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Background. The prognosis is poor for hepatocellular carcinoma (HCC), a tumor and cancer associated with inflammation that is common. New data showed that significant levels of KIAA1522 were expressed in HCC tissues and cell lines, suggesting that KIAA1522 may be a highly useful prognostic marker for HCC. However, its biochemical processes and impacts on the immune system go deeper. Objective. To verify the significance of KIAA1522 in HCC and investigate its related carcinogenic mechanisms. Methods. Studies examining the relationship between KIAA1522 expression and clinical-pathologic characteristics in HCC have been checked in the Cancer Genome Atlas (TCGA) database. A receiver operating characteristic (ROC) curve was used to assess the diagnostic efficacy of KIAA1522 in HCC. Western blot analysis was used to find the presence of the KIAA1522 protein in the tumor and paraneoplastic tissues of eight randomly chosen HCC patients. The GSVA program in R language was used to evaluate the relationship between KIAA1522 and immune cell infiltration in HCC. We searched the Search Tool for the Retrieval of Interacting Genes (STRING) database for interacting proteins connected to the expression of KIAA1522. Pathways were involved in the enrichment analysis of KIAA1522 to anticipate potential mechanisms through which KIAA1522 may affect immunological infiltration. Results. Our study found that KIAA1522 was commonly elevated in HCC tumor tissues and that it also signaled a bad outcome. We found an inverse link between KIAA1522 and cytotoxic cells and an inverse relationship between KIAA1522 and Th2 cell infiltration. In STRING analysis, the top 5 coexpressed proteins of KIAA1522 were BAIAP2, NCK2, TSNAXIP1, POGK, and KLHL31. The effect of KIAA1522 on HCC may entail cell cycle alteration, an immunological response, and suppression of the PPAR signaling pathway. Conclusion. High expression of KIAA1522 was linked to HCC immune cell infiltration, disease progression, and a poor prognosis.

Backgound. Multiple patients with hepatocellular carcinoma (HCC) received sorafenib therapy presented primary or secondary chemoresistance. The underlying mechanisms of chemoresistance are rather vague, ferroptosis is a new kind of cell death, and its discovery provides new opportunities to overcome sorafenib resistance. Therefore, it is essential to investigate the regulators of ferroptosis and implement a corresponding therapy strategy. Methods and Results. The levels of MCF2L were detected by PCR and western blotting assay. The effect of MCF2L on ferroptosis was confirmed by MTT, colony formation assay, Brdu, in vivo animal experiment, and the content of Iron, GSH, ROS, and MDA. The underlying mechanisms were explored by PCR, western blotting, and affinity precipitation assay. Our findings demonstrated that MCF2L is remarkedly upregulated in HCC tissues, and sorafenib can induce the levels of MCF2L, suggesting that MCF2L might function in sorafenib resistance of HCC. Further analysis showed that downregulation of MCF2L enhances HCC cell death induced by sorafenib, and ferroptosis inhibitor can reverse this process. Subsequent experiments showed that downregulation of MCF2L elevates the content of Iron, ROS, and MDA, which are all indicators of ferroptosis. Finally, mechanism analysis showed that MCF2L regulates the PI3K/AKT pathway in a RhoA/Rac1 dependent manner. Conclusions. Our study showed that targeting MCF2L may be a hopeful method to overcome sorafenib-resistance through inducing ferroptosis in HCC.

Background. Infantile Hemangiomas (IHs) are common benign vascular tumors of infancy that may have serious consequences. The research on diagnostic markers for IHs is scarce. Methods. The “limma” R package was applied to identify differentially expressed genes (DEGs) in developing IHs. Plugin ClueGO in Cytoscape software performed functional enrichment of DEGs. The Search Tool for Retrieving Interacting Genes (STRING) database was utilized to construct the PPI network. The least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were used to identify diagnostic genes for IHs. The receiver operating characteristic (ROC) curve evaluated diagnostic genes’ discriminatory ability. Single-gene based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted by Gene Set Enrichment Analysis (GSEA). The chemicals related to the diagnostic genes were excavated by the Comparative Toxicogenomics Database (CTD). Finally, the online website Network Analyst was used to predict the transcription factors targeting the diagnostic genes. Results. A total of 205 DEGs were singled out from IHs samples of 6-, 12-, and 24-month-old infants. These genes principally participated in vasculogenesis and development-related, endothelial cell-related biological processes. Then we mined 127 interacting proteins and created a network with 127 nodes and 251 edges. Furthermore, LASSO and SVM-RRF algorithms identified five diagnostic genes, namely, TMEM2, GUCY1A2, ISL1, WARS, and STEAP4. ROC curve analysis results indicated that the diagnostic genes had a powerful ability to distinguish IHs samples from normal samples. Next, the results of GSEA for a single gene illustrated that all five diagnostic genes inhibited the “valine, leucine, and isoleucine degradation” pathway in the development of IHs. WARS, TMEM2, and STEAP4 activated the “blood vessel development” and “vasculature development” in IHs. Subsequently, inhibitors targeting TMEM2, GUCY1A2, ISL1, and STEAP4 were mined. Finally, 14 transcription factors regulating GUCY1A2, 14 transcription factors regulating STEAP4, and 26 transcription factors regulating ISL1 were predicted. Conclusion. This study identified five diagnostic markers for IHs and further explored the mechanisms and targeting drugs, providing a basis for diagnosing and treating IHs.