ITPKA1 Promotes Growth, Migration and Invasion of Renal Cell Carcinoma via Activation of mTOR Signaling Pathway

A multifunctional antibody fusion protein 57103 targeting CD24, IL-4R, and αvβ3 for treating cancer and regulating the tumor microenvironment

Cancer is one of the top 10 fatal diseases worldwide, among which advanced metastatic carcinoma has the highest mortality rate. Sunitinib and immune checkpoint blockers are commonly used to treat metastatic renal carcinoma with limited efficacy. Therefore, there is an urgent need to develop novel targeted therapies for metastatic renal cancer. In this study, we designed an antibody fusion protein, 57103, that simultaneously targeted the cluster of differentiation 24 (CD24), interleukin 4 receptor (IL-4R), and integrin receptors αvβ3 and α5β1. In vitro assays showed that 57103 significantly suppressed the proliferation, migration, invasion, colony formation, and adhesion abilities of renal cancer cells, resulting in a comprehensive and significant antitumor effect. Furthermore, 57103 inhibited angiogenesis, promoted THP1-derived M0-type macrophage phagocytosis, and enhanced the antibody-dependent cellular cytotoxicity of peripheral blood mononuclear and NK92MI-CD16a cells. In vivo experiments revealed significant inhibition of tumor growth in ACHN cell xenograft nude mice and an MC38-hCD24 tumor-bearing mouse model. Immunohistochemical analysis showed that 57103 decreased the proliferation and induced the apoptosis of renal cancer cells, while inhibiting angiogenesis. The MC38-hPDL1 and MC38-hCD24-hPDL1 tumor-bearing mouse models further offer the possibility of combining 57103 with the PDL1 antagonist atezolizumab. In conclusion, 57103 is a potential candidate drug for the treatment of metastatic renal carcinoma or PDL1-overexpressing cancer.

The prognostic value of cancer stage-associated genes in clear cell renal cell carcinoma

OBJECTIVES

Clear cell renal cell carcinoma (ccRCC) is a highly prevalent subtype of malignant renal tumor, but unfortunately, the survival rate remains unsatisfactory. The aim of the present study is to explore genomic features that are correlated with cancer stage, allowing for the identification of subgroups of ccRCC patients with high risk of unfavorable outcomes and enabling prompt intervention and treatment.

METHODS

We compared the gene expression levels across ccRCC patients with diverse cancer stages from The Cancer Genome Atlas (TCGA) database, which revealed characteristic genes associated with tumor stage. We then extracted prognostic genes and used least absolute shrinkage selection operator (LASSO) regression to select four genes for feature extraction and the construction of a prognostic risk model.

RESULTS

We have identified a total of 171 differentially expressed genes (DEGs) that are closely linked to the tumor stage of ccRCC through difference analysis. A prognostic risk model constructed based on the expression levels of ZIC2, TFAP2A-AS1, ITPKA, and SLC16A12 holds significant prognostic value in ccRCC. The results of the functional enrichment analysis imply that the DEGs are mainly involved in the regulation of immune-related signaling pathways, and therefore may have a significant function in immune system regulation of ccRCC.

CONCLUSIONS

Our study has successfully identified significant DEGs between high- and low-staging groups of ccRCC using bioinformatics methods. The construction of a prognostic risk model based on the expression levels of ZIC2, TFAP2A-AS1, ITPKA, and SLC16A12 has displayed promising prognostic significance, indicating its valuable potential for clinical application.

Better prediction of clinical outcome in clear cell renal cell carcinoma based on a 6 metabolism-related gene signature

It has been reported that metabolic disorders participate in the formation and progression of clear cell renal cell carcinoma (ccRCC). However, the predictive value of metabolism-related genes (MRGs) in clinical outcome of ccRCC is still largely unknown. Herein, a novel metabolism-related signature was generated to assess the effect of MRGs on the prognosis of ccRCC patients. Important module MRGs were selected by differentially expressed analysis and WGCNA. Subsequently, the hub MRGs were screened via univariate cox regression as well as LASSO regression. A new metabolism-related signature of 6 hub MRGs (PAFAH2, ACADSB, ACADM, HADH, PYCR1 and ITPKA) was constructed, with a good prognostic prediction ability in the TCGA cohort. The prediction accuracy of this signature was further confirmed in both GSE22541 and FAHWMU cohort. Interestingly, this MRG risk signature was highly correlated with tumor mutation burden and immune infiltration in ccRCC. Notably, lower PAFAH2, a member of 6 MRGs, was found in ccRCC. Knockdown of PAFAH2 contributed to renal cancer cell proliferation and migration. Collectively, a 6-MRG prognostic risk signature is generated to estimate the prognostic status of ccRCC patients, providing a novel insight in the prognosis prediction and treatment of ccRCC.

Identification of calcium metabolism related score associated with the poor outcome in papillary thyroid carcinoma

Introduction

Papillary thyroid carcinoma is a type of thyroid cancer that exhibits significant variability in prognosis. Extensive research indicates that the impaired signaling of 1,25(OH)2D3-VDR may be a crucial factor in the development and progression of PTC.

Methods

To investigate this further, Integrated analysis mRNA expression information from The Cancer Genome Atlas and GEO, we compared gene expression in cancer and normal tissues and identified differentially expressed genes (DEGs). Through this analysis, we identified DEGs and calculated risk estimates for seven genetic markers.

Results

Subsequently, we constructed predictive models using LASSO-Cox regression to test the predictive value of these markers. Our results revealed that 64 calcium metabolism-related genes showed significant differences between tumor and normal tissues. Ten of the identified DEGs were significantly associated with overall survival, indicating their potential role in disease progression. Using the average risk score for the seven genetic markers, we divided patients into high- and low-risk groups. We found that patients in the low-risk group had significantly better overall survival than those in the high-risk group, highlighting the importance of these genetic markers in predicting prognosis. Further analysis using Cox regression demonstrated that the risk levels had independent predictive power. Additionally, we conducted functional analysis of the identified genetic markers, which showed significant differences in immune status between the two patient groups. We also investigated the effect of these calcium metabolism-related genes on thyroid cancer biological functions, immune microenvironment, and drug resistance.

Discussion

Our findings provide evidence of a novel genetic signature associated with calcium metabolism, which can predict prognosis in patients with PTC. These results may have significant implications for the development of new diagnostic and therapeutic approaches to improve outcomes for PTC patients.

A novel five-gene metabolism-related risk signature for predicting prognosis and immune infiltration in endometrial cancer: A TCGA data mining

BACKGROUND

Metabolism dysfunction can affect the biological behavior of tumor cells and result in carcinogenesis and the development of various cancers. However, few thoughtful studies focus on the predictive value and efficacy of immunotherapy of metabolism-related gene signatures in endometrial cancer (EC). This research aims to construct a predictive metabolism-related gene signature in EC with prognostic and therapeutic implications.

METHODS

We downloaded the RNA profile and clinical data of 503 EC patients and screened out different expressions of metabolism-related genes with prognosis influence of EC from The Cancer Genome Atlas (TCGA) database. We first established a metabolism-related genes model using univariate and multivariate Cox regression and Lasso regression analysis. To internally validate the predictive model, 503 samples (entire set) were randomly assigned into the test set and the train set. Then, we applied the receiver operating characteristic (ROC) curve to confirm our previous predictive model and depicted a nomogram integrating the risk score and the clinicopathological feature. We employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways of the model. Afterward, we used ESTIMATE to evaluate the TME. Also, we adopted CIBERSORT and ssGSEA to estimate the fraction of immune infiltrating cells and immune function. At last, we investigated the relationship between the predictive model and immune checkpoint genes.

RESULTS

We first constructed a predictive model based on five metabolism-related genes (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA). This model showed the ability to predict EC patients' prognosis accurately and performed well in the train set, test set, and entire set. Then we confirmed the predictive signature was a novel independent prognostic factor in EC patients. In addition, we drew and validated a nomogram to precisely predict the survival rate of EC patients at 1-, 3-, and 5-years (ROC_1-year = 0.714, ROC_3-year = 0.750, ROC_5-year = 0.767). Furthermore, GSEA unveiled that the cell cycle, certain malignant tumors, and cell metabolism were the main biological functions enriched in this identified model. We found the five metabolism-related genes signature was associated with the immune infiltrating cells and immune functions. Most importantly, it was linked with specific immune checkpoints (PD-1, CTLA4, and CD40) that could predict immunotherapy's clinical response.

CONCLUSION

The metabolism-related genes signature (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA) is a valuable index for predicting the survival outcomes and efficacy of immunotherapy for EC in clinical settings.

A metabolism-related gene signature for predicting the prognosis in thyroid carcinoma

Metabolic reprogramming is one of the cancer hallmarks, important for the survival of malignant cells. We investigated the prognostic value of genes associated with metabolism in thyroid carcinoma (THCA). A prognostic risk model of metabolism-related genes (MRGs) was built and tested based on datasets in The Cancer Genome Atlas (TCGA), with univariate Cox regression analysis, LASSO, and multivariate Cox regression analysis. We used Kaplan-Meier (KM) curves, time-dependent receiver operating characteristic curves (ROC), a nomogram, concordance index (C-index) and restricted mean survival (RMS) to assess the performance of the risk model, indicating the splendid predictive performance. We established a three-gene risk model related to metabolism, consisting of PAPSS2, ITPKA, and CYP1A1. The correlation analysis in patients with different risk statuses involved immune infiltration, mutation and therapeutic reaction. We also performed pan-cancer analyses of model genes to predict the mutational value in various cancers. Our metabolism-related risk model had a powerful predictive capability in the prognosis of THCA. This research will provide the fundamental data for further development of prognostic markers and individualized therapy in THCA.

N6-Methyladenosine Methyltransferase METTL14-Mediated Autophagy in Malignant Development of Oral Squamous Cell Carcinoma

N6-methyladenosine (m⁶A) is the most abundant internal mRNA modification in eukaryotes and is related to stability, localization, or translation efficiency in tumorigenesis. Autophagy plays an important role in the occurrence and development of tumours. However, the relationship between m⁶A and autophagy remains unclear. In this study, we used a rapamycin-induced autophagy model of oral squamous cell carcinoma (OSCC) cells, and observed increased m⁶A RNA methylation. When autophagy was activated, the methyltransferase-like 14 (METTL14) expression was upregulated and influenced the proliferation, migration, and invasiveness of OSCC cells. Through meRIP-seq and RNA-seq analysis, we found that METTL14 directly combined with eukaryotic translation initiation factor gamma 1 (eIF4G1) mRNA and decreased its RNA stability. According to the dual-luciferase reporter and mutagenesis assay, the mutated site 1 of exon 11 of eIF4G1 is the key target of METTL14. Knockdown of the main m⁶A binding protein YTHDF2 may rescue the shortened half-life of eIF4G1 mRNA induced by METTL14 overexpression. Furthermore, an in vivo tumour xenograft model confirmed that a high METTL14 expression can effectively reduce OSCC growth. Additionally, using clinical samples, we found that patients with advanced or moderately/poorly differentiated tumours exhibited lower METTL14 levels. Taken together, our results revealed that METTL14 mediated eIF4G1 expression via m⁶A modification and regulated autophagy levels and biological functions in OSCC. Our findings not only expand our understanding of the correlation between autophagy and RNA methylation in tumorigenesis but also present an opportunity to develop new therapeutic options.

N6-methyladenosine demethyltransferase FTO-mediated autophagy in malignant development of oral squamous cell carcinoma

N6-methyladenosine (m⁶A) is the most abundant internal mRNA modification in eukaryotes and plays an important role in tumorigenesis. However, the underlying mechanism remains largely unclear. Here, we established a cell model of rapamycin-induced autophagy to screen m⁶A-modifying enzymes. We found that m⁶A demethylase fat mass and obesity-associated protein (FTO) plays a key role in regulating autophagy and tumorigenesis by targeting the gene encoding eukaryotic translation initiation factor gamma 1 (eIF4G1) in oral squamous cell carcinoma (OSCC). Knocked down of FTO expression in OSCC cell lines, resulting in downregulation of eIF4G1 along with enhanced autophagic flux and inhibition of tumorigenesis. Rapamycin inhibited FTO activity, and directly targeted eIF4G1 transcripts and mediated their expression in an m⁶A-dependent manner. Dual-luciferase reporter and mutagenesis assays confirmed that YTH N6-methyladenosine RNA-binding protein 2 (YTHDF2) targets eIF4G1. Conclusively, after FTO silencing, YTHDF2 captured eIF4G1 transcripts containing m⁶A, resulting in mRNA degradation and decreased expression of eIF4G1 protein, thereby promoting autophagy and reducing tumor occurrence. Therefore, rapamycin may regulate m⁶A levels, determining the autophagic flux of OSCC, thereby affecting the biological characteristics of cancer cells. This insight expands our understanding of the crosstalk between autophagy and RNA methylation in tumorigenesis, which is essential for therapeutic strategy development for OSCC.