Background: Hepatocellular carcinoma (HCC) is the leading cause of cancer death. Kinesin family member 2C (KIF2C) has been shown as oncogene in a variety of tumors. However, it's role in HCC remains unclear.Methods: In this study, the expression level of KIF2C in HCC was detected by immunohistochemical staining and RT-PCR, and verified by Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Oncomine database. A curve was established to evaluate the diagnostic efficiency of KIF2C. The effect of KIF2C on HCC was investigated by flow cytometry, Cell Counting Kit-8, Transwell, and the wound-healing assay. We explored the underlying mechanism through epithelial-to-mesenchymal transition (EMT) and transcriptome sequences analysis.Results: KIF2C was overexpression in HCC tissue and related to neoplasm histologic grade (P<0.001), pathology stage (P=0.001), and a dismal prognosis (overall, recurrence-free, and disease-free survival). The diagnostic efficacy of KIF2C was >90% in diagnosing HCC. The HCC cell function experiments showed that KIF2C promoted HCC cell proliferation, migration, invasion, and an accelerated cell cycle, and inhibited apoptosis. Based on western blot analysis and RT-PCR, we found that KIF2C promoted HCC invasion and metastasis through activation of the EMT. Based on transcriptome sequences, we showed that KIF2C promoted HCC through the Ras/MAPK and PI3K/Akt signaling pathway.Conclusions: KIF2C was found to promote the progression of HCC and is anticipated to serve as a biomarker for HCC diagnosis, prognosis, and targeted therapy.

Background: It has been reported that atractylodin has a potential antitumor effect. This study aimed to investigate the effects of atractylodin on Huh7 and Hccm hepatocellular carcinoma (HCC) cells and its molecular mechanism.Methods: Huh7 and Hccm cells were cultured in vitro, and their viability was detected by CCK-8 assay and the half inhibitory concentration (IC50) was calculated. The cells were treated with different concentrations of atractylodin, and the migration and invasion ability of cells was detected by scratch assay and Transwell assay. The cell cycle change and apoptosis rate were detected by flow cytometry. IlluminaHiSeq4000 platform was used for transcriptome sequencing, and the results were analyzed for gene differential expression, gene function, and signal pathway enrichment. Morphological changes of cells were detected by transmission electron microscopy, reactive oxygen species (ROS) levels were detected by DCFH-DA probe, and the expressions of ferroptosis related proteins GPX4, ACSL4, FTL, and TFR1 were detected by Western blot. Results:The results showed that atractylodin could inhibit the proliferation, migration, and invasion of Huh7 and Hccm cells, regulate the cell cycle, and induce cell apoptosis and G1 phase cell cycle arrest. In addition, it could significantly induce the increase of intracellular ROS levels, decrease the expression of GPX4 and FTL proteins, and up-regulate the expression of ACSL4 and TFR1 proteins.Conclusions: Atractylodin can inhibit the proliferation, migration, and invasion of Huh7 and Hccm liver cancer cells, and induce cell apoptosis and cell cycle arrest. In addition, our results suggest that atractylodin may induce ferroptosis in HCC cells by inhibiting the expression of GPX4 and FTL proteins, and upregulating the expression of ACSL4 and TFR1 proteins.

Background The protein high-mobility group AT-hook 1 (HMGA1) has been demonstrated that modulated cellular proliferation, invasion, and apoptosis with a poor prognosis in miscellaneous carcinomas. However, the mechanism of circumstantial carcinogenesis and association with the immune microenvironment of HMGA1 in hepatocellular carcinoma (HCC) had not been extensively explored. Methods The gene expression, clinicopathological correlation, and prognosis analysis were performed in the data obtained from TCGA. The results were further validated by ICGC and GEO database and external validation cohort from Guangxi. The HMGA1 protein expression was further examined in the HPA database. Biological function analyses were conducted by GSEA, STRING database, and Coexpedia online tool. Using TIMER and CIBERSORT method, the relationship between immune infiltrate and HMGA1 was investigated. Results In HCC, HMGA1 had much higher transcriptional and proteomic expression than in corresponding paraneoplastic tissue. Patients with high HMGA1 expression had a poor prognosis and unpromising clinicopathological features. High HMGA1 expression was closely related to the cell cycle, tumorigenesis, substance metabolism, and immune processes by regulating complex signaling pathways. Notably, HMGA1 may be associated with TP53 mutational carcinogenesis. Moreover, increased HMGA1 expression may lead to an increase in immune infiltration and a decrease in tumor purity in HCC. CIBERSORT analysis elucidated that the amount of B cell naive, B cell memory, T cells gamma delta, macrophages M2, and mast cell resting decreased when HMGA1 expression was high, whereas T cells follicular helper, macrophages M0, and Dendritic cells resting increased. Conclusion In conclusions, HMGA1 is a potent prognostic biomarker and a sign of immune infiltration in HCC, which may be a potential immunotherapy target for HCC.

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Background: The purpose of this study was to investigate the prognostic significance of like-Sm (LSM) genes in early pancreatic ductal adenocarcinoma (PDAC) and explore the potential molecular mechanism.The protein product of the LSM1 gene is also known as CASM and YJL124C, while that of the LSM4 gene is known as GRP and YER112W.Methods: Data from 112 patients attached to the Whipple surgery were collected from the TCGA database of clinical characteristics and survival data. The Kaplan-Meier method and the multivariate Cox proportional risk regression model were used to analyze the impact of LSM genes on outcomes in these 112 patients. We performed gene-gene interaction (GGI) and protein-protein interaction (PPI) analysis to probe interactions between LSM family genes. Bioinformatics techniques were applied to study the potential early-stage molecular mechanisms of LSM genes. Previously, only a few studies have explored the role and potential mechanisms of LSM1 in pancreatic tumor transformation, revealing possible links to transforming growth factor-β, altering the expression of MMP1, uPAR, and SerpinB5 to enhance invasion and metastasis in pancreatic cancer, and facilitating mRNA decapping and degradation. Gene set enrichment analysis (GSEA) also proved that LSM genes are associated with RNA splicing, RNA synthesis, and RNA decomposition, and they may indirectly cause carcinogenesis through other genes such as myc. Results:The results showed that LSM1 (adjusted P=0.004) and LSM4 (adjusted P=0.034) were associated with the prognosis of patients with PDAC, and patients with high expression levels of LSM1 (adjusted HR =2.338) or LSM4 (adjusted HR =1.803) tended to experience bad outcomes.Conclusions: Our study revealed that LSM1 and LSM4 might be used as prognostic biomarkers in early PDAC.

Background: The aim of this study was to determine the relationship between tumor mutation burden (TMB) and prognosis of patients with hepatocellular carcinoma (HCC), and to explore the differential expression of genes in HCC by TMB and the relationship between immune cells, TMB, and HCC.Methods: Somatic variation data, gene transcriptional expression data and clinical information of patients with HCC were obtained from cancer genome map (TCGA) database. Analyze the characteristics of the gene mutation data of the sample, divide the high and low TMB groups and draw the survival curve at the same time, carry on the difference analysis to the gene of TMB, further carry on the univariate Cox regression analysis and Lasso regression analysis and construct the clinical model. Download the dataset GSE14520, from the Gene Expression Omnibus (GEO) database to verify the genes of the prognostic model. The differential genes were analyzed by gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes by (KEGG) enrichment analysis. Then the relative abundance of 22 immune cell types in HCC and normal control samples was calculated. Finally, the correlation between the scores of immune cells and Risk model was analyzed.Results: Tumor protein p53 (TP53), catenin1 (CTNNB1), titin (TTN), mucin 16 (MUC16), and albumin (ALB) are the most common top 5 mutations in HCC. The prognosis of high level TMB group is worse than that of low TMB group. A total of 122 differentially expressed genes were screened by differential analysis of TMB genes. SQSTM1, ME1, BAMBI and PTTG1 are independent risk factors for poor prognosis of HCC.GO and KEGG analysis showed that the differential genes were mainly in extracellular matrix and immune response. There were significant differences in the distribution of Macrophages M0 and T cells CD4 native cells between HCC and normal tissues, which were correlated with the differential genes of TMB and correlated with prognosis.Conclusions: There is a negative correlation between TMB and the prognosis of patients with HCC.TMB has an effect on the differential expression of genes in HCC cells and the distribution of immune cells in tumor tissues.

Background: The solute carrier (SLC) 7 family genes play central roles in cancer cell metabolism as glucose and glutamate transporters. However, their expression and prognostic value in breast cancer (BC) remains to be elucidated.Methods: Clinical data from BC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Kaplan-Meier (KM) plotter database. The mechanisms underlying the association between SLC7A expression and overall survival (OS) were explored using Cox regression and log-rank tests. ESTIMATE gives a measure of the immune-cell infiltrates. Single-sample (ss) Gene Set Enrichment Analysis (GSEA) was conducted to quantify immune cell infiltration.Results: High SLC7A5 expression was associated with a poorer survival time in BC patients according to the TCGA and KM plotter data. SLC7A4 was associated with good progression-free interval (PFI) and disease-specific survival (DSS) according to the TCGA data. Furthermore, SLC7A4 was correlated with good prognosis of OS, distant metastasis-free survival (DMFS), relapse-free survival (RFS), and post-progression survival (PPS) according to the KM plotter data. SLC7A3 expression was positively associated with OS, but was not strongly associated with PFI nor DSS in the TCGA data. However, SLC7A3 was positively correlated with DMFS and RFS in the KM database analysis. SLC7A had excellent diagnostic value in BC patients and was strongly correlated with tumor infiltration. T helper 2 (Th2) cells, CD56 bright natural killer (NK) cells, and NK cells were the most strongly correlated with the SLC7A family genes, suggesting that these genes play a crucial role in BC partly by modulating immune infiltration.Conclusions: SLC7A4 and SLC7A5 expression levels may be sensitive biomarkers for predicting BC outcomes. SLC7A3 may be a potential diagnostic and prognostic biomarker in BC, but further studies are warranted to verify these results.