Pan-cancer analysis of HOMER1 in hepatocellular carcinoma revealing diagnostic potential and immune landscape with experimental validation

Abstract

Objective

Homer scaffold protein 1 (HOMER1), a postsynaptic scaffold protein, regulates excitatory synapses and intracellular signaling and has been implicated in tumorigenesis. This study systematically evaluates the oncogenic roles of HOMER1 through pan-cancer bioinformatics analysis and functional validation in hepatocellular carcinoma (LIHC).

Methods

Multi-omics data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Tumor Immune Estimation Resource 2.0 (TIMER 2.0), cBioPortal, UALCAN, and other public databases were integrated to analyze HOMER1 expression profiles, prognostic relevance, epigenetic modifications, and immune infiltration. Drug sensitivity was assessed using Gene Set Cancer Analysis Lite (GSCALite) and DrugBank databases. Protein-protein interaction networks were constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and functionally annotated via Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. HOMER1 protein expression was validated by immunohistochemistry in LIHC tissues. Cellular assays, including Cell Counting Kit-8 (CCK-8), Transwell migration and invasion assays, wound healing assay, and quantitative polymerase chain reaction (qPCR), were performed to investigate the effects of HOMER1 knockdown on LIHC cell proliferation, migration, and invasion.

Results

Pan-cancer analysis revealed that HOMER1 exhibits cancer-type-specific dysregulation and is significantly associated with patient prognosis, DNA methylation, RNA modifications, immune cell infiltration, and immune checkpoint gene expression. HOMER1 expression correlated with anticancer drug sensitivity and was enriched in tumor-promoting pathways such as the peroxisome proliferator-activated receptor (PPAR) signaling pathway. In LIHC, HOMER1 was highly expressed, and its knockdown markedly suppressed hepatoma cell proliferation, migration, and invasion in vitro.

Conclusion

Our findings demonstrate that HOMER1 functions as a potential oncogene across multiple cancers and actively promotes malignant phenotypes in hepatocellular carcinoma. This study highlights HOMER1 as a promising prognostic biomarker and therapeutic target, providing new insights into cancer prognosis and immunotherapy strategies.

Introduction

LIHC is one of the major malignant tumors worldwide, with a persistently high incidence and mortality rate. Statistics from 2020 show that LIHC is the sixth most common cancer and the third leading cause of cancer-related deaths worldwide [1]. Clinical research has found that the majority of LIHC patients have progressed to an advanced stage by the time of diagnosis [2, 3]At this point, the difficulty of treatment has significantly increased, the prognosis of patients is poor, and the 5-year survival rate is less than 10% [4, 5]. Many studies have shown that finding relevant indicators for diagnosing LIHC and predicting prognosis is extremely important for guiding the treatment of LIHC [6, 7]and immunotargeted therapy may be a good option [8]. In the tumor microenvironment (TME) [9]the discovery of tumor epigenetic changes [10]immune cell populations [11]immune detection points [12]etc., may help to better realize immune-targeted therapy and provide new oncology strategies for the precision treatment of cancer. Against this backdrop, HOMER1, a postsynaptic scaffold protein traditionally associated with regulating excitatory synapses and intracellular signaling, has recently been implicated in tumorigenesis. Its potential role in modulating tumor-related pathways and immune responses has yet to be fully elucidated. Given the growing evidence linking HOMER1 to cancer biology, we hypothesized that HOMER1 may serve as a key regulator in LIHC progression and immunotherapy response. To address this, we conducted a comprehensive study to explore the correlation between HOMER1 and its prognostic value, potential molecular mechanisms, and immune cell infiltration in LIHC through pan-cancer bioinformatics analysis. Furthermore, we experimentally validated our findings in LIHC models to assess the functional impact of HOMER1 on tumor behavior. Current research on HOMER1 in tumors mostly focuses on individual cancer types, lacking a panoramic systematic analysis, and its immune-related functions remain unclear [13, 14]. This study is the first to integrate multi-omics and immunological features from a pan-cancer perspective, systematically revealing the expression patterns and clinical significance of HOMER1 in different cancer types.

Materials and methods

HOMER1 expression profiling

HOMER1 mRNA levels in normal tissues were analyzed using the Human Protein Atlas (HPA, https://www.proteinatlas.org/). RNA-seq data from 33 cancers types were retrieved from TCGA (https://portal.gdc.cancer.gov/) and GTEx (https://www.gtexportal.org/home), with additional data from UALCAN (http://ualcan.path.uab.edu/analysis.html) for 24 cancers. The acronyms of 33 tumors are shown in Table 1.

Table 1. Abbreviations for 33 cancers

Cancer Type	Abbreviations
Adrenocortical carcinoma	ACC
Bladder urothelial carcinoma	BLCA
Breast invasive carcinoma	BRCA
Cervical squamous cell carcinoma and endocervical adenocarcinoma	CESC
Cholangiocarcinoma	CHOL
Colon adenocarcinoma	COAD
Lymphoid neoplasm diffuse Large B-cell lymphoma	DLBC
Esophageal carcinoma	ESCA
Glioblastoma multiforme	GBM
Head and neck squamous cell carcinoma	HNSC
Kidney chromophobe	KICH
Kidney renal papillary cell carcinoma	KIRC
Acute myeloid leukemia	LAML
Brain lower grade glioma	LGG
Liver hepatocellular carcinoma	LIHC
Lung adenocarcinoma	LUAD
Lung squamous cell carcinoma	LUSC
Mesothelioma	MESO
Ovarian serous cystadenocarcinoma	OV
Pancreatic adenocarcinoma	PAAD
Pheochromocytoma and paraganglioma	PCPG
Prostate adenocarcinoma	PRAD
Rectum adenocarcinoma	READ
Sarcoma	SARC
Skin cutaneous melanoma	SKCM
Stomach adenocarcinoma	STAD
Testicular germ cell tumors	TGCT
Thyroid carcinoma	THCA
Thymoma	THYM
Uterine corpus endometrial carcinoma	UCEC
Uterine carcinosarcoma	UCS
Uveal melanoma	UVM
Kidney renal papillary cell carcinoma	KIRP

Prognostic and diagnostic evaluation

The RNA-seq dataset from TCGA and GTEx was log2-transformed, and inter-cancer expression differences were assessed using the Wilcoxon rank-sum test (P < 0.05). Cox regression models were employed to generate hazard ratios (HR) and 95% confidence intervals (CI), with survival outcomes visualized using survminer and ggplot2. ROC curves were plotted to evaluate diagnostic potential, categorizing AUC values as low (0.5–0.7), medium (0.7–0.9), or high (> 0.9).

Epigenetic and RNA modification analysis

UALCAN was used to explore HOMER1 DNA methylation (β-values: hypomethylation 0.3–0.25, hypermethylation 0.7–0.5) in normal and tumor tissues. MethSurv provided methylation profiles in LIHC. RNA modifications (m1A and m5C) were analyzed using SangerBox 3.0 (http://sangerbox.com/).

Immune infiltration and microenvironment

TIMER 2.0 (http://timer.comp-genomics.org/) was used to correlate HOMER1 expression with immune cell infiltration. GSEA quantified immune infiltration differences. Immunomodulatory and checkpoint gene correlations were analyzed via SangerBox 3.0. ESTIMATE scores evaluated stromal and immune infiltration, while TMB and MSI were assessed using cBioPortal.

Drug sensitivity assessment

GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/) analyzed HOMER1-related drug sensitivity using GDSC and CTRP datasets.

Co-expression and functional enrichment

STRING (https://cn.string-db.org/) identified co-expressed genes, with KEGG and GO enrichment performed using Xiantao.

Immunohistochemistry

Paraffin-embedded tissue sections from 20 LIHC patients were analyzed. Inclusion criteria: primary LIHC, first-time treatment, partial hepatectomy, no prior therapy. Exclusion: non-hepatocellular malignancies. Ethical approval (No. 2025-E0019) and informed consent were obtained. Tissues were deparaffinized, rehydrated, and stained with HOMER1 antibody (BOSTER, M03877-1, 1:200). Staining intensity (0–3) and percentage of positive cells (0–2) were scored, with final scores classified as low (0–2) or high (3–6) expression.

Cell culture

Huh7 and SNU-449 cells were purchased from the Shanghai Cell Bank, Chinese Academy of Sciences, and treated at 37^°C and 5% CO₂. Cells were treated with Dulbecco’s modified Eagle medium (DMEM) and 1640 medium (RPMI 1640 medium) containing 10% fetal bovine serum and 1% penicillin-streptomycin.

Cell transfection

HOMER1-siRNA was purchased from. The Si-1 sequence is 5′-AGAAAUGUAUAGGAUAA-3′; The Si-2 sequence is 5′-GAAGCUGCUCGACUAGCAA-3′. Huh7 and SNU-449 cells were seeded in 6-well plates and transfected when the cell density reached 70%. HOMER1 knockdown cell lines were obtained after transfection for subsequent experiments.

Real-time quantitative PCR

Extract total RNA from cells using an RNA extraction kit and reverse transcribe the RNA into cDNA according to the manufacturer’s instructions. Quantitative real-time polymerase chain reaction (qPCR) was used to detect gene expression levels. The HOMER1 primer sequences were: 5′-CTTCGGGACACCTGCTTGCTTC − 3′ (forward) and 5′-TGCTCACAATACGAGTGGACACATTC − 3′ (reversed). β-actin was used as an internal control, and the relative expression level of the gene was calculated by using the 2^−ΔΔCt method.

Western blotting and antibodies

A percentage of RIPA and PMSF (Thermo Fisher Scientific, USA) lysate was added to the transfected cells to obtain cellular proteins, the protein concentration was determined, and the protein loading buffer was added in a 1:4 ratio. SDS-PAGE gel electrophoresis proteins, polyvinylidene fluoride (PVDF) membrane transferred proteins from the gel, skimmed milk powder blocking for 1 h, PBST wash 3 times, and primary antibody incubation at 4 °C overnight. Wash again, incubate the secondary antibody for 1 h at room temperature, visualize the protein bands using the Bio-Rad ChemiDoc MP Imaging System, and analyze them by Image J software. The primary antibodies are against HOMER1(Proteintech, 68597-1-Ig) and GAPDH (Proteintech, 60004-1-Ig).

CCK-8 assay

Cell Counting Kit-8 (CCK8) reagent was used to detect cell proliferation, and the cell proliferation ability was evaluated on days 0, 1, 2, 3, 4 and 5.

Wound healing assay

A scratch test is performed to assess the ability of cells to migrate. Huh7 and SNU-449 cells were obtained in logarithmic growth phase, adjusted to a cell density of 2 × 10⁵ cells/mL, seeded in 6-well plates, and incubated for 48 h at 37 °C and 5% CO₂ in an incubator. Cells were cultured using 2% FBS medium, and after the cells were spread out, scratches were performed. Observe the cells under an inverted light microscope and take photographs to record, and calculate the cell migration area with ImageJ software. Migration tests are performed to assess the ability of cells to migrate.

Clone formation assay

Cells are seeded in 6-well plates with 3000 cells per well and fresh medium is changed every 3 days. After 12 days of culture, the cells were fixed with 4% paraformaldehyde for 30 min, stained with 0.5% crystal violet for 30 min, washed in PBS and photographed, and the number of colonies was calculated using ImageJ.

Transwell assay

To test migration and invasion, the Transwell chamber (8 μm pore size, BD) is coated with Matrigel (Corning) and placed in a 37 °C incubator for 1 h to make it gelatinous. There is no need to lay Matrigel to test migration ability. Resuspend cells using FBS-free medium, add 200 µL of cells/mL cell suspension at a density of 8 × 10⁴ to the upper chamber, add to the Trans wells, and add 700 µL of medium containing 10% FBS to the lower chamber. After 48 h of incubation, the cells were fixed by PBS wash, 4% paraformaldehyde was fixed, and crystal violet (0.1%) cells were stained for 45 min at room temperature. After washing and drying, the number of cells passed through was recorded under an inverted microscope to evaluate the migration and invasion ability of the cells.

Statistical analysis

Data analysis was performed using R (version 4.2.1). Wilcoxon’s rank-sum test was used to evaluate the relationship between HOMER1 expression and normal liver tissue of LIHC and its clinicopathological characteristics. The t-test was used to assess HOMER1 expression in both unpaired and paired tissues. The chi-square test and Yates’ correction were used to compare the categorical variables between the groups. A P value of < 0.05 indicates statistically significant. Spearman correlation analysis estimated the correlation of HOMER1 with RNA methylation, TMB, and MSI; p < 0.05 indicates statistical significance. For cell and molecular biology experiments, data from at least three independent experimental replicates were analyzed using SPSS and Prism 8 (GraphPad) using either the t-test or the Wilcoxon rank-sum test; P < 0.05 was statistically significant.

Results

Expression of HOMER1 in pan-cancer tissues

The expression pattern of HOMER1 across various human organ systems was analyzed using the Human Protein Atlas (HPA) database. HOMER1 showed the highest expression levels in the musculoskeletal, nervous, and cardiovascular systems (Fig. 1A). Further analysis of The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases revealed that HOMER1 expression was significantly upregulated in cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), rectum adenocarcinoma (READ), and stomach adenocarcinoma (STAD) compared to normal tissues (Fig. 1B–C). Consistently, UALCAN database analysis showed elevated HOMER1 expression in CHOL, COAD, HNSC, LIHC, READ, and STAD (Fig. 1D).

However, it is noteworthy that HOMER1 expression was lower in tumor tissues compared to normal tissues in certain cancer types (data not shown). These results suggest that HOMER1 expression exhibits tumor-type-specific regulation, and its upregulation in selected cancers may imply a potential oncogenic role.

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Fig. 1

Expression patterns of HOMER1 in different types of cancer. A Expression levels of HOMER1 in normal HPA-BASED tissues. B HOMER1 expression in multiple cancer types was analyzed using the TCGA database. C Analysis of HOMER1 expression in pan-cancer using paired adjacent cancer/tumor samples from the TCGA + GTEx database. D Analysis of the experssion levels of HOMER1 protein in 24 cancers USING UALCAN. “ns” indicates that the result is not significant. (* p < 0.05, ** p < 0.005, *** p < 0.001)

Survival analysis

Using TCGA datasets, we explored the relationship between HOMER1 expression and patient survival across 33 different cancer types. Survival analysis revealed that elevated HOMER1 expression was associated with poorer prognosis in LIHC, PAAD, BLCA, ACC, MESO, DLBC, and LUAD, while decreased expression correlated with worse prognosis in ESCA and COAD (Fig. 2A). Diagnostic utility analysis via ROC curves indicated that HOMER1 expression had moderate diagnostic accuracy for LIHC (AUC = 0.664), ESCA (AUC = 0.815), LUAD (AUC = 0.731), and COAD (AUC = 0.933) (Fig. 2B). These findings suggest that HOMER1 expression may serve both as a prognostic and diagnostic marker across various cancers.

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Fig. 2

Cancer prognosis and diagnostic analysis based on HOMER1 expression. A Relationship between HOMER1 mRNA expression and overall survival in TCGA and GEO datasets. B ROC analysis of cancer diagnosis based on HOMER1 mRNA expression levels. P < 0.05 was statistically significant. (* p < 0.05, ** p < 0.005, *** p < 0.001)

Genetic alterations and DNA methylation of HOMER1

Analysis of genetic alterations showed that HOMER1 undergoes mutations, amplifications, and deletions across cancers, with amplifications being the most common. The highest alteration frequency was observed in UCEC (~ 15%), while amplifications were particularly prevalent in TGCT, KIRC, LUAD, and PRAD (Fig. 3A). Copy number variation (CNV) analysis revealed that HOMER1 expression was significantly associated with CNV status in BLCA, HNSC, ESCA, and OV, while pan-cancer CNV-survival analysis highlighted its influence on overall survival (OS) and disease-specific survival (DSS) in LIHC (Fig. 3B-C).

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Fig. 3

DNA methylation analysis and characterization of HOMER1 gene variants in cancer. A Detection of HOMER1 mutations in pan-cancer using the cbiopportal database shows the frequency of different types of HOMER1 mutations in each cancer. B Correlation between HOMER family genes and CNV in pan-cancer. C The effect of various survival outcomes in patients with CNV-related cancers in pan-cancer, including OS, disease-free survival (DFS), progression-free survival (PFS), and DSS. P < 0.05 was statistically significant. (* p < 0.05, ** p < 0.005, *** p < 0.001)

Relationship between HOMER1 expression and tumor immunity

Utilizing TIMER data, we found that HOMER1 expression correlated with the infiltration levels of 24 immune cell types across multiple cancers, notably in BLCA, BRCA, CESC, KIRC, LGG, LIHC, LUAD, LUSC, PAAD, PRAD, SARC, SKCM, STAD, TGCT, THCA, and UCEC (Fig. 4A). In LIHC, HOMER1 high and low expression groups exhibited significant differences in the enrichment scores of Eosinophils, T cells, Macrophages, Th17 cells, and Th2 cells (Fig. 4B). Correlation analyses further indicated that HOMER1 expression positively associated with most immune cells within the TME of LIHC (Fig. 4C). Additionally, HOMER1 expression was highly correlated with immune checkpoint molecules such as PD-L1, PD-1, LAG-3, and CTLA-4, suggesting its potential role in promoting immune escape mechanisms (Fig. 4D).

Moreover, HOMER1 expression showed significant correlations with TMB and MSI in various malignancies. Specifically, HOMER1 positively correlated with TMB in ACC, COAD, READ, KIRC, LUAD, PRAD, PCPG, SKCM, TGCT, and THYM, while negative correlations were noted in CHOL and UVM. For MSI, positive correlations were observed in COAD, ESCA, KIRC, and TGCT, and negative correlations in DLBC, HNSC, and MESO (Fig. 4E). These findings propose HOMER1 as a potential biomarker for immune checkpoint therapy eligibility across cancers.

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Fig. 4

Correlation analysis between HOMER1 expression and immune invasion A Relationship between HOMER1 gene expression and immune cell invasion in pan-cancer B Enrichment score in a variety of immune cells in LIHCs with high and low HOMER1 expression C Correlation between HOMER1 expression in LIHC and immune cell infiltration in TME D Expression of immune checkpoint molecules in LIHC (E) Radar map showing the relationship between TMB and MSI and HOMER1 gene expression in different tumors. “ns” indicates that the result is not significant. (* p < 0.05, ** p < 0.005, *** p < 0.001)

Relationship between HOMER1 and Immunomodulatory genes

Further analyses revealed that HOMER1 expression was positively associated with various immunomodulatory genes, including chemokines, chemokine receptors, MHC molecules, immunosuppressive, and immunostimulatory genes across cancers (Fig. 5A). Notably, HOMER1 displayed a positive correlation with immune checkpoint genes in PRAD, KICH, PAAD, SCAD, and MESO, while largely negative correlations were observed in PCPG, COAD, UVM, KIRC, CHOL, and LUSC (Fig. 5B).

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Fig. 5

Analysis of the relationship between HOMER1 and immune checkpoints and immunoregulatory genes in pan-cancer tissues. A Relationship between HOMER1 and immunomodulatory genes. B Correlation between HOMER1 expression and immune checkpoints in pan-cancer tissues. (* p < 0.05, ** p < 0.005, *** p < 0.001.)

DNA methylation and RNA modifications

Using UALCAN, we identified HOMER1 promoter hypermethylation in LUSC, COAD, KIRC, BRCA, and PAAD, while hypomethylation occurred in PRAD, KIRP, BLCA, and THCA (Fig. 6A). This dysregulation likely influences HOMER1 expression. Furthermore, pan-cancer analyses revealed that HOMER1 expression significantly correlates with RNA modification regulators involved in m6A, m5C, and m1A pathways (Fig. 6B), suggesting a complex interplay between DNA methylation and RNA modifications in tumor biology. Mechanistically, HOMER1 promoter methylation might alter chromatin accessibility, thereby regulating RNA methyltransferase expression. Clinically, reader proteins like YTHDF3 and methyltransferase activators such as METTL14 may represent potential therapeutic targets in HOMER1-driven tumors.

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Fig. 6

Relationship of HOMER1 with methylation and methyltransferases. A HOMER1 promoter methylation levels in PRAD, KIRP, BLCA, THCA, PRAD, KIRP, BLCA, and THCA. B Correlation between HOMER1 expression and m1A, m5C, and m6A regulatory genes. (* p < 0.05, ** p < 0.005, *** p < 0.001.)

Drug sensitivity analysis

Drug sensitivity analyses using GSCALite indicated that HOMER1 expression negatively correlated with the IC₅₀ values of several anti-tumor agents including afatinib, austocystin D, bardoxolone methyl, bleomycin A2, daporinad, eriotinib, and others (Fig. 6A). Additionally, HOMER1 expression correlated with increased sensitivity to Camptothecin, Cisplatin, Cytarabine, Doxorubicin, Gemcitabine, and Vinblastine among others (Fig. 6B). These findings highlight the potential of HOMER1 as a biomarker for predicting tumor responsiveness to specific chemotherapeutic agents.

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Fig. 7

Drug sensitivity of HOMER1-related drugs in pan-cancer tissues. A Relationship between CTRP drug sensitivity and HOMER1 mRNA expression. B Relationship between GDSC drug sensitivity and HOMER1 mRNA expression

Functional enrichment and mechanistic insights

Differential gene analysis in LIHC revealed that HOMER1-related genes were enriched in pathways including chemical carcinogenesis, steroid hormone biosynthesis, and bile secretion (Fig. 8A). GO enrichment suggested involvement in xenobiotic metabolism, cell import processes, and hormone regulation (BP); apical/basal cell polarity (CC); and channel/transporter activities (MF) (Fig. 8B). HOMER1 also appeared to modulate the PPAR signaling pathway, with key co-expressed genes including RXRG, CYP8B1, FABP4, ACADL, SLC27A5, and others (Fig. 8C-D). Spearman correlation analysis further showed that HOMER1 expression was inversely associated with CYP8B1, ACADL, SLC27A5, APOA5, and CYP7A1.

Previous studies suggest that combining MMP1, HMGCS2, and SLC27A5 in the PPAR pathway could predict LIHC prognosis. Thus, HOMER1’s regulatory influence on this pathway provides new insights into liver cancer diagnosis, treatment, and prognostic evaluation.

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Fig. 8

Enrichment and functional analysis of HOMER1-related genes in LIHC. KEGG A and GO enrichment B were analyzed based on the related genes of HOMER1 in LIHC. C Expression heat map of 13 genes related to PPAR signaling pathway that were positively and negatively correlated with HOMER1 expression. D Scatter plots of some PPAR signaling pathway-related genes and HOMER1 expression

Experimental validation

By immunohistochemistry of liver cancer tissues and adjacent tissues, the results showed that the expression of HOMER1 protein in liver cancer tissues was significantly higher than that in adjacent tissues (Fig. 9A-B), suggesting that HOMER1 may be a pro-tumor gene of LIHC. Through the HPA website, it was known that the expression of HOMER1 in Huh7 and SNU-449 cells was higher than that of normal hepatocytes, so we selected these two cell lines for subsequent validation. The knockdown efficiency of HOMER1 gene was verified by qPCR assay as shown in (Fig. 9C-D), the two sites of si-1 and si-2 showed good knockout efficiency, and the effect of HOMER1 gene knockdown site was further verified by western blot assay. The results showed that the expression of HOMER1 at the two knockdown sites of si-1 and si-2 was significantly decreased (Fig. 9E-H). These two sites were selected for HOMER1 knockdown cell construction in subsequent cell experiments. CCK-8 proliferation assays showed that knockdown of HOMER1 significantly inhibited the proliferative capacity of Huh7 and SNU-449 cells (Fig. 9I). Colony formation experiments showed that knockdown of HOMER1 significantly reduced the size and number of cell colonies, further confirming these results (Fig. 9J-L). We then performed wound healing assays and Transwell assays to further explore the effect of HOMER1 on LIHC cell invasion and migration. Wound healing experiments showed that knockdown of HOMER1 significantly inhibited the migration ability of Huh7 and SNU-449 cells (Fig. 9M-O). Similarly, the transwell assay further demonstrated the ability of knockdown HOMER1 to significantly inhibit Huh7 and SNU-449 cell migration and invasion (Fig. 9P-T). These results indicated that the decrease in HOMER1 expression inhibited the proliferation, migration and invasion of hepatocellular carcinoma cells, further proving that HOMER1 is a pro-cancer factor of LIHC.

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Fig. 9

A–H HOMER1 knockout efficiency was verified by quantitative PCR (qPCR) and western blotting after transfection of siRNA in Huh7 and SNU-449 cells. CCK8 assay (I) and colony formation assay (J–L) were used to verify the effect of down-regulation of HOMER1 expression on cell proliferation. The effect of HOMER1 knockdown on the migration ability of Huh7 and SNU-449 cells was evaluated by wound healing assay (M–O). The effects of down-regulation of HOMER1 expression on Huh7 and SNU-449 cell migration (P–R) and invasion ability (S–T) were analyzed by transwell assay. (* p < 0.05, ** p < 0.005, *** p < 0.001.)

Discussion

Previously, it has been pointed out that Homer1 plays an important role in the nervous system [15]not only participating in a variety of physiological processes [16]but also may be a potential therapeutic target for certain diseases [17]. However, so far there has been no pan-cancer analysis of Homer1. Our study provides a comprehensive and systematic analysis of the role of Homer1 in human cancer, investigates its differences and commonalities between different cancer species, and explores its potential as a pan-cancer biomarker. We focused on the role of HOMER1 in LIHC, LIHC was selected as the primary focus for in-depth analysis based on it ranks among the leading causes of cancer-related mortality, with particularly high incidence and death rates in global —and on our preliminary bioinformatics screening, which revealed markedly elevated HOMER1 expression in LIHC closely associated with poor clinical outcomes. These findings suggest that HOMER1 may play a critical role in LIHC progression. Overall, our work provides novel insights into the oncogenic potential of HOMER1 and underscores its significance as a diagnostic and prognostic marker across multiple cancer types.

We first analyzed HOMER1 expression levels in different cancers. Unpaired and paired differential expression analyses revealed that HOMER1 is dysregulated in almost all tumors. HOMER1 exhibits high expression in most cancers, particularly in CHOL, COAD, ESCA, HNSC, LIHC, READ, and STAD, whereas its expression is significantly reduced in KICH, KIRC, and KIRP. This bidirectional pattern suggests that HOMER1 may act as a context-dependent regulator, with its functional impact shaped by isoform composition, interaction partner availability, and tissue-specific signaling programs. Notably, the short HOMER1a isoform acts as a dominant-negative regulator of synaptic signaling, whileThe long HOMER1b/c subtype serves as a stable scaffold for signaling complexes in pathways such as mTOR, PI3K/AKT or GPCR [18, 19]. We hypothesize that in cancers such as LIHC, the predominance of HOMER1b/c in a pro-inflammatory microenvironment may promote oncogenic signaling by facilitating assembly of proliferative or survival complexes. Conversely, in KICH, KIRC, and KIRP, reduced HOMER1 expression could reflect a shift toward alternative scaffolding proteins or loss of tumor-suppressive interactions, such as modulation of apoptosis or metabolic stress responses. Further studies are needed to dissect the precise mechanisms underlying these tissue-specific disparities, including isoform-specific knockout models and interactome profiling in distinct tumor contexts. Furthermore, HOMER1 dysregulation was linked to clinical parameters and prognosis in several malignancies. Elevated HOMER1 expression in LIHC, PAAD, BLCA, ACC, MESO, DLBC, and LUAD was associated with poor OS and DFS. These findings suggest that HOMER1 could serve as a predictive biomarker for cancer prognosis and may aid in identifying patients with a higher risk of poor clinical outcomes.

Genomic alterations play a crucial role in tumor progression and treatment response [20]. We found that HOMER1 harbored genetic mutations in various cancers, with the highest mutation frequency observed in UCEC (> 15%), followed by PAAD and OV. CNV analysis revealed a strong correlation between HOMER1 expression and CNV status in BLCA, HNSC, ESCA, and OV. Additionally, CNV-related survival analysis suggested that HOMER1 influences OS and DSS in LIHC, supporting its role as an oncogene in several cancers, including colorectal and breast cancer. Understanding these genomic alterations provides essential insights into the molecular mechanisms of cancer development and may contribute to the development of targeted therapies.

To explore HOMER1’s role at the epigenetic level, we examined its promoter DNA methylation patterns across multiple cancers. Aberrant methylation of the HOMER1 promoter was detected in nine cancers and was strongly correlated with dysregulated mRNA expression, suggesting that epigenetic modifications may contribute to its transcriptional alterations. This finding underscores the importance of DNA methylation in regulating HOMER1 expression and suggests that targeting epigenetic modifications could be a potential therapeutic approach in HOMER1-associated cancers.

The intricate relationship between tumor immunity and prognosis is well established [21]. Numerous genes modulate tumor progression by shaping TME [22] and influencing immune cell infiltration dynamics [23]. Given this, we investigated the association between HOMER1 and tumor immunity. Immune infiltration analysis revealed a significant correlation between HOMER1 expression and infiltration levels of multiple immune cell subtypes across various malignancies suggesting that they might be involved in the immune regulatory process. Notably, in LIHC, the high expression of HOMER1 is accompanied by significant upregulation of immune checkpoint molecules such as PD-L1, PD-1, LAG-3 and CTLA-4 [24]. This co-expression pattern suggests that HOMER1 may drive immune escape by promoting the activation of immune checkpoints. Immunotherapeutic blockade of immune checkpoints has been shown to restore CD8 + T-cell function [25]. Dysfunctional or exhausted T cells express multiple immune checkpoint receptors [26, 27]with LAG-3 and PD-1 frequently co-expressed in chronic infection [28] and autoantigen recognition models [29]. Coordinated inhibition of PD-L1, PD-1, LAG-3, and CTLA-4 can enhance TCR signaling and boost antitumor immunity [30]. Based on our analysis results, it is speculated that HOMER1 may affect the secretion of chemokines such as CXCL9, CXCL10 and CCL5 by regulating Ca²⁺ signaling, thereby altering the recruitment, functional status and exhaustion degree of CD8+ T cells, and may simultaneously influence the antigen presentation pathway. Based on this, HOMER1 not only plays a role in the regulation of immune infiltration but may also serve as a potential biomarker for predicting the response to immunotherapy. Its predictive and stratified value in the treatment with immune checkpoint inhibitors (ICI) is worthy of further verification. In addition, the development of drugs targeting the protein-protein interaction interface or signaling axis mediated by HOMER1 may provide new therapeutic strategies for LIHC and other tumors with high HOMER1 expression.

The efficacy of immune checkpoint inhibitors (ICIs) in the treatment of tumors is widely recognized [31]and TMB and MSI appear to be the key factors in deciding whether to proceed with immune checkpoint therapy [32]. we further explored HOMER1’s correlation with immunomodulatory genes, including chemokines, chemokine receptors, MHC genes, immunosuppressive genes, and immunostimulatory genes. Most cancers exhibited a positive correlation between HOMER1 and these immune-related genes, except for PCPG, COAD, UVM, KIRC, CHOL, and LUSC. Drug sensitivity analysis using the CTRP and GDSC databases indicated that HOMER1 overexpression was positively correlated with the IC₅₀ values of several drugs, such as NSC19630, sotrastaurin, and SRT-1720. Additionally, HOMER1 was found to be a potential resistance gene for LIHC treatment drugs like linifanib and doxorubicin, providing valuable insights into drug selection and treatment strategies. Previous studies have shown that EVH1 domain of HOMER1 and the coiled-coil region provide potential PPI inhibition targets. Some PPI inhibitors target the Ena/VASP EVH1 domain, providing a precedent for targeting HOMER1 [33, 34–35]. In the future, pocket prediction tools such as FTMap and DoGSite can be combined to screen and combine hotspots, and verified through fragments or macrocyclic compounds. These findings emphasize the potential role of HOMER1 in guiding personalized cancer therapy and predicting drug resistance.

Functional enrichment analysis revealed that HOMER1 expression in LIHC was significantly associated with the PPAR signaling pathway [36]. The results of PPAR signaling pathway have two important factors, PPARα and PPARγ [37]and it has been found that the activation of PPARγ will promote the proliferation, invasion and migration of hepatocellular carcinoma [38]and accelerate the progression of hepatocellular carcinoma [39]. Functional experiments, including CCK-8, colony formation, scratch, and transwell assays, demonstrated that HOMER1 knockdown inhibited the proliferation, invasion, and migration of Huh7 and SNU-449 cells, suggesting a potential interaction between HOMER1 and PPARγ. I Additionally, PPARα-mediated fatty acid oxidation has been reported to reduce LIHC cell sensitivity to sorafenib [40]and PPARγ can reduce the production of mitochondrial reactive oxygen species (ROS) by upregulating superoxide dismutase 2 (SOD2) [41]thereby inhibiting the mitochondrial dysfunction of sorafenib-induced hepatocellular carcinoma cells and reducing ROS accumulation [42]. However, the precise mechanisms linking HOMER1 to the PPAR signaling pathway require further investigation. This finding highlights a potential molecular mechanism by which HOMER1 contributes to tumor progression and drug resistance in LIHC.

Our pan-cancer analysis and experimental validation suggest that HOMER1 plays a crucial role in cancer progression, prognosis, and immune regulation across multiple tumor types. In particular, HOMER1 appears to act as an oncogene in LIHC, highlighting its potential as a prognostic biomarker and therapeutic target. Despite these findings, our research still has some limitations. Firstly, we verified our results in multiple databases, but the datasets used were mainly from non-Asian populations. Whether the conclusions in this paper hold true in Asian populations remains to be verified. Secondly, the functional verification of Homer1 was limited to the phenotype of LIHC cells and no in vivo experiments or related pathways were conducted. Further research is needed in the future to further clarify the potential of targeting HOMER1 in the treatment of hepatocellular carcinoma. Finally, the validation of the therapeutic potential of HOMER1 is limited to LIHC. Further research is needed to confirm the role of HOMER1 in other types of cancer. This study provides a foundation for future research on HOMER1 and highlights its potential implications in precision oncology and immunotherapy.

Conclusion

This study provides a comprehensive pan-cancer analysis of HOMER1, revealing its crucial role in tumor progression, prognosis, and immune regulation across multiple cancer types. HOMER1 is identified as a tumor promoter in LIHC, where it influences the tumor microenvironment, immune infiltration, and key oncogenic pathways. Its dysregulation is associated with poor survival outcomes and resistance to certain therapies, underscoring its potential as a prognostic biomarker and therapeutic target. These findings enhance our understanding of HOMER1’s oncogenic functions and offer new insights for precision oncology and immunotherapy, particularly in LIHC.

Acknowledgements

Thank to Professor Tao Peng for his support in the implementation of the experiment.

Author contributions

Xinping Ye, and Chuangye Han supervised the project and designed this study. Xiang Wang, Shutian Mo, Jiakao Zhang organized the public data and prepared all the figures and tables. Meifeng Chen, Jiaming Liang and Yongguang Wei conducted the data analysis. Guohong Yan, and Ziyan Lu drafted and revised the manuscript. Guohong Yan1 †, Ziyan Lu1 † and Jiakao Zhang1 † contributed equally to the writing of this article and shared first authorship. Chuangye Han1†, Xinping Ye1† contributed equally to this work and share last authorship.

Funding

No funding.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study involving human participants was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (batch number: 2025-E0019). Written informed consent was obtained from all human participants prior to sample collection, and the study strictly adhered to the principles of the Declaration of Helsinki.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

1. Lu, J; Yu, C; Bao, Q; Zhang, X; Wang, J. Identification and analysis of necroptosis-associated signatures for prognostic and immune microenvironment evaluation in hepatocellular carcinoma. Front Immunol; 2022; 13, 973649.1:CAS:528:DC%2BB38XitlCgsr%2FE [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36081504][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9445885][DOI: https://dx.doi.org/10.3389/fimmu.2022.973649]

2. Kaur, H; Bhalla, S; Raghava, GPS. Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles. PLoS ONE; 2019; 14, e0221476.1:CAS:528:DC%2BC1MXhvF2is7jL [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31490960][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6730898][DOI: https://dx.doi.org/10.1371/journal.pone.0221476]

3. Li, Y-K et al. Portal venous and hepatic arterial coefficients predict Post-Hepatectomy overall and Recurrence-Free survival in patients with hepatocellular carcinoma: A retrospective study. J Hepatocell Carcinoma; 2024; 11, pp. 1389-402. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39011125][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247128][DOI: https://dx.doi.org/10.2147/JHC.S462168]

4. Calderon-Martinez, E et al. Prognostic scores and survival rates by etiology of hepatocellular carcinoma: A review. J Clin Med Res; 2023; 15, pp. 200-7.1:CAS:528:DC%2BB3sXisFCnsbfN [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37187717][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181349][DOI: https://dx.doi.org/10.14740/jocmr4902]

5. Li, J et al. Tumor-associated lymphatic vessel density is a postoperative prognostic biomarker of hepatobiliary cancers: a systematic review and meta-analysis. Front Immunol; 2024; 15, 1519999.1:CAS:528:DC%2BB2MXjtlOht7c%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39840048][DOI: https://dx.doi.org/10.3389/fimmu.2024.1519999]

6. Brown, ZJ et al. Management of hepatocellular carcinoma: A review. JAMA Surg; 2023; 158, pp. 410-20. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36790767][DOI: https://dx.doi.org/10.1001/jamasurg.2022.7989]

7. Wang, Q-B et al. The effectiveness and safety of therapies for hepatocellular carcinoma with tumor thrombus in the hepatic vein, inferior Vena cave and/or right atrium: a systematic review and single-arm meta-analysis. Expert Rev Anticancer Ther; 2025; 25, pp. 561-70.1:CAS:528:DC%2BB2MXosVCqsL4%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/40181594][DOI: https://dx.doi.org/10.1080/14737140.2025.2489651]

8. Sun, L-Y; Zhang, K-J; Xie, Y-M; Liu, J-W. Xiao, Z.-Q. Immunotherapies for advanced hepatocellular carcinoma. Front Pharmacol; 2023; 14, 1138493.1:CAS:528:DC%2BB3sXnsFeqsbw%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37025485][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10070708][DOI: https://dx.doi.org/10.3389/fphar.2023.1138493]

9. Chen, C; Wang, Z; Ding, Y; Qin, Y. Tumor microenvironment-mediated immune evasion in hepatocellular carcinoma. Front Immunol; 2023; 14, 1133308.1:CAS:528:DC%2BB3sXkslShs70%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36845131][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9950271][DOI: https://dx.doi.org/10.3389/fimmu.2023.1133308]

10. Wolinska, E; Skrzypczak, M. Epigenetic changes affecting the development of hepatocellular carcinoma. Cancers; 2021; 13, 4237.1:CAS:528:DC%2BB3MXisFGgs7nP [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34439391][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392268][DOI: https://dx.doi.org/10.3390/cancers13164237]

11. Kotsari, M; Dimopoulou, V; Koskinas, J; Armakolas, A. Immune system and hepatocellular carcinoma (HCC): new insights into HCC progression. Int J Mol Sci; 2023; 24, 11471.1:CAS:528:DC%2BB3sXhs1emtLvN [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37511228][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10380581][DOI: https://dx.doi.org/10.3390/ijms241411471]

12. Foglia, B; Turato, C; Cannito, S. Hepatocellular carcinoma: latest research in pathogenesis, detection and treatment. Int J Mol Sci; 2023; 24, 12224. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37569600][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10419038][DOI: https://dx.doi.org/10.3390/ijms241512224]

13. Cui, X; Liang, H; Hao, C; Jing, X. Homer1 is a potential biomarker for prognosis in human colorectal carcinoma, possibly in association with G3BP1 signaling. Cancer Manag Res; 2020; 12, pp. 2899-909.1:CAS:528:DC%2BB3cXit1Omu7fE [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32425603][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196245][DOI: https://dx.doi.org/10.2147/CMAR.S240942]

14. Wu, S-Y et al. Identification of Homer1 as a potential prognostic marker for intrahepatic cholangiocarcinoma. Asian Pac J Cancer Prev; 2014; 15, pp. 3299-304. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24815486][DOI: https://dx.doi.org/10.7314/APJCP.2014.15.7.3299]

15. Luo, P; Li, X; Fei, Z; Poon, W. Scaffold protein Homer 1: implications for neurological diseases. Neurochem Int; 2012; 61, pp. 731-8.1:CAS:528:DC%2BC38XhsV2qtL7M [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22749857][DOI: https://dx.doi.org/10.1016/j.neuint.2012.06.014]

16. Yoon, S et al. Homer1 promotes dendritic spine growth through ankyrin-G and its loss reshapes the synaptic proteome. Mol Psychiatry; 2021; 26, pp. 1775-89.1:CAS:528:DC%2BB3MXhtF2hsLY%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33398084][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8254828][DOI: https://dx.doi.org/10.1038/s41380-020-00991-1]

17. Foa, L; Gasperini, R. Developmental roles for homer: more than just a pretty scaffold. J Neurochem; 2009; 108, pp. 1-10.1:CAS:528:DC%2BD1MXns1Wkug%3D%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19046353][DOI: https://dx.doi.org/10.1111/j.1471-4159.2008.05726.x]

18. Rybchyn, MS et al. Homer1 mediates CaSR-dependent activation of mTOR complex 2 and initiates a novel pathway for AKT-dependent β-catenin stabilization in osteoblasts. J Biol Chem; 2019; 294, pp. 16337-50.1:CAS:528:DC%2BC1MXitV2nu7jF [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31527082][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6827303][DOI: https://dx.doi.org/10.1074/jbc.RA118.006587]

19. Li, Y; Li, P; Wang, N. Effect of let-7c on the PI3K/Akt/FoxO signaling pathway in hepatocellular carcinoma. Oncol Lett; 2021; 21, 96. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33376529][DOI: https://dx.doi.org/10.3892/ol.2020.12357]

20. Hashemi, M et al. Targeting and regulation of autophagy in hepatocellular carcinoma: revisiting the molecular interactions and mechanisms for new therapy approaches. Cell Commun Signal; 2023; 21, 32.1:CAS:528:DC%2BB3sXjtVGrsr8%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36759819][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912665][DOI: https://dx.doi.org/10.1186/s12964-023-01053-z]

21. Cheng, K et al. Tumor-associated macrophages in liver cancer: from mechanisms to therapy. Cancer Commun; 2022; 42, pp. 1112-40. [DOI: https://dx.doi.org/10.1002/cac2.12345]

22. Czekay, R-P; Cheon, D-J; Samarakoon, R; Kutz, SM; Higgins, PJ. Cancer-Associated fibroblasts: mechanisms of tumor progression and novel therapeutic targets. Cancers; 2022; 14, 1231.1:CAS:528:DC%2BB38Xpt1Kks7k%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35267539][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8909913][DOI: https://dx.doi.org/10.3390/cancers14051231]

23. Zhang, J et al. CD8 + T-cell marker genes reveal different immune subtypes of oral lichen planus by integrating single-cell RNA-seq and bulk RNA-sequencing. BMC Oral Health; 2023; 23, 464.1:CAS:528:DC%2BB3sXhsVKjsbbO [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37422617][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10329325][DOI: https://dx.doi.org/10.1186/s12903-023-03138-0]

24. Llovet, JM et al. Immunotherapies for hepatocellular carcinoma. Nat Rev Clin Oncol; 2022; 19, pp. 151-72.1:CAS:528:DC%2BB38XkvV2qurc%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34764464][DOI: https://dx.doi.org/10.1038/s41571-021-00573-2]

25. Chen, J et al. Unbalanced glutamine partitioning between CD8T cells and cancer cells accompanied by immune cell dysfunction in hepatocellular carcinoma. Cells; 2022; 11, 3924.1:CAS:528:DC%2BB38XjtFKrt7vP [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36497182][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739589][DOI: https://dx.doi.org/10.3390/cells11233924]

26. McKenzie, B; Valitutti, S. Resisting T cell attack: tumor-cell-intrinsic defense and reparation mechanisms. Trends Cancer; 2023; 9, pp. 198-211.1:CAS:528:DC%2BB3sXkslCktg%3D%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36593148][DOI: https://dx.doi.org/10.1016/j.trecan.2022.12.003]

27. Marei, HE; Hasan, A; Pozzoli, G; Cenciarelli, C. Cancer immunotherapy with immune checkpoint inhibitors (ICIs): potential, mechanisms of resistance, and strategies for reinvigorating T cell responsiveness when resistance is acquired. Cancer Cell Int; 2023; 23, 64.1:CAS:528:DC%2BB3sXntl2nu7g%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37038154][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10088229][DOI: https://dx.doi.org/10.1186/s12935-023-02902-0]

28. Tobin, JWD; Bednarska, K; Campbell, A; Keane, C. PD-1 and LAG-3 checkpoint blockade: potential avenues for therapy in B-Cell lymphoma. Cells; 2021; 10, 1152.1:CAS:528:DC%2BB3MXisFyqur3L [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34068762][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8151045][DOI: https://dx.doi.org/10.3390/cells10051152]

29. Yin, X; Wu, T; Lan, Y; Yang, W. Current progress of immune checkpoint inhibitors in the treatment of advanced hepatocellular carcinoma. Biosci Rep; 2022; 42, BSR20212304.1:CAS:528:DC%2BB38XlsFWrtrw%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35075482][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8821949][DOI: https://dx.doi.org/10.1042/BSR20212304]

30. Kraehenbuehl, L; Weng, C-H; Eghbali, S; Wolchok, JD; Merghoub, T. Enhancing immunotherapy in cancer by targeting emerging Immunomodulatory pathways. Nat Rev Clin Oncol; 2022; 19, pp. 37-50.1:CAS:528:DC%2BB38XjsVOntLw%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34580473][DOI: https://dx.doi.org/10.1038/s41571-021-00552-7]

31. Munir, AZ; Gutierrez, A; Qin, J; Lichtman, AH; Moslehi, JJ. Immune-checkpoint inhibitor-mediated myocarditis: CTLA4, PD1 and LAG3 in the heart. Nat Rev Cancer; 2024; 24, pp. 540-53.1:CAS:528:DC%2BB2cXhsFCqtr3L [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38982146][DOI: https://dx.doi.org/10.1038/s41568-024-00715-5]

32. Kavun, A et al. Microsatellite instability: A review of molecular epidemiology and implications for immune checkpoint inhibitor therapy. Cancers; 2023; 15, 2288.1:CAS:528:DC%2BB3sXpslSnur8%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37190216][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137295][DOI: https://dx.doi.org/10.3390/cancers15082288]

33. Wang, T; Zhang, L; Shi, C; Wei, R; Yin, C. Interaction of the Homer1 EVH1 domain and skeletal muscle Ryanodine receptor. Biochem Biophys Res Commun; 2019; 514, pp. 720-5.1:CAS:528:DC%2BC1MXptFWlu7c%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31078268][DOI: https://dx.doi.org/10.1016/j.bbrc.2019.04.199]

34. Singer, A; Ramos, A; Keating, AE. Elaboration of the Homer1 recognition landscape reveals incomplete divergence of paralogous EVH1 domains. Protein Sci; 2024; 33, e5094.1:CAS:528:DC%2BB2cXhsFejtrnF [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38989636][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11237882][DOI: https://dx.doi.org/10.1002/pro.5094]

35. Barone, M et al. Designed nanomolar small-molecule inhibitors of ena/vasp EVH1 interaction impair invasion and extravasation of breast cancer cells. Proc Natl Acad Sci U S A; 2020; 117, pp. 29684-90.1:CAS:528:DC%2BB3cXisVKnsrvJ [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33184177][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703624][DOI: https://dx.doi.org/10.1073/pnas.2007213117]

36. Shi, Q et al. Development of a promising PPAR signaling pathway-related prognostic prediction model for hepatocellular carcinoma. Sci Rep; 2024; 14, 4926.1:CAS:528:DC%2BB2cXlsF2jsrk%3D [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38418897][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10902383][DOI: https://dx.doi.org/10.1038/s41598-024-55086-6]

37. Porcuna, J; Mínguez-Martínez, J; Ricote, M. The PPARα and PPARγ epigenetic landscape in cancer and immune and metabolic disorders. Int J Mol Sci; 2021; 22, 10573.1:CAS:528:DC%2BB3MXitlGmu73N [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34638914][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8508752][DOI: https://dx.doi.org/10.3390/ijms221910573]

38. Wu, L; Guo, C; Wu, J. Therapeutic potential of PPARγ natural agonists in liver diseases. J Cell Mol Med; 2020; 24, pp. 2736-48. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32031298][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7077554][DOI: https://dx.doi.org/10.1111/jcmm.15028]

39. Feng, J et al. Simvastatin re-sensitizes hepatocellular carcinoma cells to Sorafenib by inhibiting HIF-1α/PPAR-γ/PKM2-mediated Glycolysis. J Exp Clin Canc Res; 2020; 39, 24.1:CAS:528:DC%2BB3cXkvVWku7s%3D [DOI: https://dx.doi.org/10.1186/s13046-020-1528-x]

40. Tan, J; Zhang, C; Dong, Y; Liang, H. Bilirubin impairs the Sorafenib sensitivity in Huh7 cells through fatty acid oxidation by up-regulating PPARα/CPT1A. J Army Med Univ; 2022; 44, pp. 1606-12.

41. Nie, S et al. PPARγ/SOD2 protects against mitochondrial ROS-Dependent apoptosis via inhibiting ATG4D-Mediated mitophagy to promote pancreatic cancer proliferation. Front Cell Dev Biol; 2022; 9, 745554. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35186942][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847684][DOI: https://dx.doi.org/10.3389/fcell.2021.745554]

42. Li, Y et al. Sorafenib induces mitochondrial dysfunction and exhibits synergistic effect with cysteine depletion by promoting HCC cells ferroptosis. Biochem Biophys Res Commun; 2021; 534, pp. 877-84.1:CAS:528:DC%2BB3cXit12hsb%2FO [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33162029][DOI: https://dx.doi.org/10.1016/j.bbrc.2020.10.083]