High expression levels of CLEC4M indicate poor prognosis in patients with hepatocellular carcinoma

Levels of APE1 Repair Gene and CLEC4M in Lung Cancer Patients Receiving Cisplatin Chemotherapy

This study aimed to detect the levels of apurinic/apyrimidinic endonuclease 1 (APE1) gene expression and C-type lectin domain family 4 member M (CLEC4M) and their association with cisplatin chemotherapy in lung cancer patients. Overall, 105 individuals who attended the Al-Amal National Hospital for Cancer Management, Baghdad, Iraq, were enrolled in the study and divided into three equal groups. The groups included the patients newly diagnosed with lung cancer, cancer patients who received cisplatin, and the healthy control group. All study groups were subjected to the sampling of the venous blood for molecular analysis by real-time polymerase chain reaction (RT-PCR) to detect the APE1 gene and enzyme-linked immunosorbent assay (ELISA) for serological testing to measure the concentration of CLEC4M protein. Significantly, the values of both cancer groups were higher than those reported in the control group. The relative index revealed a significant difference in the mean fold change level of APE1 in the newly diagnosed group (3 fold) and cisplatin therapy patients group (2 fold), compared to the control group (P=0.005). No significant differences were detected between the two cancer groups in terms of fold change mean of expression, demographic characteristics, and cancer histological type. Regarding human CLEC4M protein level, cases receiving cisplatin (139.2±25.9) and newly diagnosed patients (331.0±38.1) had a highly significant difference with the control group (100.3±47.5, P<0.001). There was no significant difference between the concentration level of CLEC4M and all parameters in demographic characteristics and cancer histological type. This was the first study to demonstrate that higher expression levels of new APE1, CLEC4M, and glutathione, especially after chemotherapy, are beneficial as diagnostic and prognostic markers for resistance to platinum chemotherapy in Iraqi lung cancer patients.

The XPA Protein-Life under Precise Control

Nucleotide excision repair (NER) is a central DNA repair pathway responsible for removing a wide variety of DNA-distorting lesions from the genome. The highly choreographed cascade of core NER reactions requires more than 30 polypeptides. The xeroderma pigmentosum group A (XPA) protein plays an essential role in the NER process. XPA interacts with almost all NER participants and organizes the correct NER repair complex. In the absence of XPA's scaffolding function, no repair process occurs. In this review, we briefly summarize our current knowledge about the XPA protein structure and analyze the formation of contact with its protein partners during NER complex assembling. We focus on different ways of regulation of the XPA protein's activity and expression and pay special attention to the network of post-translational modifications. We also discuss the data that is not in line with the currently accepted hypothesis about the functioning of the XPA protein.

Ficolin-2: A potential immune-related therapeutic target with low expression in liver cancer

Objective

This study aimed to investigate the role of ficolin-2 (FCN2) in the development and course of hepatocellular carcinoma (HCC) and to contribute to the evolution of innovative HCC therapeutics.

Methods

Oncomine, GEPIA (Gene Expression Profiling Interactive Analysis), TISIDB (Tumor Immune System Interactions and Drug Bank database), UALCAN (University of Alabama at Birmingham Cancer data analysis portal), UCSC (University of California, Santa Cruz), R package, the Kaplan–Meier technique, Cox regression analysis, LinkedOmics, Pearson’s correlation, and a nomogram were used to investigate the prognostic value of FCN2 in HCC. Co-expressed genes were screened. A protein–protein interaction network was created using the STRING database. Finally, immunohistochemistry was performed to establish the expression of FCN2 in HCC tissues. A pan-cancer study centered on HCC-related molecular analysis was also conducted to look for a link between FCN2 and immune infiltration, immune modulators, and chemokine receptors.

Results

In HCC tissues, the expression of FCN2 was observed to be lower than that in normal tissues. This was connected to the HCC marker alpha-fetoprotein, showing that FCN2 is involved in the development and progression of cancer. FCN2 may act through Staphylococcus aureus infection, lectins, and other pathways. Furthermore, at the immune level, the expression of FCN2 in HCC was associated with some immune cell infiltration, immunomodulators, and chemokine receptors.

Conclusion

FCN2 may be an immune checkpoint inhibitor for HCC, creating a breakthrough in the treatment of HCC.

Multi-omics data integration for hepatocellular carcinoma subtyping with multi-kernel learning

Hepatocellular carcinoma (HCC) is a leading malignant liver tumor with high mortality and morbidity. Patients at the same stage can be defined as different molecular subtypes associated with specific genomic disorders and clinical features. Thus, identifying subtypes is essential to realize efficient treatment and improve survival outcomes of HCC patients. Here, we applied a regularized multiple kernel learning with locality preserving projections method to integrate mRNA, miRNA and DNA methylation data of HCC patients to identify subtypes. We identified two HCC subtypes significantly correlated with the overall survival. The patient 3-years mortality rates in the high-risk and low-risk group was 51.0% and 23.5%, respectively. The high-risk group HCC patients were 3.37 times higher in death risk compared to the low-risk group after adjusting for clinically relevant covariates. A total of 196 differentially expressed mRNAs, 2,151 differentially methylated genes and 58 differentially expressed miRNAs were identified between the two subtypes. Additionally, pathway activity analysis showed that the activities of six pathways between the two subtypes were significantly different. Immune cell infiltration analysis revealed that the abundance of nine immune cells differed significantly between the two subtypes. We further applied the weighted gene co-expression network analysis to identify gene modules that may affect patients prognosis. Among the identified modules, the key module genes significantly associated with prognosis were found to be involved in multiple biological processes and pathways, revealing the mechanism underlying the progression of HCC. Hub gene analysis showed that the expression levels of CDK1, CDCA8, TACC3, and NCAPG were significantly associated with HCC prognosis. Our findings may bring novel insights into the subtypes of HCC and promote the realization of precision medicine.

Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis

As liver hepatocellular carcinoma (LIHC) has high morbidity and mortality rates, improving the clinical diagnosis and treatment of LIHC is an important issue. The advent of the era of precision medicine provides us with new opportunities to cure cancers, including the accumulation of multi-omics data of cancers. Here, we proposed an integration method that involved the Fisher ratio, Spearman correlation coefficient, classified information index, and an ensemble of decision trees (DTs) for biomarker identification based on an unbalanced dataset of LIHC. Then, we obtained 34 differentially expressed genes (DEGs). The ability of the 34 DEGs to discriminate tumor samples from normal samples was evaluated by classification, and a high area under the curve (AUC) was achieved in our studied dataset and in two external validation datasets (AUC = 0.997, 0.973, and 0.949, respectively). Additionally, we also found three subtypes of LIHC, and revealed different biological mechanisms behind the three subtypes. Mutation enrichment analysis showed that subtype 3 had many enriched mutations, including tumor protein p53 (TP53) mutations. Overall, our study suggested that the 34 DEGs could serve as diagnostic biomarkers, and the three subtypes could help with precise treatment for LIHC.