Knockdown of ALPK2 blocks development and progression of renal cell carcinoma

Comprehensive analysis of signaling lymphocyte activation molecule family as a prognostic biomarker and correlation with immune infiltration in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common type of kidney cancer and accounts for 2-3% of all cancer cases. Furthermore, a growing number of immunotherapy approaches are being used in antitumor treatment. Signaling lymphocyte activation molecule family (SLAMF) members have been well studied in several cancers, whereas their roles in ccRCC have not been investigated. The present study comprehensively assessed the molecular mechanisms of SLAMF members in ccRCC, performed using The Cancer Genome Atlas database, with analysis of gene transcription, prognosis, biological function, clinical features, tumor-associated immune cells and the correlation with programmed cell death protein 1/programmed death-ligand 1 immune checkpoints. Simultaneously, the Tumor Immune Dysfunction and Exclusion algorithm was used to predict the efficacy of immune checkpoint blockade (ICB) therapy in patients with high and low SLAMF expression levels. The results demonstrated that all SLAMF members were highly expressed in ccRCC, and patients with high expression levels of SLAMF1, 4, 7 and 8 had a worse prognosis that those with low expression. SLAMF members were not only highly associated with immune activation but also with immunosuppressive agents. The level of immune cell infiltration was associated with the prognosis of patients with ccRCC with high SLAMF expression. Moreover, high ICB response rates were observed in patients with high expression levels of SMALF1 and 4. In summary, SLAMF members may serve as future potential biomarkers for predicting the prognosis of ccRCC and emerge as a novel immunotherapy target.

Fisetin enhances cisplatin sensitivity in renal cell carcinoma via the CDK6/PI3K/Akt/mTOR signaling pathway

Cisplatin resistance is ubiquitous among patients with renal cell carcinoma (RCC). The present study assessed the role of fisetin in regulating cisplatin sensitivity and increasing the efficacy of chemotherapy for patients with RCC. Cell Counting Kit-8 and colony formation assays were used to assess the proliferation of RCC cells after fisetin and cisplatin treatment. The mRNA expression levels of cyclin-dependent kinase (CDK)6 were evaluated using reverse transcription-quantitative PCR. The expression levels of CDK6 and key proteins of the PI3K/Akt/mTOR signaling pathway were assessed using western blotting. The present study demonstrated that fisetin inhibited the proliferation and colony-forming ability of RCC cells, and induced apoptosis and cell cycle arrest in a dose-dependent manner. Additionally, fisetin enhanced the antineoplastic effects of cisplatin, as demonstrated by the increase in proliferation inhibition and apoptosis promotion after fisetin and cisplatin combination treatment. Furthermore, fisetin regulated the PI3K/Akt/mTOR signaling pathway through CDK6 inhibition, which enhanced cisplatin sensitivity. Overexpression of CDK6 neutralized the positive effects of fisetin on the improvement of cisplatin sensitivity in RCC cells. In conclusion, fisetin may enhance the sensitivity of RCC cells to cisplatin via the CDK6/PI3K/Akt/mTOR signaling pathway.

Whole-Exome Sequencing (WES) Reveals Novel Sex-Specific Gene Variants in Non-Alcoholic Steatohepatitis (MASH)

Non-alcoholic steatohepatitis (NASH, also known as MASH) is a severe form of non-alcoholic fatty liver disease (NAFLD, also known as MASLD). Emerging data indicate that the progression of the disease to MASH is higher in postmenopausal women and that genetic susceptibility increases the risk of MASH-related cirrhosis. This study aimed to investigate the association between genetic polymorphisms in MASH and sexual dimorphism. We applied whole-exome sequencing (WES) to identify gene variants in 8 age-adjusted matched pairs of livers from both male and female patients. Sequencing alignment, variant calling, and annotation were performed using standard methods. Polymerase chain reaction (PCR) coupled with Sanger sequencing and immunoblot analysis were used to validate specific gene variants. cBioPortal and Gene Set Enrichment Analysis (GSEA) were used for actionable target analysis. We identified 148,881 gene variants, representing 57,121 and 50,150 variants in the female and male cohorts, respectively, of which 251 were highly significant and MASH sex-specific (p < 0.0286). Polymorphisms in CAPN14, SLC37A3, BAZ1A, SRP54, MYH11, ABCC1, and RNFT1 were highly expressed in male liver samples. In female samples, Polymorphisms in RGSL1, SLC17A2, HFE, NLRC5, ACTN4, SBF1, and ALPK2 were identified. A heterozygous variant 1151G>T located on 18q21.32 for ALPK2 (rs3809983) was validated by Sanger sequencing and expressed only in female samples. Immunoblot analysis confirmed that the protein level of β-catenin in female samples was 2-fold higher than normal, whereas ALPK2 expression was 0.5-fold lower than normal. No changes in the protein levels of either ALPK2 or β-catenin were observed in male samples. Our study suggests that the perturbation of canonical Wnt/β-catenin signaling observed in postmenopausal women with MASH could be the result of polymorphisms in ALPK2.

Alpha Protein Kinase 2 Promotes Esophageal Cancer via Integrin Alpha 11

Background

As a common disease around the world, esophageal cancer (EC) primarily includes two subclasses: esophageal adenocarcinoma and esophageal squamous cell carcinoma. Mortality has been rising over the years; hence, exploring the mechanism of EC development has become critical. Among the alpha protein kinases, alpha protein kinase 2 (ALPK2) presumably has a connection with EC, but it has never been revealed before.

Methods

In this study, IHC analysis was used for ALPK2 expression quantification in ES tissues. TE-1 and Eca-109, which are both human EC cell lines, were used for in vitro analysis of cell proliferation, migration, apoptosis, and colony formation.

Results

ALPK2 was found to have an abundant expression within EC tissues (P < 0.001), as well as in the two selected human EC cell lines (P < 0.05). The data showed that ALPK2 depletion suppressed EC cell proliferation, migration, and colony formation, meanwhile stimulating apoptosis (P < 0.001). The in vivo experiments also displayed inhibitory effects caused by ALPK2 depletion on EC tumorigenesis (P < 0.001). It was further validated that ALPK2 depletion made the phosphorylation of Akt and mTOR, as well as CDK6 and PIK3CA levels downregulated (P < 0.001). Mechanistically, we identified integrin alpha 11 (ITGA11) as a downstream gene of ALPK2 regulating EC. More importantly, we found that ITGA11 elevation promoted cell proliferation and migration and rescued the suppression effects caused by ALPK2 depletion (P < 0.001).

Conclusions

ALPK2 promotes esophageal cancer via integrin its downstream gene alpha 11; ALPK2 can potentially act as a target for the treatment of EC.

Hsa_circ_0065217 promotes growth and metastasis of renal cancer through regulating the miR-214-3p-ALPK2 axis

Circular RNA (circRNA) deregulation impacts on normal cell physiology leading to malignant phenotypic changes. Here, we determined the function of the circRNA, hsa_circ_0065217 in malignant renal cell carcinoma (RCC). Hsa_circ_0065217 was abundantly expressed in RCC tissue and cell lines, and its expression linked to advanced TNM stages, large tumor sizes, and lymph-node metastasis. Hsa_circ_0065217 silencing reduced in vitro RCC cell-line growth and aggressiveness. Mechanistically, hsa_circ_0065217 promoted alpha protein kinase 2 (ALPK2) expression via its competing endogenous RNA (ceRNA) activity toward miR-214-3p. Moreover, ALPK2 overexpression reversed hsa_circ_0065217 knockdown effects on RCC cell-line malignancy. Thus, hsa_circ_0065217/miR-214-3p/ALPK2 signaling putatively promotes RCC tumorigenesis and is a putative RCC treatment target.

Stratified neural networks in a time-to-event setting

Deep neural networks are frequently employed to predict survival conditional on omics-type biomarkers, e.g., by employing the partial likelihood of Cox proportional hazards model as loss function. Due to the generally limited number of observations in clinical studies, combining different data sets has been proposed to improve learning of network parameters. However, if baseline hazards differ between the studies, the assumptions of Cox proportional hazards model are violated. Based on high dimensional transcriptome profiles from different tumor entities, we demonstrate how using a stratified partial likelihood as loss function allows for accounting for the different baseline hazards in a deep learning framework. Additionally, we compare the partial likelihood with the ranking loss, which is frequently employed as loss function in machine learning approaches due to its seemingly simplicity. Using RNA-seq data from the Cancer Genome Atlas (TCGA) we show that use of stratified loss functions leads to an overall better discriminatory power and lower prediction error compared to their non-stratified counterparts. We investigate which genes are identified to have the greatest marginal impact on prediction of survival when using different loss functions. We find that while similar genes are identified, in particular known prognostic genes receive higher importance from stratified loss functions. Taken together, pooling data from different sources for improved parameter learning of deep neural networks benefits largely from employing stratified loss functions that consider potentially varying baseline hazards. For easy application, we provide PyTorch code for stratified loss functions and an explanatory Jupyter notebook in a GitHub repository.

Whole exome sequencing characterization of individuals presenting extreme phenotypes of high and low risk of developing tobacco-induced lung adenocarcinoma

Background

Tobacco is the main risk factor for developing lung cancer. Yet, some heavy smokers do not develop lung cancer at advanced ages while others develop it at young ages. Here, we assess for the first time the genetic background of these clinically relevant extreme phenotypes using whole exome sequencing (WES).

Methods

We performed WES of germline DNA from heavy smokers who either developed lung adenocarcinoma at an early age (extreme cases, n=50) or did not present lung adenocarcinoma or other tumors at an advanced age (extreme controls, n=50). We selected non-synonymous variants located in exonic regions and consensus splice sites of the genes that showed significantly different allelic frequencies between both cohorts. We validated our results in all the additional extreme cases (i.e., heavy smokers who developed lung adenocarcinoma at an early age) available from The Cancer Genome Atlas (TCGA).

Results

The mean age for the extreme cases and controls was respectively 49.7 and 77.5 years. Mean tobacco consumption was 43.6 and 56.8 pack-years. We identified 619 significantly different variants between both cohorts, and we validated 108 of these in extreme cases selected from TCGA. Nine validated variants, located in relevant cancer related genes, such as PARP4, HLA-A or NQO1, among others, achieved statistical significance in the False Discovery Rate test. The most significant validated variant (P=4.48×10⁻⁵) was located in the tumor-suppressor gene ALPK2.

Conclusions

We describe genetic variants associated with extreme phenotypes of high and low risk for the development of tobacco-induced lung adenocarcinoma. Our results and our strategy may help to identify high-risk subjects and to develop new therapeutic approaches.