ANXA10 is a prognostic biomarker and suppressor of hepatocellular carcinoma: a bioinformatics analysis and experimental validation

Identification of KRT16 and ANXA10 as cell cycle regulation genes for lung adenocarcinoma based on self-transcriptome sequencing of surgical samples and TCGA public data mining

AIM

This study aimed to identify the genes associated with the development of lung adenocarcinoma (LUAD) and potential therapeutic targets.

METHODS

Differentially expressed genes (DEGs) were identified by self-transcriptome sequencing of tumor tissues and paracancerous tissues resected during surgery and combined with The Cancer Genome Atlas (TCGA) data to screen for the genes associated with LUAD prognosis. The expression was validated at mRNA and protein levels, and the gene knockdown was used to examine the impact and underlying mechanisms on lung cancer cells.

RESULTS

A total of 227 DEGs were identified by transcriptome sequencing, and the 20 DEGs with the most significant differences were used for co-analysis with TCGA data. The findings suggested that KRT16 and ANXA10 might have an important role in the development of LUAD after validating the mRNA and protein expression levels at the cellular level. The knockdown of KRT16 and ANXA10 inhibited the proliferation of lung cancer cells, and the cell cycle was blocked in the G1 phase. The expression of the G1/S-phase cell cycle checkpoint-related proteins cyclin D1 and cyclin E was inhibited by KRT16 and ANXA10 knockdown, respectively. The tumor formation ability decreased after KRT16 or ANXA10 knockdown in vivo.

CONCLUSIONS

KRT16 and ANXA10 are potential genes regulating the development of LUAD. Also, they may be potential targets for the targeted therapy of LUAD by inhibiting the proliferation of lung cancer cells and blocking the cell cycle by affecting key protein expression levels at cell cycle checkpoints.

Analysis and Validation of Tyrosine Metabolism-related Prognostic Features for Liver Hepatocellular Carcinoma Therapy

AIMS

To explore tyrosine metabolism-related characteristics in liver hepatocellular carcinoma (LIHC) and to establish a risk signature for the prognostic prediction of LIHC. Novel prognostic signatures contribute to the mining of novel biomarkers, which are essential for the construction of a precision medicine system for LIHC and the improvement of survival.

BACKGROUND

Tyrosine metabolism plays a critical role in the initiation and development of LIHC. Based on the tyrosine metabolism-related characteristics in LIHC, this study developed a risk signature to improve the prognostic prediction of patients with LIHC.

OBJECTIVE

To investigate the correlation between tyrosine metabolism and progression of LIHC and to develop a tyrosine metabolism-related prognostic model.

METHODS

Gene expression and clinicopathological information of LIHC were obtained from The Cancer Genome Atlas (TCGA) database. Distinct subtypes of LIHC were classified by performing consensus cluster analysis on the tyrosine metabolism-related genes. Univariate and Lasso Cox regression were used to develop a RiskScore prognosis model. Kaplan-Meier (KM) survival analysis with log-rank test and area under the curve (AUC) of receiver operating characteristic (ROC) were employed in the prognostic evaluation and prediction validation. Immune infiltration, tyrosine metabolism score, and pathway enrichment were evaluated using single-sample gene set enrichment analysis (ssGSEA). Finally, a nomogram model was developed with the RiskScore and other clinicopathological features.

RESULTS

Based on the tyrosine metabolism genes in the TCGA cohort, we identified 3 tyrosine metabolism-related subtypes showing significant prognostic differences. Four candidate genes selected from the common differentially expressed genes (DEGs) between the 3 subtypes were used to develop a RiskScore model, which could effectively divide LIHC patients into high- and lowrisk groups. In both the training and validation sets, high-risk patients tended to have worse overall survival, less active immunotherapy response, higher immune infiltration and clinical grade, and higher oxidative, fatty, and xenobiotic metabolism pathways. Multivariate analysis confirmed that the RiskScore was an independent indicator for the prognosis of LIHC. The results from pan-- cancer analysis also supported that the RiskScore had a strong prognostic performance in other cancers. The nomogram demonstrated that the RiskScore contributed the most to the prediction of LIHC prognosis.

CONCLUSION

Our study developed a tyrosine metabolism-related risk model that performed well in survival prediction, showing the potential to serve as an independent prognostic predictor for LIHC treatment.

Pyroptosis-related genes features on prediction of the prognosis in liver cancer: An integrated analysis of bulk and single-cell RNA sequencing

Objective

This study explores the impact of pyroptosis-related genes (PRG) on the prognosis of liver cancer (LC).

Methods

421 samples (371 tumor samples and 50 normal samples) from the Cancer Genome Atlas (TCGA) were included in this study. GSE14520 dataset (data of RNA expression and relevant clinicopathological features), GSE125449 dataset (single-cell data in LC) and HCCDB18 dataset (validation on the reliability of the model) were downloaded as appropriate. Download the PRG and its corresponding pathway information from the gene set enrichment analysis (GSEA) website. The consensus clustering was performed by ConsensusClusterPlus package. Differentially expressed genes (DEGs) were identified using limma package, and prognostic features were constructed using un/multivariate and Lasso Cox regression. Pathway enrichment analysis was conducted by ssGSEA method. Receiver Operating Characteristic and the survival analysis were conducted by timeROC and Survminer packages. The Seurat package was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validation, following the quantification on the key genes via reverse-transcription quantitative PCR, the Transwell and scratch assays were applied to evaluate the in-vitro invasion and migration of LC cells Huh-7.

Results

12 prognosis-related genes were identified to be related to the progression of LC. Three subtypes including C1, C2 and C3 were categorized using the 12 prognosis-related genes and PRGs significantly related to the prognosis of LC patients. The worst and best prognosis was seen in C3 subtype and C2 subtype, respectively. Hallmark pathway enrichment analysis has shown the concurrent immunoactivation and immune escape in C3 subtype. A RiskScore model was constructed using 8 key genes (KPNA2, UCK2, FTCD, CBX2, RAB32, HMMR, S100A9 and ANXA10) from the DEGs of three subtypes. The RiskScore system as an independent prognostic factor dividing the patients into high and low risk groups, and patients of the high-risk group had poor prognosis in both test set and validation set. A nomogram model combining the risk score had the extreme higher benefit. Further, 6 subclusters were identified from scRNA-seq analysis, where the highest PYROPTOSIS score was seen in Monocytic-Macrophages. The quantification on the key genes has suggested the high expressions of KPNA2, UCK2, CBX2, RAB32, HMMR and S100A9 and the low expressions of FTCD and ANXA10 in LC cells Huh-7. Particularly, UCK2 knockdown evidently diminished the number of invaded and migrated LC cells in vitro.

Conclusion

The risk model associated with pyproptosis is crucial for the tumor immunity of LC and may serve as a prognostic indicator for patients suffering from LC. Our findings will offer new perspectives for immunotherapies targeting LC.

In vitro screening of understudied PFAS with a focus on lipid metabolism disruption

Per- and polyfluoroalkyl substances (PFAS) are man-made chemicals used in many industrial applications. Exposure to PFAS is associated with several health risks, including a decrease in infant birth weight, hepatoxicity, disruption of lipid metabolism, and decreased immune response. We used the in vitro cell models to screen six less studied PFAS [perfluorooctane sulfonamide (PFOSA), perfluoropentanoic acid (PFPeA), perfluoropropionic acid (PFPrA), 6:2 fluorotelomer alcohol (6:2 FTOH), 6:2 fluorotelomer sulfonic acid (6:2 FTSA), and 8:2 fluorotelomer sulfonic acid (8:2 FTSA)] for their capacity to activate nuclear receptors and to cause differential expression of genes involved in lipid metabolism. Cytotoxicity assays were run in parallel to exclude that observed differential gene expression was due to cytotoxicity. Based on the cytotoxicity assays and gene expression studies, PFOSA was shown to be more potent than other tested PFAS. PFOSA decreased the gene expression of crucial genes involved in bile acid synthesis and detoxification, cholesterol synthesis, bile acid and cholesterol transport, and lipid metabolism regulation. Except for 6:2 FTOH and 8:2 FTSA, all tested PFAS downregulated PPARA gene expression. The reporter gene assay also showed that 8:2 FTSA transactivated the farnesoid X receptor (FXR). Based on this study, PFOSA, 6:2 FTSA, and 8:2 FTSA were prioritized for further studies to confirm and understand their possible effects on hepatic lipid metabolism.

Evolutionary screening of precision oncology biomarkers and its applications in prognostic model construction

Biomarker screening is critical for precision oncology. However, one of the main challenges in precision oncology is that the screened biomarkers often fail to achieve the expected clinical effects and are rarely approved by regulatory authorities. Considering the close association between cancer pathogenesis and the evolutionary events of organisms, we first explored the evolutionary feature underlying clinically approved biomarkers, and two evolutionary features of approved biomarkers (Ohnologs and specific evolutionary stages of genes) were identified. Subsequently, we utilized evolutionary features for screening potential prognostic biomarkers in four common cancers: head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Finally, we constructed an evolution-strengthened prognostic model (ESPM) for cancers. These models can predict cancer patients' survival time across different cancer cohorts effectively and perform better than conventional models. In summary, our study highlights the application potentials of evolutionary information in precision oncology biomarker screening.

Advances in prognostic models for osteosarcoma risk

The risk prognosis model is a statistical model that uses a set of features to predict whether an individual will develop a specific disease or clinical outcome. It can be used in clinical practice to stratify disease severity and assess risk or prognosis. With the advancement of large-scale second-generation sequencing technology, along Prognosis models for osteosarcoma are increasingly being developed as large-scale second-generation sequencing technology advances and clinical and biological data becomes more abundant. This expansion greatly increases the number of prognostic models and candidate genes suitable for clinical use. This article will present the predictive effects and reliability of various prognosis models, serving as a reference for their evaluation and application.