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Abstract Background Metastasis is the major cause of colorectal cancer (CRC) mortality. Emerging evidence suggests that long noncoding RNAs (lncRNAs) drive cancer metastasis and that their regulatory pathways could be targeted for preventing metastasis. However, the underlying mechanisms of lncRNAs in CRC metastasis remain poorly understood. Methods Microarray analysis was used to screen for differentially expressed lncRNAs. Transwell assays, fibronectin cell adhesion assays, and mouse metastasis models were utilized to evaluate the metastatic capacities of CRC in vitro and in vivo. Chromatin isolation by RNA purification, chromatin immunoprecipitation and chromosome conformation capture were applied to investigate the underlying mechanism involved. qRT‒PCR and transmission electron microscopy were performed to confirm macrophage polarization and the presence of cancer-derived exosomes. Results The lncRNA RP11-417E7.1 was screened and identified as a novel metastasis-associated lncRNA that was correlated with a poor prognosis. RP11-417E7.1 enhances the metastatic capacity of CRC cells in vivo and in vitro. Mechanistically, RP11-417E7.1 binding with High mobility group A1 (HMGA1) promotes neighboring thrombospondin 2 (THBS2) transcription via chromatin loop formation between its promoter and enhancer, which activates the Wnt/β-catenin signaling pathway and facilitates CRC metastasis. Furthermore, exosomes derived from CRC cells transport THBS2 into macrophages, thereby inducing the M2 polarization of macrophages to sustain the prometastatic microenvironment. Notably, netropsin, a DNA-binding drug, suppresses chromatin loop formation mediated by RP11-417E7.1 at the THBS2 locus and significantly inhibits CRC metastasis in vitro and in vivo. Conclusions This study revealed the novel prometastatic function and mechanism of the lncRNA RP11-417E7.1, which provides a potential prognostic indicator and therapeutic target in CRC.

Within the Tan-Lu Fault Zone, the largest active tectonic belt in eastern China, the Anqiu-Juxian Fault exhibits the most recent activity period, evident surface traces, and highest seismic hazard, making it a Holocene active fault. This study utilized the vertical component continuous data observed by 100 short-period temporary stations from August 1–21, 2023, and extracted 1,944 Rayleigh wave group velocity dispersion curves within the period of 0.2–4 s. Using the direct surface wave tomography method, we calculated a high-resolution 3-D shear-wave velocity structure at depths of 0.2–1.25 km within the study area. Our results are summarized as follows: 1) The development of faults F1, F2, and F5 in the Tan-Lu Fault Zone highly correlated with the shear-wave velocity anomalies at depths >0.8 km. Specifically, fault F5 comprised two boundary faults, F5-1 and F5-2, which together controlled a Cenozoic depression covered by a thick, low-velocity sediment layer. 2) The complex velocity structure characteristics in the Suqian area revealed that the influence of faults on the sedimentary layers in the Suqian area was not expressed as an overall uplift or subsidence of the block but rather as differential subsidence. 3) Near Sankeshu, the F5 fault formed a small pull-apart basin. The latest activity in this pull-apart basin has shifted to the fault in the center of the basin, indicating that the pull-apart basin has entered the extinction stage.

Background: Clinical differentiation between pulmonary metastases and noncalcified pulmonary hamartomas (NCPH) often presents challenges, leading to potential misdiagnosis. However, the efficacy of a comprehensive model that integrates clinical features, radiomics, and deep learning (CRDL) for differential diagnosis of these two diseases remains uncertain. Objective: This study evaluated the diagnostic efficacy of a CRDL model in differentiating pulmonary metastases from NCPH. Methods: The authors retrospectively analyzed the clinical and imaging data of 256 patients from the First Medical Centre of the General Hospital of the People's Liberation Army (PLA) and 85 patients from Shanghai Changhai Hospital, who were pathologically confirmed pulmonary hamartomas or pulmonary metastases after thoracic surgery. Employing Python 3.7 software suites, the authors extracted radiomic features and deep learning (DL) attributes from patient datasets. The cohort was divided into training set, internal validation set, and external validation set. The diagnostic performance of the constructed models was evaluated using receiver operating characteristic (ROC) curve analysis to determine their effectiveness in differentiating between pulmonary metastases and NCPH. Results: Clinical features such as white blood cell count (WBC), platelet count (PLT), history of cancer, carcinoembryonic antigen (CEA) level, tumor marker status, lesion margin characteristics (smooth or blurred), and maximum diameter were found to have diagnostic value in differentiating between the two diseases. In the domains of radiomics and DL. Of the 1130 radiomics features and 512 DL features, 24 and 7, respectively, were selected for model development. The area under the ROC curve (AUC) values for the four groups were 0.980, 0.979, 0.999, and 0.985 in the training set, 0.947, 0.816, 0.934, and 0.952 in the internal validation set, and 0.890, 0.904, 0.923, and 0.938 in the external validation set. This demonstrated that the CRDL model showed the greatest efficacy. Conclusions: The comprehensive model incorporating clinical features, radiomics, and DL shows promise for aiding in the differentiation between pulmonary metastases and hamartomas. [ABSTRACT FROM AUTHOR]