CT-derived radiomic analysis for predicting the survival rate of patients with non-small cell lung cancer receiving radiotherapy.

BACKGROUND: Radiomics provides an opportunity to minimize adverse effects and optimize the efficacy of treatments noninvasively. This study aims to develop a computed tomography (CT) derived radiomic signature to predict radiological response for the patients with non-small cell lung cancer (NSCLC) receiving radiotherapy. METHODS: Total 815 NSCLC patients receiving radiotherapy were sourced from public datasets. Using CT images of 281 NSCLC patients, we adopted genetic algorithm to establish a predictive radiomic signature for radiotherapy that had optimal C-index value by Cox model. Survival analysis and receiver operating characteristic curve were performed to estimate the predictive performance of the radiomic signature. Furthermore, radiogenomics analysis was performed in a dataset with matched images and transcriptome data. RESULTS: Radiomic signature consisting of three features was established and then validated in the validation dataset (log-rank P = 0.0047) including 140 patient, and showed a significant predictive power in two independent datasets totaling 395 NSCLC patients with binary 2-year survival endpoint. Furthermore, the novel proposed radiomic nomogram significantly improved the prognostic performance (concordance index) of clinicopathological factors. Radiogenomics analysis linked our signature with important tumor biological processes (e.g. Mismatch repair, Cell adhesion molecules and DNA replication) associated with clinical outcomes. CONCLUSIONS: The radiomic signature, reflecting tumor biological processes, could noninvasively predict therapeutic efficacy of NSCLC patients receiving radiotherapy and demonstrate unique advantage for clinical application.

Bioinformatic-based mechanism identification of E2F1-related ceRNA and E2F1 immunoassays in hepatocellular carcinoma.

Background: E2F1 is an important transcription factor. Previous studies have shown that the overexpression of E2F1 is closely related to the occurrence and development of hepatocellular carcinoma (HCC). However, the current research on the regulatory mechanism of E2F1 is still insufficient. This study sought to identify valuable therapeutic E2F1-related targets for HCC. Methods: HCC-related transcriptome data and patient clinical information downloaded from The Cancer Genome Atlas (TCGA) database. The expression of the E2F1 gene in pan-cancer was analyzed using the Tumor IMmune Estimation Resource (TIMER) 2.0 database, and the expression level of E2F1 in HCC was verified using the Gene Expression Profiling Interactive Analysis database. The overall survival (OS) and progression-free survival (PFS) in HCC patients were also analyzed. Subsequently, based on the Encyclopedia of RNA Interactomes (ENCORI) database, we adopted E2F1 as the research objective and identified the target long non-coding RNAs (lncRNAs) and microRNAs that suggested the competing endogenous RNA (ceRNA) mechanisms related to E2F1. We also performed a correlation analysis of E2F1 using the R language package that contained immune cell and immune checkpoint information. Finally, the drug sensitivity of E2F1 was detected using the R language package, "pRRophetic." Results: Ultimately, the following 6 potential ceRNA-based pathways targeting E2F1 were identified-lncRNA: LINC01224, PCBP1-AS1, and ITGA9-AS1-miR-29b-3p-E2F1; lncRNA: SNHG7 and THUMPD3-AS1, and LINC02323-miR-29c-3p-E2F1. Cluster of differentiation (CD)4 memory activated T cells, memory B cells, eosinophils, and T follicular helper cells were positively correlated with E2F1 (P<0.05), and monocytes, naïve B cells, and CD4 memory resting T cells were negatively correlated with E2F1 (P<0.05). The immune checkpoint analysis showed that E2F1 was positively correlated with PDCD1, CTLA4, and LAG3 (P>0.2). According to the drug sensitivity analysis, E2F1 may be sensitive to 39 drugs (P<0.05). Conclusions: This study provides a valuable direction for researching transcription factor E2F1, which may be conducive in identifying research targets for HCC-related molecular biological therapy and immunotherapy in future.

Identifying 18F-FDG PET-metabolic radiomic signature for lung adenocarcinoma prognosis via the leveraging of prognostic transcriptomic module.

Background: Imaging with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET), which identifies molecular and metabolic abnormalities within tumor cells, could support prognostic assessment of lung adenocarcinoma (LUAD). We aimed to develop a radiomic signature with the aid of a transcriptomic module for individualized clinical prognostic assessment of LUAD patients. Methods: Using a gene expression profile consisting of 334 stage I-IIIA LUAD patients, prognostic-related gene coexpression modules were constructed via a weighted correlation network analysis algorithm. The robustness and prognostic performance of the coexpression modules were then tested across 2 gene expression datasets totaling 331 patients. Finally, using a discovery dataset with matched transcriptomic and 18F-FDG PET radiomic data of 15 patients and multiple linear regression analysis, we developed a PET-metabolic radiomic signature that had optimal correlation with the expression of a robust prognostic module. Results: We selected a superior coexpression module for LUAD prognosis in which the genes were significantly enriched in important biological processes associated with tumors (e.g., cell cycle, DNA replication and p53 signaling pathway). The prognostic performance of the module for overall survival (OS) and recurrence-free survival (RFS) was validated in 2 independent gene expression datasets (log-rank P<0.05). Through the leveraging of this prognostic coexpression module, a radiomic signature consisting of 3 PET features associated with metabolic processes was developed in the discovery dataset. The radiomic signature was significantly associated with patients' OS and RFS in an independent PET dataset consisting of 72 LUAD patients (OS: log-rank P=0.0006; RFS: log-rank P=0.0013). Multivariate Cox analysis demonstrated that the radiomic signature was an independent prognostic factor for OS and RFS. Furthermore, the novel proposed radiomic nomograms for OS and RFS had significantly better performance (concordance indices) than did the clinicopathological nomograms. Conclusions: The radiomic signature, which reflects biological processes in tumors (e.g., cell cycle and p53 signaling pathway), could noninvasively identify LUAD patients with poor prognosis who should receive postoperative adjuvant treatment. The signature is suitable for clinical application and could be robustly applied at an individual level across multicenter cohorts.