUAD. The gene expression profiles and associated clinical information of patients with LUAD were downloaded from The Cancer Genome Atlas database. The cohort was randomly sub-divided into training and verification cohorts. Univariate Cox regression analysis was performed on differentially expressed lncRNAs in the training cohort to select candidate lncRNAs closely associated with survival. Next, a risk score (RS) model consisting of 5 lncRNAs was established by multivariate Cox regression analysis on candidate lncRNAs. Using the median RS obtained from the training cohort as a cut-off point, patients were classified into high- and low-risk groups. Kaplan-Meier survival analysis revealed a significant difference in OS between high- and low-risk groups. The survival prediction ability of the 5-lncRNA signature was further tested in the verification and total cohorts. The results proved that the 5-lncRNA signature had good reliability and stability in survival prediction for patients with LUAD. The univariate Cox regression analysis for the 5-lncRNA signature in each cohort indicated that the 5-lncRNA signature was closely associated with survival. Multivariate Cox regression analysis and stratification analysis proved that the prognostic signature was an independent predictor of survival for patients with LUAD. In addition, functional enrichment analysis indicated that the 5 prognostic lncRNAs may be involved in the tumorigenesis of LUAD through cancer-associated pathways and biological processes. Taken together, the present study provided a 5-lncRNA signature that may serve as an independent survival predictor for patients with LUAD.