Background
Liver cancer is among the prevailing clinical malignancies, and its global incidence has escalated in recent years, positioning it as the fourth foremost contributor to global cancer-related mortality [
1]. Hepatocellular carcinoma (HCC) is the most common type of liver cancer, accounting for approximately 85% of all primary liver cancer cases [
2]. Early detection of HCC is challenging, often leading to diagnosis at advanced stages that preclude the opportunity for curative surgery [
3,
4]. For more than a decade, Sorafenib has become the only first-line targeted drug approved for advanced HCC [
5,
6]. In 2018, clinical trials demonstrated that Lenvatinib’s effectiveness in treating advanced HCC was non-inferior to that of sorafenib, leading to its designation as a first-line option for targeted therapy in advanced HCC [
7,
8]. Furthermore, Lenvatinib demonstrated significantly superior rates of progression-free survival and objective remission compared to Sorafenib [
9]. Lenvatinib inhibits tyrosine kinase and exerts tumor suppression, and the main targets are rearranged during transfection (RET), vascular endothelial growth factor receptor (VEGFR1-3), platelet-derived growth factor receptor α (PDGFRα), fibroblast growth factor receptor (FGFR1-4), and KIT. In addition, Lenvatinib has immunomodulatory activity, showing stronger antitumor activity in immunocompetent mice [
10].
In recent years, cancer immunotherapy has achieved tremendous clinical progress in solid tumors [
11]. The immunotherapy response rate in HCC was merely 15%, with treatment failure potentially stemming from the immunosuppressive nature of the HCC microenvironment [
12,
13]. Studies have shown that Lenvatinib can affect the immune microenvironment and increase the percentage of CD8 + T cells in HCC [
14,
15]. Lenvatinib could block FGFR4, resulting in decreased tumor PD-L1 expression, providing a rationale for combined immunotherapy regimens [
16]. The objective effective rate of Lenvatinib combined with Pembrolizumab was 44.8%, showing strong anti-tumor activity [
17]. Nonetheless, a subset of patients exhibits resistance to the combined regimen of immunotherapy and Lenvatinib. Therefore, it is still necessary to find new targets to improve the combination therapy scheme.
MEX3C is an RNA binding protein that may affect the energy balance of cells [
18]. MEX3C has been found in extracellular secretions, but its overall function is still unclear [
19]. In this article, we elucidated the biological function of MEX3C in HCC and further explored the significance of MEX3C expression on patients’ prognosis and the immune microenvironment. Finally, we verified the combined effect of MEX3C and Lenvatinib on hepatoma cells through functional experiments. Our findings have offered valuable insights for the investigation of innovative combined immunotherapy strategies for future HCC studies.
Methods
Patients and samples
RNA sequencing data and clinical information from both normal liver tissues and HCC tissues were obtained from The Cancer Genome Atlas (TCGA) database. RNA sequencing data for validating the differential gene expression between normal tissues and HCC tissues were obtained from the Gene Expression Omnibus (GEO) database (GSE14520_GPL571 and GSE76427_GPL10558). Clinical and follow-up information utilized for constructing the prediction model was obtained from the International Cancer Genomics Consortium (ICGC LIRI-JP) website.
Prognostic analysis
Using the average MEX3C expression value as a threshold, the samples were categorized into high and low expression groups. Subsequently, survival analysis was conducted utilizing the R package (survival). Univariate analysis was performed first, and factors with p < 0.05 were included in multivariate cox retrospective analysis.
Predictive prognosis models were constructed using the R(rms) package. The accuracy of survival predictions was assessed using calibration curves and C-index values.
Immune analysis
ESTIMATE can assess the tumor microenvironment in tumor samples based on expression data. The R package (estimate) was utilized to estimate the stromal and immune cell scores (Stromal Score, Immune Score, Estimate Score, and Tumor Purity) for HCC patients.
The R package (e1071, parallel, preprocessCore) was employed to estimate the proportion of immune cells in a sample using the CIBERSORT method. The outcome was computed by CIBERSORT, representing the proportions of 22 distinct immune cell types.
The ssGSEA algorithm was primarily executed using the R package (gsva), enabling the quantification of immune infiltration in HCC. The obtained results represented the degree of infiltration of the 28 immune cell types within the sample.
MEX3C was categorized into two groups based on the average expression level, followed by a comparison of expression differences in immune checkpoint genes (PD1, PDL1, and PDL2) between these groups.
Functional enrichment analysis
Initially, Pearson correlation analysis was conducted on tumor expression data to identify genes associated with MEX3C. Subsequently, the relevant gene data were ranked based on their correlation (cor) value and P value. The top 500 genes were then selected for enrichment analysis, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA) analysis. These analyses were carried out using the R package (clusterProfiler) [
20].
Co-expression network analysis
We constructed co-expression networks using Differentially Expressed Genes (|log2(fold-change)|> 1, p < 0.05) data. Modules were linked to immunity using the R package (WGCNA), while gene significance (GS) and module membership (MM) were calculated [
21]. The genes with GS > 0.2 and MM > 0.8 in the black module were defined as hub genes, and correlation analysis was performed. A protein-protein interaction (PPI) network was constructed with the STRING database.
Cell counting Kit-8 assay
A total of 2 × 10
3 cells were seeded per well in 96-well plates, followed by cell adhesion to the surface and treatment with a gradient of drugs (0–20µM). Following 72 h of drug exposure, Cell Counting Kit-8 reagent (Yeasen Biotechnology, 40203ES80) was diluted at a 1:10 ratio and added to each well. The samples were then incubated at 37 °C for a duration of two hours. Finally, the absorbance of the cells was measured at a wavelength of 450 nm [
22].
Wounding healing assay
The scratch chamber (Ibidi, 80,209) was positioned on a 12-well plate, and 7 × 105 cells were inoculated on both sides of the scratch chamber. After the cells adhered to the wall, the scratch chamber was removed, and the cells were placed in a low serum medium. Subsequently, Lenvatinib drug was added to the respective wells. Finally, images were captured at 0-hour and 24-hour time points to compare the cellular healing rates among the different groups.
Transwell migration assay
The chambers (Corning, 3464) were positioned in 24-well plates, and 200 µL of serum-free cell suspension containing 2 × 104 cells was added to each upper chamber. Each lower chamber was filled with 600 µL of 10% serum medium. Following a 24-hour incubation at 37 °C, cells were fixed with methanol and subsequently stained with crystal violet dye. Cells in the upper chamber were gently wiped, and images were captured and recorded for the cells that migrated across the chamber’s transmembrane.
RNA extraction and fluorescence quantitative PCR
RNA extraction from cells was performed using the RNA rapid extraction kit (ESscience Biotech, RN001). Complementary DNA (cDNA) was synthesized from the extracted RNAs using the kit (Takara, RR037A-1). Real-time fluorescent PCR quantification was performed using the SYBR Green reagent (Yeasen, 11184ES08). The primer sequences can be found in Supplementary Table 1.
Western blotting
The supernatant protein was collected following the lysis of Huh7 cells. Protein samples were quantified using a BCA Kit. Protein samples were separated using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene difluoride membranes (PVDF). At the end of transfer, blocking was performed using 5% nonfat milk for about 1 h at room temperature conditions. Blots were probed with anti-MEX3C (Santa, 398,440) and anti-β-Actin (Affinity, AF7018) antibodies, which were incubated overnight at 4 ° C on a shaker. The next day, the membranes were washed using Tris-buffered saline with Tween 20 (TBST). Subsequently, the membrane was next washed using TBST after binding with secondary antibodies for 1 h at room temperature. Finally, the amount of protein expression in the sample bands was detected using chemiluminescence.
Transfected SIRNA
5 × 105 cells were inoculated in a six well culture plate and transfected when the cell density reached 70%. Lipo3000 was used as transfection reagent. The siRNA of MEX3C (GenePharma, Shanghai, China) was added to the medium and transfected for about 48 h. After two days of culture, it was used for subsequent experiments. The primer sequences can be found in Supplementary Table 2.
Statistical analysis
Student’s t-test was used to analyze the difference between the means of the two groups. All data were statistically analyzed using R (v4.2.0), SPSS (v25.0) and GraphPad Prism (8.0).
Discussion
In our study, MEX3C can independently affect the prognosis of patients, whether in univariate analysis or multivariate analysis. A noteworthy correlation was observed between elevated MEX3C gene expression and unfavorable prognosis. This discovery implies that targeting MEX3C could hold promise as a potential therapeutic strategy to enhance the prognosis for individuals diagnosed with HCC. Considering the large sample size of ICGC and TCGA databases, we built a prediction model for prognosis, and found that MEX3C can improve the prediction effectiveness of the model. The transwell and sphere formation assays indicated that MEX3C might significantly contribute to suppressing the invasion and stemness of HCC cells. This discovery offers a potential therapeutic avenue for clinical targeted therapy.
Lenvatinib-based combination therapy represents a significant recent breakthrough in HCC treatment, showing ongoing advancements. The combination of TACE and Lenvatinib enhances the objective response rate, outperforming transarterial chemoembolization (TACE) monotherapy [
23]. In recent times, the synergistic application of Lenvatinib and an immune checkpoint inhibitor has yielded substantial outcomes, thus expanding the potential horizons for addressing HCC. However, Lenvatinib resistance is an Important obstacle to maintaining long-term therapeutic effects, and better targets are needed to enhance the killing effect of Lenvatinib. Subsequently, we intend to establish drug-resistant Lenvatinib cell lines and explore the potential impact of MEX3C on the resistance of HCC cells to Lenvatinib.
HCC contains a large number of immune cells, which provide a complex microenvironment for tumorigenesis and development [
13]. Immune checkpoint inhibitors have played a solid role in the treatment of some cancers. Overexpression of immune checkpoint (PD1, PD-L1 and PD-L2) can lead to tumor immune escape [
24,
25]. In the high expression group of MEX3C within HCC tissue, there was a statistically significant increase in the expression of immune checkpoints. The ssGSEA analysis revealed that elevated MEX3C expression is linked to a higher count of MDSCs in both databases. MDSCs have the ability to prompt the differentiation of regulatory T cells and facilitate the development of an immunosuppressive microenvironment surrounding cancer cells [
26]. The accumulation of mononuclear MDSCs (M-MDSCs) is associated with heightened tumorigenicity in a mouse model of liver cancer [
27]. Targeting the MEX3C gene could potentially influence immunosuppressive cells in the tumor microenvironment, including regulatory T cells and MDSCs, potentially enhancing the efficacy of immunotherapy.
Our analysis of pathways involving MEX3C-related genes revealed an enrichment of MEX3C within the TGF-β signaling pathway. The TGF-β signaling pathway assumes a pivotal role in both fibrosis and immune regulation within the HCC microenvironment [
28]. TGF-β signaling can be exploited by cancer cells to reshape the immune microenvironment and foster the development of “immune evasion.“ Upon stimulation by TGF-β factor, the tumor microenvironment becomes abundant in collagen fibers, leading to the exclusion of T cells by fibroblasts at the tumor periphery. Concurrent inhibition of PDL1 and TGF-β in clinical contexts heightens tumor responsiveness to immunotherapy, resulting in diminished tumor volume [
29]. Despite these findings, our study exhibits certain limitations, necessitating additional in vivo experiments for the substantiation of our conclusions. The absence of co-culture experiments to assess the impact of MEX3C on immune infiltration is notable. Additionally, due to the nature of the database, protein-level data is unavailable.
Conclusions
Overall, our study encompassed an exhaustive exploration of MEX3C’s biological functionality, elucidating its potential utility as a prognostic marker for patients as well as its immunomodulatory influence. MEX3C exhibits associations with various immunosuppressive pathways, including TGF-β, MDSCs, and Tregs, while also augmenting the tumor-suppressive impact of Lenvatinib. These findings introduce a novel avenue for prospective immune combination strategies.
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