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Recently studies found that APEX1 was abnormally expressed in melanoma, indicating that it might be involved in the development of melanoma. However, the underlying mechanism and the interaction between APEX1 and LINC00470 in melanoma are not clear. Therefore, we aimed to investigate the role of LINC00470 in the development of melanoma in this work. We discovered that LINC00470 was overexpressed in melanoma tissues and cells compared with the adjacent normal tissues and cells by qPCR. The overexpression of LINC00470 promoted the proliferation and migration of melanoma cells. The functional investigation demonstrated that LINC00470 activated the transcription factor, ZNF131, to regulate the APEX1 expression, which finally promoted cell proliferation and migration. In contrast, knockdown of LINC00470 could significantly inhibit the melanoma cell proliferation and migration, and suppress the growth of tumor in vivo. Overexpression of APEX1 could reverse the impact of the silence of LINC00470 in melanoma cells. In summary, our studies revealed that LINC00470 promoted melanoma proliferation and migration by enhancing the expression of APEX1, which indicated that LINC00470 might be a therapeutic target for the treatment of melanoma.

The study investigates the molecular mechanisms underlying the skeletal muscle-enhancing effects of Epimedin C, a natural flavonoid, focusing on its interaction with the mitochondrial cristae structural protein MIC25. Using C57BL/6 mice, we demonstrate that Epimedin C enhances exercise performance through preservation of mitochondrial function. Proteomic analysis identified MIC25 as a key protein modulated by Epimedin C, whose stability is regulated via ubiquitin-dependent degradation. Functional experiments revealed that Epimedin C disrupts the interaction between MIC25 and ubiquitin-conjugating enzyme C (UBC), preventing MIC25 degradation and maintaining the integrity of the mitochondrial contact site and cristae organizing system (MICOS). This stabilization preserves mitochondrial cristae structure, improves ATP production, and delays muscle fatigue. Notably, MIC25 overexpression mimicked Epimedin C’s effects, while its knockdown abolished these benefits. Our findings establish MIC25 as a critical effector of Epimedin C, elucidating a novel pathway through which flavonoids maintain mitochondrial homeostasis to enhance muscle function. These insights hold promise for developing therapies targeting muscle atrophy and metabolic disorders.

Microsatellite instability (MSI) is a form of hypermutation that occurs in some tumors due to defects in cellular DNA mismatch repair. MSI is characterized by frequent somatic mutations (i.e., cancer-specific mutations) that change the length of simple repeats (e.g., AAAAA…., GATAGATAGATA...). Clinical MSI tests evaluate the lengths of a handful of simple repeat sites, while next-generation sequencing can assay many more sites and offers a much more complete view of their somatic mutation frequencies. Using somatic mutation data from the exomes of a 361-tumor training set, we developed classifiers to determine MSI status based on four machine-learning frameworks. All frameworks had high accuracy and after choosing one we determined that it had >98% concordance with clinical tests in a separate 163-tumor test set. Furthermore, this classifier retained high concordance even when classifying tumors based on subsets of whole-exome data. We have released a CRAN R package, MSIseq, based on this classifier. MSIseq is faster and simpler to use than software that requires large files of aligned sequenced reads. MSIseq will be useful for genomic studies in which clinical MSI test results are unavailable and for detecting possible misclassifications by clinical tests.

This study aimed to analyze the association between elevated gamma‐glutamyl transpeptidase (GGT) and metabolic syndrome (MetS), hepatic steatosis, and fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). From August 2013 to August 2018, a community‐based study was conducted in the northeastern part of Taiwan. Patients who underwent abdominal ultrasonography (US) and had no history of alcoholic liver disease were included. According to a US examination showing fatty liver degree, 1566 patients with NAFLD were divided into four groups: normal GGT, isolated GGT elevation, isolated alanine aminotransferase (ALT) elevation, and both GGT and ALT elevation groups. Further 1147 participants with normal serum ALT, GGT, and the abdominal US were included as the control group. GGT levels were associated with high sensitivity C‐reactive protein, lower adiponectin, diabetes mellitus, and chronic kidney disease. A stepwise increase in odds ratio (OR) for MetS was found in the normal GGT group (OR = 1.71), isolated GGT elevation group (OR = 3.06), isolated ALT elevation (OR = 4.00), and both GGT + ALT elevation group (OR = 4.17) than the control group. Linear regression analysis revealed a positive association between GGT/ALT value and hepatic steatosis degree, GGT value, and degree of hepatic fibrosis. Hence, GGT elevation is associated with MetS, hepatic steatosis, and fibrosis in patients with NAFLD.

: Accurate lymph node (LN) status evaluation for intrahepatic cholangiocarcinoma (ICC) patients is essential for surgical planning. This study aimed to develop and validate a prediction model for preoperative LN status evaluation in ICC patients. : A group of 106 ICC patients, who were diagnosed between April 2011 and February 2016, was used for prediction model training. Image features were extracted from T1-weighted contrast-enhanced MR images. A support vector machine (SVM) model was built by using the most LN status-related features, which were selected using the maximum relevance minimum redundancy (mRMR) algorithm. The mRMR method ranked each feature according to its relevance to the LN status and redundancy with other features. An SVM score was calculated for each patient to reflect the LN metastasis (LNM) probability from the SVM model. Finally, a combination nomogram was constructed by incorporating the SVM score and clinical features. An independent group of 42 patients who were diagnosed from March 2016 to November 2017 was used to validate the prediction models. The model performances were evaluated on discrimination, calibration, and clinical utility. : The SVM model was constructed based on five selected image features. Significant differences were found between patients with LNM and non-LNM in SVM scores in both groups (the training group: 0.5466 (interquartile range (IQR), 0.4059-0.6985) vs. 0.3226 (IQR, 0.0527-0.4659), <0.0001; the validation group: 0.5831 (IQR, 0.3641-0.8162) vs. 0.3101 (IQR, 0.1029-0.4661), =0.0015). The combination nomogram based on the SVM score, the CA 19-9 level, and the MR-reported LNM factor showed better discrimination in separating patients with LNM and non-LNM, comparing to the SVM model alone (AUC: the training group: 0.842 vs. 0.788; the validation group: 0.870 vs. 0.787). Favorable clinical utility was observed using the decision curve analysis for the nomogram. : The nomogram, incorporating the SVM score, CA 19-9 level and the MR-reported LNM factor, provided an individualized LN status evaluation and helped clinicians guide the surgical decisions.

Objective To assess the diagnostic value and clinical significance of nucleoporin 107 (NUP107) in hepatocellular carcinoma (HCC), and explore the possible mechanisms. Methods The transcriptomic and clinical data of HCC patients were retrieved from The Cancer Genome Atlas (TCGA) and GEO databases. Tissue specimens were collected from HCC patients in the Guangxi area. According to the expression levels and prognostic characteristics of NUP107, ROC curves and nomogram models were constructed using the R package. Results NUP107 was highly expressed in 26 human cancers including HCC, and was associated with advanced HCC staging and worse prognosis. NUP107 showed satisfactory ability to predict the prognosis of HCC patients (AUC >0.8). Results of gene set enrichment analysis (GSEA) further showed that NUP107 was mainly associated with cell cycle‐related pathways such as the cell cycle, DNA replication, G2M checkpoint, E2F target, and mitotic spindle. In addition, NUP107 was also associated with immune infiltration in HCC and showed significant positive correlation with immune checkpoints (PD‐L1 and TIM‐3). NUP107 was highly expressed among 26 human cancers including HCC, and associated with advanced HCC staging and worse prognosis. Results of GESA further showed that NUP107 was mainly associated with cell cycle‐related pathways such as the cell cycle, DNA replication, G2M checkpoint, and E2F target along with mitotic spindle. In addition, NUP107 was also associated with immune infiltration in HCC and showed significant positive correlation with immune checkpoints (PD‐L1 and TIM‐3).

Biejiaxiaozheng (BJXZ) pills, a traditional Chinese medicine, have demonstrated anti-fibrotic effects; however, their mechanisms in treating liver fibrosis remain unclear. This study aimed to explore the active targets and underlying mechanisms of BJXZ pills using network pharmacology and experimental validation. Network pharmacology analysis identified 213 common drug-disease targets, 1131 Gene Ontology terms, and 163 signaling pathways (including the TNF pathway). Experimental results showed that BJXZ pills significantly suppressed LPS-induced viability increase in RAW264.7 macrophages by inhibiting NF-κB activation (p-P65), reducing INOS expression, and decreasing inflammatory cytokines. They also alleviated oxidative stress by upregulating Nrf2 and HO-1, restoring mitochondrial membrane potential, and enhancing ATP production. Additionally, BJXZ pills markedly inhibited TGF-β-induced activation of LX-2 hepatic stellate cells, reducing fibrotic markers like α-SMA. These findings suggest that BJXZ pills exert anti-fibrotic effects via multiple pathways, with the TNF pathway playing a key role. Mechanistically, the protective effects involve suppressing macrophage and hepatic stellate cell activation, highlighting BJXZ pills as a promising therapeutic option for liver fibrosis.

Background Cancer genomes are peppered with somatic mutations imprinted by different mutational processes. The mutational pattern of a cancer genome can be used to identify and understand the etiology of the underlying mutational processes. A plethora of prior research has focused on examining mutational signatures and mutational patterns from single base substitutions and their immediate sequencing context. We recently demonstrated that further classification of small mutational events (including substitutions, insertions, deletions, and doublet substitutions) can be used to provide a deeper understanding of the mutational processes that have molded a cancer genome. However, there has been no standard tool that allows fast, accurate, and comprehensive classification for all types of small mutational events. Results Here, we present SigProfilerMatrixGenerator, a computational tool designed for optimized exploration and visualization of mutational patterns for all types of small mutational events. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. SigProfilerMatrixGenerator produces fourteen distinct matrices by considering transcriptional strand bias of individual events and by incorporating distinct classifications for single base substitutions, doublet base substitutions, and small insertions and deletions. While the tool provides a comprehensive classification of mutations, SigProfilerMatrixGenerator is also faster and more memory efficient than existing tools that generate only a single matrix. Conclusions SigProfilerMatrixGenerator provides a standardized method for classifying small mutational events that is both efficient and scalable to large datasets. In addition to extending the classification of single base substitutions, the tool is the first to provide support for classifying doublet base substitutions and small insertions and deletions. SigProfilerMatrixGenerator is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/ .

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, with high mortality rates partially due to limited therapeutic options and drug resistance. Arsenic trioxide (ATO), a compound clinically proven for acute promyelocytic leukemia (APL), has garnered attention for its emerging efficacy in solid tumors, including HCC. However, the molecular mechanisms driving ATO's antitumor activity in HCC remain incompletely understood. In this study, we aimed to elucidate the ferroptosis-dependent effects of ATO on HCC and and propose a potential therapeutic strategy. The response of HCC cells to ATO was evaluated using cell viability, wound healing, colony formation, Transwell migration assays, and cell cycle analysis. ATO-induced ferroptosis was assessed by measuring lipid peroxidation (via C11-BODIPY staining), intracellular iron levels, and malondialdehyde (MDA) production. Western blotting was performed to quantify protein levels of NRF2, HO-1, SLC7A11, and GPX4; immunofluorescence staining was employed to determine NRF2 subcellular localization. ATO exhibited significant cytotoxicity and inhibited the progression of HCC cells. Treatment with ATO resulted in a notable increase in lipid ROS and MDA levels, which were subsequently reversed by the ferroptosis inhibitors Fer-1 and DFO. Mechanistically, ATO induced ferroptosis by inhibiting GPX4. Furthermore, NRF2 and its downstream targets, HO-1 and SLC7A11, were upregulated during ferroptosis. NRF2 knockdown enhanced lipid peroxidation and ATO-induced cell death. ATO significantly promoted ferroptosis in HCC cells, and NRF2 knockdown enhanced the cytotoxic effects of ATO.

Background The difficulty of assessment of neoadjuvant chemotherapeutic response preoperatively may hinder personalized-medicine strategies that depend on the results from pathological examination. Methods A total of 191 patients with high-grade osteosarcoma (HOS) were enrolled retrospectively from November 2013 to November 2017 and received neoadjuvant chemotherapy (NCT). A cutoff time of November 2016 was used to divide the training set and validation set. All patients underwent diagnostic CTs before and after chemotherapy. By quantifying the tumor regions on the CT images before and after NCT, 540 delta-radiomic features were calculated. The interclass correlation coefficients for segmentations of inter/intra-observers and feature pair-wise correlation coefficients (Pearson) were used for robust feature selection. A delta-radiomics signature was constructed using the lasso algorithm based on the training set. Radiomics signatures built from single-phase CT were constructed for comparison purpose. A radiomics nomogram was then developed from the multivariate logistic regression model by combining independent clinical factors and the delta-radiomics signature. The prediction performance was assessed using area under the ROC curve (AUC), calibration curves and decision curve analysis (DCA). Results The delta-radiomics signature showed higher AUC than single-CT based radiomics signatures in both training and validation cohorts. The delta-radiomics signature, consisting of 8 selected features, showed significant differences between the pathologic good response (pGR) (necrosis fraction ≥90%) group and the non-pGR (necrosis fraction < 90%) group ( P  < 0.0001, in both training and validation sets). The delta-radiomics nomogram, which consisted of the delta-radiomics signature and new pulmonary metastasis during chemotherapy showed good calibration and great discrimination capacity with AUC 0.871 (95% CI, 0.804 to 0.923) in the training cohort, and 0.843 (95% CI, 0.718 to 0.927) in the validation cohort. The DCA confirmed the clinical utility of the radiomics model. Conclusion The delta-radiomics nomogram incorporating the radiomics signature and clinical factors in this study could be used for individualized pathologic response evaluation after chemotherapy preoperatively and help tailor appropriate chemotherapy and further treatment plans.