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.

Background: Imaging with 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-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, prognosticrelated 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 18 F-FDG PET radiomic data of 15 patients and multiple linear regression analysis, we developed a PETmetabolic 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 ^ ORCID: Yixin Liu, 0000-0002-5988-722X; Lishuang Qi, 0000-0002-2991-6544. 2 Li et al. 18 F-FDG PET radiomic signature for lung cancer prognosis

Background To explore reliable and reproducible prognostic signatures to aid in guiding clinical decision-making, the present study proposed an integrative analysis method to identify a function-derived gene signature for lung adenocarcinoma (LUAD) prognosis. Methods Total 1238 LUAD patients treated with curative resection alone were sourced from public datasets. Using three cohorts of 665 patients in the meta-discovery dataset, we first utilized an integrative analysis method to extract prognostic genes, and identified the essential prognostic genes from a function-derived perspective. Thereafter, we proposed pairwise comparison of single-sample gene set enrichment method to establish a multigene signature for LUAD prognosis based on the function-derived prognostic genes. Results Based on integrative analysis, we identified 14 metabolic-related prognostic genes involved in glycolysis metabolism, and established a function-derived signature consisting of these genes for LUAD prognosis (14GM-PS). The prognostic performance of the signature was rigorously validated in two multiple cross-platform independent datasets comprising 299 (log-rank P = 2.78E-06) and 274 (log-rank P = 0.0042) patients, respectively, with significantly different 5-year survival rate. Multivariate Cox analysis demonstrated that the function-derived signature was an independent prognostic factor for LUAD prognosis. Furthermore, the novel proposed nomogram significantly improved the prognostic performance (concordance index) of clinicopathological factors. The molecular and immune characteristics analysis showed that high-risk patients identified by 14GM-PS were characterized by higher hypoxia, proliferation and stemness scores, and lower immune score, providing evidence that could reflect transcriptomic characteristics that are strongly associated with clinical outcomes in the molecular mechanism. Conclusion This multicenter study illustrates the accuracy and stability of the function-derived signature for LUAD prognosis, and might become a promising genomic tool to guide individualized application and decision-making of LUAD in clinical practice, with further prospective validation in clinical trials.

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