Comprehensive analysis of KLF2 as a prognostic biomarker associated with fibrosis and immune infiltration in advanced hepatocellular carcinoma

Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer's disease: transcriptomics combined with machine learning analysis

JOURNAL/nrgr/04.03/01300535-202512000-00030/figure1/v/2025-01-31T122243Z/r/image-tiff Studies have shown that vascular dysfunction is closely related to the pathogenesis of Alzheimer's disease. The middle temporal gyrus region of the brain is susceptible to pronounced impairment in Alzheimer's disease. Identification of the molecules involved in vascular aberrance of the middle temporal gyrus would support elucidation of the mechanisms underlying Alzheimer's disease and discovery of novel targets for intervention. We carried out single-cell transcriptomic analysis of the middle temporal gyrus in the brains of patients with Alzheimer's disease and healthy controls, revealing obvious changes in vascular function. CellChat analysis of intercellular communication in the middle temporal gyrus showed that the number of cell interactions in this region was decreased in Alzheimer's disease patients, with altered intercellular communication of endothelial cells and pericytes being the most prominent. Differentially expressed genes were also identified. Using the CellChat results, AUCell evaluation of the pathway activity of specific cells showed that the obvious changes in vascular function in the middle temporal gyrus in Alzheimer's disease were directly related to changes in the vascular endothelial growth factor (VEGF)A-VEGF receptor (VEGFR) 2 pathway. AUCell analysis identified subtypes of endothelial cells and pericytes directly related to VEGFA-VEGFR2 pathway activity. Two subtypes of middle temporal gyrus cells showed significant alteration in AD: endothelial cells with high expression of Erb-B2 receptor tyrosine kinase 4 (ERBB4 high ) and pericytes with high expression of angiopoietin-like 4 (ANGPTL4 high ). Finally, combining bulk RNA sequencing data and two machine learning algorithms (least absolute shrinkage and selection operator and random forest), four characteristic Alzheimer's disease feature genes were identified: somatostatin ( SST ), protein tyrosine phosphatase non-receptor type 3 ( PTPN3 ), glutinase ( GL3 ), and tropomyosin 3 ( PTM3 ). These genes were downregulated in the middle temporal gyrus of patients with Alzheimer's disease and may be used to target the VEGF pathway. Alzheimer's disease mouse models demonstrated consistent altered expression of these genes in the middle temporal gyrus. In conclusion, this study detected changes in intercellular communication between endothelial cells and pericytes in the middle temporal gyrus and identified four novel feature genes related to middle temporal gyrus and vascular functioning in patients with Alzheimer's disease. These findings contribute to a deeper understanding of the molecular mechanisms underlying Alzheimer's disease and present novel treatment targets.

Cancer-associated fibroblasts in hepatocellular carcinoma: heterogeneity, mechanisms and therapeutic targets

Hepatocellular carcinoma (HCC) is one of the common malignant cancers worldwide. Although immunotherapy has improved the treatment outcome in HCC, a significant percentage of patients with advanced HCC still cannot benefit from immunotherapy. Therefore, developing new targets or combination therapeutic strategies to improve the efficacy of immunotherapy is urgently needed. A deeper understanding of the mechanisms underlying immune regulation may help in this regard. The tumor microenvironment (TME) consists of a diverse set of components modulating the efficacy of immunotherapy. Cancer-associated fibroblasts (CAFs) are critical components of the TME and can regulate both tumor and immune cells through secreted cytokines and exosomes that impact various signaling pathways in target cells. CAF-derived cytokines can also participate in extracellular matrix (ECM) remodeling, thereby impacting cancer progression and tumor responsiveness to immunotherapy among other effects. A thorough understanding of the phenotypic and functional profile dynamism of CAFs may lead the way for new treatment strategies and/or better treatment outcomes in HCC patients. In this review, we outline the biomarkers and functional heterogeneity of CAFs in HCC and elaborate on molecular mechanisms of CAFs, including anti-programmed cell death protein 1 (PD-1)/PD-ligand 1 (PD-L1) immunotherapy. We also examine current clinical implications of CAFs-related targets as potential therapeutic candidates in HCC.

Sophisticated roles of tumor microenvironment in resistance to immune checkpoint blockade therapy in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a serious threat to global health, with rising incidence and mortality rates. Therapeutic options for advanced HCC are quite limited, and the overall prognosis remains poor. Recent advancements in immunotherapy, particularly immune-checkpoint blockade (ICB) targeting anti-PD1/PD-L1 and anti-CTLA4, have facilitated a paradigm shift in cancer treatment, demonstrating substantial survival benefits across various cancer types, including HCC. However, only a subset of HCC patients exhibit a favorable response to ICB therapy, and its efficacy is often hindered by the development of resistance. There are many studies to explore the underlying mechanisms of ICB response. In this review, we compiled the latest progression in immunotherapies for HCC and systematically summarized the sophisticated mechanisms by which components of the tumor microenvironment (TME) regulate resistance to ICB therapy. Additionally, we also outlined some scientific rationale strategies to boost antitumor immunity and enhance the efficacy of ICB in HCC. These insights may serve as a roadmap for future research and help improve outcomes for HCC patients.

Integrating T-cell inflammation features for prognosis in hepatocellular carcinoma: a novel predictive model

Background

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related death globally and accounts for 75% to 90% of primary liver cancer cases. The high mortality rate of HCC, coupled with the absence of reliable prognostic biomarkers, makes its treatment and prognosis evaluation challenging. The features of the T cell-inflamed microenvironment include active interferon (IFN)-γ signaling and the presence of cytotoxic effector molecules, antigen presentation, and T-cell activating cytokines. Although these features are closely associated with anticancer immunity, their specific roles in HCC remain unclear. This study aimed to investigate the role and prognostic significance of T-cell inflammation (TCI) in HCC patients, providing new insights for clinical diagnosis and treatment strategies.

Methods

We integrated single-sample gene set enrichment analysis (ssGSEA) and weighted gene coexpression network analysis (WGCNA) to identify the genes associated with TCI at both the single-cell and bulk-transcriptome levels. The HCC TCI-related score (HTCIRS) was developed and assessed with 10 different machine learning algorithms and their combinations, which was followed by validation of the key gene KLF2 in clinical samples and tissue microarrays (TMAs).

Results

We identified 65 genes associated with TCI, of which 36 were significantly correlated with overall survival (OS). The HTCIRS demonstrated excellent performance in prognostic prediction, revealing differences in biological functions and immune cell infiltration between different risk groups within the tumor microenvironment (TME). Furthermore, KLF2 was identified to be linked to the prognosis of patients with HCC.

Conclusions

The TCI-related score proposed in this study serves as an important tool for prognostic prediction and personalized treatment of patients with HCC, with KLF2 emerging as a potential biomarker for predicting the prognosis of patients with HCC.

TMSB4X is a regulator of inflammation-associated ferroptosis, and promotes the proliferation, migration and invasion of hepatocellular carcinoma cells

BACKGROUND

Ferroptosis and inflammation are involved in cancer progression. The aim of this study was to identify inflammation-associated ferroptosis regulators in hepatocellular carcinoma (HCC).

METHODS

FerrDb database was searched for ferroptosis-related genes. RNA sequencing data and clinicopathologic information of HCC patients were downloaded from the Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis was applied to obtain the genes probably involved in inflammation-associated ferroptosis. Univariate Cox regression analysis was conducted to screen prognostic genes, and 10 machine learning algorithms were combined to find the optimal strategy to evaluate the prognosis of the patients based on the prognosis-related genes. The patients were divided into high risk group and low risk group, and the differentially expressed genes were obtained. Thymosin beta 4 X-linked (TMSB4X) was overexpressed or knocked down in HCC cell lines, and then qPCR, CCK-8, Transwell, flow cytometery assays were performed to detect the change of HCC cells' phenotypes, and Western blot was used to detect the change of ferroptosis markers.

RESULTS

157 genes related to inflammation and ferroptosis in HCC were obtained by WGCNA. rLasso algorithm, with the highest C-index, screened out 29 hub genes, and this model showed good efficacy to predict the prognosis of HCC patients. The patients in high risk group and low risk groups showed distinct molecular characteristics. TMSB4X was the most important gene which dominated the classification, and it was highly expressed in HCC samples. TMSB4X promoted the viability, migration and invasion, and repressed ferroptosis of HCC cells.

CONCLUSION

The risk model constructed based on the inflammation-associated ferroptosis regulators is effective to predict the clinical outcome of HCC patients. TMSB4X, involved in inflammation-associated ferroptosis, is a potential biomarker and therapeutic target for HCC.

Comprehensive Pan-Cancer Analysis of the Prognostic Role of KLF Transcription Factor 2 (KLF2) in Human Tumors

Background

KLF2 is a transcription factor expressed early in mammalian development that plays a role in many processes of development and disease. Recently, increasing studies revealed that KLF2 plays a key role in the occurrence and progression of cancer.

Purpose

The aim of this study was to explore the role of KLF2 in various tumor types using the Cancer Genome Atlas dataset.

Methods

Here, we set out to explore the role of KLF2 in 33 tumor types using TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus) dataset, Human Protein Atlas (HPA), UALCAN database, CancerSEA, GSCALite and several bioinformatic tools. Furthermore, we also performed immunohistochemistry and qPCR to further validate the role of KLF2 in multiple cancers and its correlation with prognosis.

Results

We found that KLF2 was underexpressed in most tumors and generally predicted poor OS in tumor patients. We found that amplification of KLF2 may be a risk factor for patients with OV (Ovarian serous cystadenocarcinoma). We also analyzed the abundance of checkpoints and markers of specific immune subsets including CD8+ T lymphocytes (T cells), CD4+ T cells, macrophages, and endothelial cells that significantly correlated with the expression level of KLF2 in pan-carcinoma tissues. In some cancers, KLF2 expression levels are positively correlated with gene promoter DNA methylation and drug sensitivity. In addition, we found that KLF2 is involved in single-cell level cell invasion in some cancers. In addition, KLF2 is co-expressed with several intracellular signal transduction genes involved in immune system processes. Immunohistochemistry and qPCR confirmed the low expression of KLF2 in STAD (stomach adenocarcinoma) and renal cancer.

Conclusion

Our pan-cancer analysis provides a comprehensive overview of the oncogenic roles of KLF2 in multiple human cancers and can be regarded as a potential prognostic marker and a novel target for cancer immunotherapy.

Prediction of CAF-related genes in immunotherapy and drug sensitivity in hepatocellular carcinoma: a multi-database analysis

This study aims to identify the cancer-associated fibroblasts (CAF)-related genes that can affect immunotherapy and drug sensitivity in hepatocellular carcinoma (HCC). Expression data and survival data associated with HCC were obtained in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted correlation network analysis (WGCNA) analysis was performed to obtain CAF-related genes. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for regression analysis and risk models. Subsequently, Gene Set Enrichment Analysis (GSEA) analysis, Gene Set Enrichment Analysis (ssGSEA) analysis, Tumor Immune Dysfunction and Exclusion (TIDE) analysis and drug sensitivity analysis were performed on the risk models. Survival analysis of CAF scores showed that the survival rate was lower in samples with high CAF scores than those with low scores. However, this difference was not significant, suggesting CAF may not directly influence the prognosis of HCC patients. Further screening of CAF-related genes yielded 33 CAF-related genes. Seven risk models constructed based on CDR2L, SPRED1, PFKP, ENG, KLF2, FSCN1 and VCAN, showed significant differences in immunotherapy and partial drug sensitivity in HCC. Seven CAF-related genes may have important roles in immunotherapy, drug sensitivity and prognostic survival in HCC patients.

IHGA: An interactive web server for large-scale and comprehensive discovery of genes of interest in hepatocellular carcinoma

Mining gene expression data is valuable for discovering novel biomarkers and therapeutic targets in hepatocellular carcinoma (HCC). Although emerging data mining tools are available for pan-cancer-related gene data analysis, few tools are dedicated to HCC. Moreover, tools specifically designed for HCC have restrictions such as small data scale and limited functionality. Therefore, we developed IHGA, a new interactive web server for discovering genes of interest in HCC on a large-scale and comprehensive basis. Integrative HCC Gene Analysis (IHGA) contains over 100 independent HCC patient-derived datasets (with over 10,000 tissue samples) and more than 90 cell models. IHGA allows users to conduct a series of large-scale and comprehensive analyses and data visualizations based on gene mRNA levels, including expression comparison, correlation analysis, clinical characteristics analysis, survival analysis, immune system interaction analysis, and drug sensitivity analysis. This method notably enhanced the richness of clinical data in IHGA. Additionally, IHGA integrates artificial intelligence (AI)-assisted gene screening based on natural language models. IHGA is free, user-friendly, and can effectively reduce time spent during data collection, organization, and analysis. In conclusion, IHGA is competitive in terms of data scale, data diversity, and functionality. It effectively alleviates the obstacles caused by HCC heterogeneity to data mining work and helps advance research on the molecular mechanisms of HCC.