Recent studies have hinted at a potential link between Alzheimer\'s Disease (AD) and cancer. Thus, our study focused on finding genes common to AD and Liver Hepatocellular Carcinoma (LIHC), assessing their promise as diagnostic indicators and guiding future treatment approaches for both conditions. Our research utilized a broad methodology, including differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), gene enrichment analysis, Receiver Operating Characteristic (ROC) curves, and Kaplan-Meier plots, supplemented with immunohistochemistry data from the Human Protein Atlas (HPA) and machine learning techniques, to identify critical genes and significant pathways shared between AD and LIHC. Through differential gene expression analysis, WGCNA, and machine learning methods, we identified nine key genes associated with AD, which served as entry points for LIHC analysis. Subsequent analyses revealed IKBKE and HSPA1A as shared pivotal genes in patients with AD and LIHC, suggesting these genes as potential targets for intervention in both conditions. Our study indicates that IKBKE and HSPA1A could influence the onset and progression of AD and LIHC by modulating the infiltration levels of immune cells. This lays a foundation for future research into targeted therapies based on their shared mechanisms.

BACKGROUND: Proteasome assembly chaperone 3 (PSMG3), a subunit of proteasome, has been found to be associated with lung cancer. However, the role of PSMG3 in other cancers has not been elucidated. The objective of this study was to explore the immune role of PSMG3 in pan-cancer and confirm the oncogenic significance in liver hepatocellular carcinoma (LIHC).
METHODS: We examined the differential expression of PSMG3 across various cancer types using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. We investigated the prognostic value of PSMG3 and examined its relationship with tumor mutation burden (TMB), microsatellite instability (MSI), and immune infiltration. The functional enrichment analysis was performed to explore the potential molecular mechanism of PSMG3. To elucidate the biological function of PSMG3, we conducted in vitro experiments using liver cancer cell lines.
RESULTS: PSMG3 was highly expressed in most cancers. The high PSMG3 expression value of PSMG3 was closely related to poor prognosis. We observed correlations between PSMG3 and TMB, and MSI immune infiltration. PSMG3 may be involved in metabolic reprogramming, cell cycle, and PPAR pathways. The over-expression of PSMG3 promoted the proliferation, migration, and invasion capabilities of liver cancer cells.
CONCLUSIONS: Our study demonstrated that PSMG3 was a pivotal oncogene in multiple cancers. PSMG3 contributed to the progression and immune infiltration in pan-cancer, especially in LIHC.

UNASSIGNED: Liver hepatocellular carcinoma (LIHC) is the most common type of liver cancer, but there is a lack of effective indicators for its early diagnosis and prognosis, so we explored the role of KEAP1 in LIHC patients in this study.
UNASSIGNED: The Cancer Genome Atlas (TCGA) dataset was used to investigate the relationship between KEAP1 expression and clinicopathological features and prognosis of LIHC patients. KEAP1 expression related pathways were enriched by Gene Ontology (GO) and gene set enrichment analysis (GSEA). Besides, KEAP1 expression-related immune infiltration was performed by single-sample GSEA (ssGSEA), and function of immune cells was detected by flow cytometry.
UNASSIGNED: It was found that KEAP1 expression was significantly increased and correlated with overall survival of LIHC patients. A total of 231 differentially expressed genes (DEGs) between LIHC patients with high- and low-KEAP1 expression were found, which associated with various biological pathways. Besides, KEAP1 expression was positively correlated with the infiltration level of T helper cells and Th2 cells but negatively correlated with DCs and cytotoxic cells. Functional analysis revealed that the expression of IL 4 in Th2 cells and CD107a, GrA and GrB in cytotoxic cells was significantly greater in LIHC patients than in HCs. In addition, KEAP1 expression was closely correlated with liver function in LIHC patients.
UNASSIGNED: Highly expressed KEAP1 was closely related to the diagnosis, prognosis, immune cell infiltration, and liver function of LIHC, which might promote the progression of LIHC through regulating cell development, signal transduction, and abnormal immune response. The current study partially revealed the role of KEAP1 in LIHC and provided a potential biomarker for the diagnosis, prognosis and treatment of LIHC.

OBJECTIVE: Polyunsaturated fatty acids (PUFAs) have attracted increasing attention for their role in liver cancer development. The objective of this study is to develop a prognosis prediction model for patients with liver cancer based on PUFA-related metabolic gene characteristics.
METHODS: Transcriptome data and clinical data were obtained from public databases, while gene sets related to PUFAs were acquired from the gene set enrichment analysis (GSEA) database. Univariate Cox analysis was conducted on the training set, followed by LASSO logistic regression and multivariate Cox analysis on genes with p < .05. Subsequently, the stepwise Akaike information criterion method was employed to construct the model. The high- and low-risk groups were divided based on the median score, and the model\'s survival prediction ability, diagnostic efficiency, and risk score distribution of clinical features were validated. The above procedures were also validated in the validation set. Immune infiltration levels were evaluated using four algorithms, and the immunotherapeutic potential of different groups was explored. Significant enrichment pathways among different groups were selected based on the GSEA algorithm, and mutation analyses were conducted. Nomogram prognostic models were constructed by incorporating clinical factors and risk scores using univariate and multivariate Cox regression analysis, validated through calibration curves and clinical decision curves. Additionally, sensitivity analysis of drugs was performed to screen potential targeted drugs.
RESULTS: We constructed a prognostic model comprising eight genes (PLA2G12A, CYP2C8, ABCCI, CD74, CCR7, P2RY4, P2RY6, and YY1). Validation across multiple datasets indicated the model\'s favorable prognostic prediction ability and diagnostic efficiency, with poorer grading and staging observed in the high-risk group. Variations in mutation status and pathway enrichment were noted among different groups. Incorporating Stage, Grade, T.Stage, and RiskScore into the nomogram prognostic model demonstrated good accuracy and clinical decision benefits. Multiple immune analyses suggested greater benefits from immunotherapy in the low-risk group. We predicted multiple targeted drugs, providing a basis for drug development.
CONCLUSIONS: Our study\'s multifactorial prognostic model across multiple datasets demonstrates good applicability, offering a reliable tool for personalized therapy. Immunological and mutation-related analyses provide theoretical foundations for further research. Drug predictions offer important insights for future drug development and treatment strategies. Overall, this study provides comprehensive insights into tumor prognosis assessment and personalized treatment planning.

Coiled-coil domain-containing 58 (CCDC58) is a member of the CCDC protein family. Similar to other members, CCDC58 exhibits potential tumorigenic roles in a variety of malignancies. However, there is no systematic and comprehensive pan-cancer analysis to investigate the diagnosis, prognosis, immune infiltration, and other related functions of CCDC58. We used several online websites and databases, such as TCGA, GTEx, UALCAN, HPA, CancerSEA, BioGRID, GEPIA 2.0, TIMER 2.0, and TISIDB, to extract CCDC58 expression data and clinical data of patients in pan-cancer. Then, the relationship between CCDC58 expression and diagnosis, prognosis, genetic alterations, DNA methylation, genomic heterogeneity, and immune infiltration level were determined. In addition, the biological function of CCDC58 in liver hepatocellular carcinoma (LIHC) was investigated. Pan-cancer analysis results showed that CCDC58 was differentially expressed in most tumors and showed excellent performance in diagnosis and prediction of prognosis. The expression of CCDC58 was highly correlated with genetic alterations, DNA methylation, and genomic heterogeneity in some tumors. In addition, the correlation analysis of CCDC58 with the level of immune infiltration and immune checkpoint marker genes indicated that CCDC58 might affect the composition of the tumor immune microenvironment. Enrichment analysis showed that CCDC58-related genes were mainly linked to mitosis, chromosome, and cell cycle. Finally, biological function experiments demonstrated that CCDC58 plays an important role in tumor cell proliferation and migration. CCDC58 was first identified as a pan-cancer biomarker. It may be used as a potential therapeutic target to improve the prognosis of patients in the future.

BAZ2A, an epigenetic regulatory factor that affects ribosomal RNA transcription, has been shown to be highly expressed in several cancers and promotes tumor cell migration. This study explored the expression and mechanism of BAZ2A in tumorigenesis at the pan-cancer level. The Cancer Genome Atlas, Gene Expression Omnibus databases and TIMER2.0, cBioPortal and other tools were used to analyze the level of expression of BAZ2A in various tumor tissues and to examine the relationship between BAZ2A and survival, prognosis, mutation and immune invasion. In vitro experiments were performed to assess the function of BAZ2A in cancer cells. Using combined transcriptome and proteome analysis, we examined the possible mechanism of BAZ2A in tumors. BAZ2A exhibited high expression levels in multiple tumor tissues and displayed a significant association with cancer patient prognosis. The main type of BAZ2A genetic variation in cancer is gene mutation. Downregulation of BAZ2A inhibited proliferation, migration, and invasion and promoted apoptosis in LM6 liver cancer cell. The mechanism of BAZ2A in cancer development may involve lipid metabolism. These results help expand our understanding of BAZ2A in tumorigenesis and development and suggest BAZ2A may serve as a prognostic and diagnostic factor in several cancers.

PSME3 plays a significant role in tumor progression. However, the prognostic value of PSME3 in pan-cancer and its involvement in tumor immunity remain unclear. We conducted a comprehensive study utilizing extensive RNA sequencing data from the TCGA (The Cancer Genome Atlas) and GTEx (Genotype-Tissue Expression) databases. Our research revealed abnormal expression levels of PSME3 in various cancer types and unveiled a correlation between high PSME3 expression and adverse clinical outcomes, especially in cancers like liver cancer (LIHC) and lung adenocarcinoma (LUAD). Functional enrichment analysis highlighted multiple biological functions of PSME3, including its involvement in protein degradation, immune responses, and stem cell regulation. Moreover, PSME3 showed associations with immune infiltration and immune cells in the tumor microenvironment, indicating its potential role in shaping the cancer immune landscape. The study also unveiled connections between PSME3 and immune checkpoint expression, with experimental validation demonstrating that PSME3 positively regulates CD276. This suggests that PSME3 could be a potential therapeutic target in immunotherapy. Additionally, we predicted sensitive drugs targeting PSME3. Finally, we confirmed in both single-factor Cox and multiple-factor Cox regression analyses that PSME3 is an independent prognostic factor. We also conducted preliminary validations of the impact of PSME3 on cell proliferation and wound healing in liver cancer. In summary, our study reveals the multifaceted role of PSME3 in cancer biology, immune regulation, and clinical outcomes, providing crucial insights for personalized cancer treatment strategies and the development of immunotherapy.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a serious liver disease worldwide, and its pathogenesis is complicated.
OBJECTIVE: This study investigated the potential role of FANCA in the advancement and prognosis of LIHC.
METHODS: Public databases, quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blot (WB) and immunohistochemistry (IHC) were employed to measure FANCA expression between tumor and normal samples. The relationship between FANCA expression and prognosis of LIHC patients were examined. Functional enrichment of FANCA-related genes was performed. Furthermore, univariate and multivariate analyses were conducted to determine the independent prognosis value of FANCA in LIHC. Finally, influence of FANCA knockout on the proliferation, migration, and invasion of HepG2 cell was validated with cloning formation, CCK8, and Transwell assays.
RESULTS: Expression analysis presented that FANCA had high expression level in LIHC tissues and cells. Receiver operating characteristic (ROC) curve analysis showed that FANCA was of great diagnosis value in LIHC. Clinicopathological analysis revealed that FANCA was significantly greater expressed in the advanced stage than in the early stage of LIHC. Univariate, multivariate, and Kaplan-Meier survival analysis confirmed that high expression of FANCA was strongly associated with poor survival of LIHC patients. In addition, high level of FANCA in LIHC showed a negative association with immunoinfiltrated B cells, T cells, and stromal scores. Moreover, Knockout of FANCA significantly inhibited HepG2 cell proliferative activity, migration, and invasion ability.
CONCLUSIONS: Our data revealed that high level of FANCA was closely associated with LIHC malignant progression, suggesting its potential utility as a diagnostic, predictive indicator, and therapeutic target.

This study aims to investigate the impact of semaglutide on the expression of liver cancer proteins in obese mice induced by a high-fat diet. Sixteen obese mice were randomly divided into two groups: the high-fat diet group and the semaglutide group, each consisting of eight mice. Additionally, eight normal male mice were included as the control group. Serum samples were collected, and a differential expression analysis of total proteins in adipose tissue was performed using quantitative tandem mass spectrometry (TMT) in combination with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Significant differential proteins were identified and subjected to a bioinformatics analysis. The findings revealed that these differential proteins, namely, integrin αV (ITGAV), laminin γ1 (LAMC1), fatty acid-binding protein 5 (FABP5), and lipoprotein lipase (LPL), regulate the occurrence and development of liver cancer by participating in the extracellular matrix (ECM) signaling pathway and the peroxisome proliferator-activated receptor (PPAR) signaling pathway. Notably, semaglutide can decelerate the progression of liver cancer by inducing the expression of ITGAV, LAMC1, FABP5, and LPL in the adipose tissue of obese mice.

UNASSIGNED: Hepatocellular carcinoma (HCC) being a complex disease, commonly exhibits multifaceted presentations, rendering its treatment challenging and necessitating specific approaches. The tumor immune microenvironment is crucial in cancer treatment, and cholesterol metabolism is a key component that helps cells grow and produce vital metabolites. However, the reprogramming of cholesterol metabolism in the tumor microenvironment (TME) can promote HCC development, and cancer classifiers relating to cholesterol metabolism are currently limited. Despite significant progress, further research is needed to improve early detection, liver function, and treatment options to improve patient outcomes.
UNASSIGNED: To evaluate the expression abundance of tumor immune microenvironment (TIME) and cholesterol metabolism in 8 types of liver cancer cells, we comprehensively evaluated the immune cell composition, extracellular matrix alterations, and activity of relevant signaling pathways in the TIME through nine liver cancer patients, stromal scoring, immune scoring, tumor purity scoring, immune infiltration analysis, and pathway enrichment. Subsequently, we utilized machine learning techniques to construct prognostic models for both cholesterol metabolism and the tumor immune microenvironment, further exploring the tumor mutation burden, immune infiltration levels, and drug sensitivity in different subtypes of HCC patients.
UNASSIGNED: Our study constructed three cancer screening models to identify HCC patients with high cholesterol metabolism and low TIME, who have a poorer prognosis. On the contrary, patients with low cholesterol metabolism and high TIME often have better prognosis. Furthermore, we identified chemical compounds, such as BPD-00008900, ML323, Doramapimod, and AZD2014, which display better chemotherapy results for high-risk patients in specific sub-groups.