Genomic and transcriptomic significance of multiple primary lung cancers detected by next-generation sequencing in clinical settings.

Effective diagnosis and understanding of the mechanism of intrapulmonary metastasis (IM) from multiple primary lung cancers (MPLC) aid clinical management. However, the actual detection panels used in the clinic are variable. Current research on tumor microenvironment (TME) of MPLC and IM is insufficient. Therefore, additional investigation into the differential diagnosis and discrepancies in TME between two conditions is crucial. Two hundred and fourteen non-small cell lung cancer patients with multiple tumors were enrolled and 507 samples were subjected to DNA sequencing (NGS 10). Then, DNA and RNA sequencing (master panel) were performed on the specimens from 32 patients, the TME profiles between tumors within each patient and across patients and the differentially expressed genes were compared. Four patients were regrouped with NGS 10 results. Master panel resolved the classifications of six undetermined patients. The TME in MPLC exhibited a high degree of infiltration by natural killer (NK) cells, CD56dim NK cells, endothelial cells, etc., P < 0.05. Conversely, B cells, activated B cells, regulatory cells, immature dendritic cells, etc., P < 0.001, were heavily infiltrated in the IM. NECTIN4 and LILRB4 mRNA were downregulated in the MPLC (P < 0.0001). Additionally, NECTIN4 (P < 0.05) and LILRB4 were linked to improved disease-free survival in the MPLC. In conclusion, IM is screened from MPLC by pathology joint NGS 10 detections, followed by a large NGS panel for indistinguishable patients. A superior prognosis of MPLC may be associated with an immune-activating TME and the downregulation of NECTIN4 and LILRB4 considered as potential drug therapeutic targets.

Exploration of a screening model for intrahepatic cholangiocarcinoma patients prone to cuproptosis and mechanisms of the susceptibility of CD274-knockdown intrahepatic cholangiocarcinoma cells to cuproptosis.

Intrahepatic cholangiocarcinoma (ICC) is a form of liver cancer with poor long-term survival rates that requires novel therapeutic methods. Our team's previous research found that ICC patients prone to cuproptosis possessed a more satisfactory long-term prognosis and a more sensitive response to copper carrier Elesclomol. Thus, we aimed to identify new diagnostic and treatment strategies for ICC patients prone to cuproptosis and further explore the associated intracellular and extracellular mechanisms of ICC cells prone to cuproptosis. We employed FU-ICC (n = 255) as the training dataset, and validated our findings using SRRSH-ICC (from our center, n = 65), GSE26566 (n = 104), E-MTAB-6389 (n = 78), and scRNA-seq (n = 14) datasets. Single sample gene set enrichment analysis and subsequent unsupervised cluster analysis was conducted on the training dataset for the pan-programmed cell death gene set (including apoptosis, autophagy, ferroptosis, pyroptosis, necroptosis, and cuproptosis) to define and screen ICC patients prone to cuproptosis. We constructed a nomogram model using weighted gene co-expression network analysis and machine learning algorithms to predict ICC patients prone to cuproptosis, then explored its clinical value with multi-center transcriptome profiling. Furthermore, we validated the hub genes with in vitro and animal experiments to define ICC cells prone to cuproptosis. Ultimately, bulk and single-cell transcriptome profiling were utilized to explore the immune microenvironment of ICC cells prone to cuproptosis. Our nomogram model could help predict ICC patients prone to cuproptosis and possessed excellent prediction efficiency and clinical significance via internal and external verification. In vitro experiments demonstrated that ICC cells with siRNA-mediated knockdown of CD274 (PD-L1) and stimulation with elescomol-CuCl2 were prone to cuproptosis, and CD274-negative ICC cells could be defined as ICC cells prone to cuproptosis. The safety and feasibility of lenti-sh CD274+Elesclomol-CuCl2 as a therapeutic approach for ICC were verified using bioinformatics analysis and animal experiments. Bulk and single-cell transcriptome profiling indicated that the interactions between ICC cells prone to cuproptosis and monocytes/macrophages were particularly relevant. In conclusion, this study systematically and comprehensively explored cuproptosis in ICC for the first time. We constructed precise diagnostic and treatment strategies for ICC patients prone to cuproptosis and further explored the intracellular and extracellular mechanisms of ICC cells prone to cuproptosis. Further work with large prospective cohorts will help verify these conclusions.

Circ-RAPGEF5 promotes intrahepatic cholangiocarcinoma progression by stabilizing SAE1 to facilitate SUMOylation.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is an aggressive malignancy with a poor prognosis. The underlying functions and mechanisms of circular RNA and SUMOylation in the development of ICC remain poorly understood. METHODS: Circular RNA hsa_circ_0001681 (termed Circ-RAPGEF5 hereafter) was identified by circular RNA sequencing from 19 pairs of ICC and adjacent tissue samples. The biological function of Circ-RAPGEF5 in tumor proliferation and metastasis was examined by a series of in vitro assays. A preclinical model was used to validate the therapeutic effect of targeting Circ-RAPGEF5. RNA pull-down and dual-luciferase reporter assays were used to access the RNA interactions. Western blot and Co-IP assays were used to detect SUMOylation levels. RESULTS: Circ-RAPGEF5, which is generated from exons 2 to 6 of the host gene RAPGEF5, was upregulated in ICC. In vitro and in vivo assays showed that Circ-RAPGEF5 promoted ICC tumor proliferation and metastasis, and inhibited apoptosis. Additionally, high Circ-RAPGEF5 expression was significantly correlated with a poor prognosis. Further investigation showed that SAE1, a potential target of Circ-RAPGEF5, was also associated with poor oncological outcomes. RNA pull-down and dual-luciferase reporter assays showed an interaction of miR-3185 with Circ-RAPGEF5 and SAE1. Co-IP and western blot assays showed that Circ-RAPGEF5 is capable of regulating SUMOylation. CONCLUSION: Circ-RAPGEF5 promotes ICC tumor progression and SUMOylation by acting as a sponge for miR-3185 to stabilize SAE1. Targeting Circ-RAPGEF5 or SAE1 might be a novel diagnostic and therapeutic strategy in ICC.

Bulk and single-cell transcriptome profiling reveal extracellular matrix mechanical regulation of lipid metabolism reprograming through YAP/TEAD4/ACADL axis in hepatocellular carcinoma.

Emerging studies have revealed matrix stiffness promotes hepatocellular carcinoma (HCC) development. We studied metabolic dysregulation in HCC using the TCGA-LIHC database (n=374) and GEO datasets (GSE14520). HCC samples were classified into three heterogeneous metabolic pathway subtypes with different metabolic profiles: Cluster 1, an ECM-producing subtype with upregulated glycan metabolism; Cluster 2, a hybrid subtype with partial pathway dysregulation. Cluster 3, a lipogenic subtype with upregulated lipid metabolism; These three subtypes have different prognosis, clinical features and genomic alterations. We identified key enzymes that respond to matrix stiffness and regulate lipid metabolism through bioinformatic analysis. We found long-chain acyl-CoA dehydrogenase (ACADL) is a mechanoreactive enzyme that reprograms HCC cell lipid metabolism in response to extracellular matrix stiffness. ACADL is also regarded as tumor suppressor in HCC. We found that increased extracellular matrix stiffness led to activation of Yes-associated protein (YAP) and the YAP/TEA Domain transcription factor 4 (TEAD4) transcriptional complex was able to directly repress ACADL at the transcriptional level. The ACADL-dependent mechanoresponsive pathway is a potential therapeutic target for HCC treatment.

Structural Model Identification Using a Modified Electromagnetism-Like Mechanism Algorithm.

A modified electromagnetism-like mechanism (EM) algorithm is proposed to identify structural model parameters using modal data. EM is a heuristic algorithm, which utilizes an attraction-repulsion mechanism to move the sample points towards the optimal solution. In order to improve the performance of original algorithm, a new local search strategy, new charge and force calculation formulas, new particle movement and updating rules are proposed. The test results of benchmark functions show that the modified EM algorithm has better accuracy and faster convergence rate than the original EM algorithm and the particle swarm optimization (PSO) algorithm. In order to investigate the applicability of this approach in parameter identification of structural models, one numerical truss model and one experimental shear-building model are presented as illustrative examples. The identification results show that this approach can achieve remarkable parameter identification even in the case of large noise contamination and few measurements. The modified EM algorithm can also be used to solve other optimization problems.