ITPKA expression is a novel prognostic factor in hepatocellular carcinoma

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.

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.

Relationship of ITPKA expression with the prognosis of breast cancer

BACKGROUND

Breast cancer (BC) represents a most common cancer among women worldwide. The outcomes of this disease remain dismal due to frequent recurrence and metastasis. Inositol-1,4,5-trisphosphate-3-kinase-A (ITPKA) plays an important role in regulating calcium signaling and actin dynamics. The dysregulation of ITPKA has been observed in several human cancers. The present study aimed to assess ITPKA expression and its prognostic value in BC.

METHODS

ITPKA expression was examined via quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) methods. In addition, Kaplan-Meier survival analysis and Cox regression analysis were performed to evaluate prognostic value of ITPKA in BC.

RESULTS

Upregulated ITPKA expression was found in BC samples, according to both qRT-PCR and IHC analyses (all p < .05). ITPKA expression was positively correlated with lymph node metastasis (p = .021) and TNM stage (p = .009). Moreover, BC patients with high expression of ITPKA had poor overall survival compared with those with low expression (log-rank p < .05). Cox analysis verified that ITPKA expression was an independent prognostic factor for BC patients (HR = 4.239, 95%CI = 2.221-8.093 and p = .000).

CONCLUSION

BC cases show increased expression of ITPKA. ITPKA may act as an independent prognostic biomarker in BC.

An eleven metabolic gene signature-based prognostic model for clear cell renal cell carcinoma

In this study, we performed bioinformatics and statistical analyses to investigate the prognostic significance of metabolic genes in clear cell renal cell carcinoma (ccRCC) using the transcriptome data of 539 ccRCC and 72 normal renal tissues from TCGA database. We identified 79 upregulated and 45 downregulated (n=124) metabolic genes in ccRCC tissues. Eleven prognostic metabolic genes (NOS1, ALAD, ALDH3B2, ACADM, ITPKA, IMPDH1, SCD5, FADS2, ACHE, CA4, and HK3) were identified by further analysis. We then constructed an 11-metabolic gene signature-based prognostic risk score model and classified ccRCC patients into high- and low-risk groups. Overall survival (OS) among the high-risk ccRCC patients was significantly shorter than among the low-risk ccRCC patients. Receiver operating characteristic (ROC) curve analysis of the prognostic risk score model showed that the areas under the ROC curve for the 1-, 3-, and 5-year OS were 0.810, 0.738, and 0.771, respectively. Thus, our prognostic model showed favorable predictive power in the TCGA and E-MTAB-1980 ccRCC patient cohorts. We also established a nomogram based on these eleven metabolic genes and validated internally in the TCGA cohort, showing an accurate prediction for prognosis in ccRCC.

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

Background

Renal cell cancer (RCC) is one of the most lethal malignancies of the kidney in adults. mTOR (mammalian target of rapamycin) signaling pathway plays a pivotal role in RCC tumorigenesis and progression and inhibitors targeting the mTOR pathway have been widely used in advanced RCC treatment. Therefore, it is of great significance to explore the potential regulators of the mTOR pathway as RCC therapeutic targets.

Materials and Methods

Bioinformatics analysis was used to screen out the most significant differentially expressed genes in the RCC dataset of The Cancer Genome Atlas (TCGA). Real-time PCR and Western-blot analysis were utilized to examine the expression of inositol-1,4,5-trisphosphate-3-kinase-A (ITPKA) in four RCC cell lines and one human embryonic kidney cell line. Cell counting Kit-8 and colony formation assay were performed to estimate the effect of ITPKA on the proliferation ability of RCC cells. Wound healing and Transwell assays were used to test the effect of ITPKA on RCC cell migration and invasion. Xenograft formation assay was performed in nude mice to investigate the effect of ITPKA in vivo. mTORC1 pathway inhibitor was added to explore the mechanisms by which ITPKA regulates RCC cell growth and progression.

Results

Based on bioinformatics analysis, ITPKA is screened out as one of the most significant differentially expressed genes in RCC. ITPKA is upregulated and positively correlated with RCC malignancy and poorer prognosis. ITPKA promotes RCC growth, migration and invasion in cultured cells, and accelerates tumor growth in nude mice. Mechanistically, ITPKA stimulates the mTORC1 signaling pathway which is a requirement for ITPKA modulation of RCC cell proliferation, migration and invasion.

Conclusion

Our data demonstrate a critical regulatory role of the ITPKA in RCC and suggest that ITPKA/mTORC1 axis may be a promising target for diagnosis and treatment of RCC.

TFAP2A Induced ITPKA Serves as an Oncogene and Interacts with DBN1 in Lung Adenocarcinoma

The inositol polyphosphate kinase (IPK) family member ITPKA (inositol 1,4,5-trisphosphate 3-kinase) regulates the levels of many inositol polyphosphates which are important in cellular signaling. Several recent studies reported the aberrant expression of ITPKA in malignancy disease and usually made cancer more aggressive. However, the contribution of the inositol polyphosphate kinase ITPKA to lung cancer development remains unclear.

Here we report that ITPKA is overexpressed in lung adenocarcinoma (LUAD) and positively correlated with advanced clinical parameters. ITPKA contributes to the malignant phenotypes in-vitro. Mechanistically, ITPKA executed its action through the inducting of epithelial-mesenchymal transition (EMT) and interacting with Drebrin 1 (which is related to cancer metastasis). Moreover, the hyper-expression of ITPKA in LUAD is transcriptionally activated by the transcription factor TFAP2A. In survival analysis by using tissue microarray (TMA), we indicate that ITPKA is hyper-expressed in LUAD tissues compared to adjacent normal tissues, and increased expression of ITPKA is associated with poor prognosis.

Collectively, this study indicates that TFAP2A induced ITPKA hyperexpression promotes LUAD via interacting with Drebrin 1 and activating epithelial-mesenchymal transition (EMT). ITPKA might represent a potent candidate for the treatment and prognostic prediction of LUAD.

Inositol-1,4,5-trisphosphate 3-kinase-A (ITPKA) is frequently over-expressed and functions as an oncogene in several tumor types

At present targeted tumor therapy is based on inhibition of proteins or protein mutants that are up-regulated in tumor but not in corresponding normal cells. The actin bundling Inositol-trisphosphate 3-kinase A (ITPKA) belongs to such molecular targets. ITPKA is expressed in a broad range of tumor types but shows limited expression in normal cells. In lung and breast cancer expression of ITPKA is stimulated by gene body methylation which increases with increasing malignancy of these tumors but is not detectable in the corresponding normal tissues. Since ITPKA gene body methylation occurs early in tumor development, it could serve as biomarker for early detection of lung cancer. Detailed mechanistic studies revealed that down-regulation of ITPKA in lung adenocarcinoma cancers reduced both, tumor growth and metastasis. It is assumed that tumor growth is stimulated by the InsP₃Kinase activity of ITPKA and metastasis by its actin bundling activity. A selective inhibitor against the InsP₃Kinase activity of ITPKA has been identified but compounds inhibiting the actin bundling activity are not available yet. Since no curative therapy option for metastatic lung or breast tumors exist, therapies that block activities of ITPKA may offer new options for patients with these tumors. Thus, efforts should be made to develop clinical drugs that selectively target InsP₃Kinase activity as well as actin bundling activity of ITPKA.