Suppression of optineurin impairs the progression of hepatocellular carcinoma through regulating mitophagy

Mitophagy at the crossroads of cancer development: Exploring the role of mitophagy in tumor progression and therapy resistance

Preserving a functional mitochondrial network is crucial for cellular well-being, considering the pivotal role of mitochondria in ensuring cellular survival, especially under stressful conditions. Mitophagy, the selective removal of damaged mitochondria through autophagy, plays a pivotal role in preserving cellular homeostasis by preventing the production of harmful reactive oxygen species from dysfunctional mitochondria. While the involvement of mitophagy in neurodegenerative diseases has been thoroughly investigated, it is becoming increasingly evident that mitophagy plays a significant role in cancer biology. Perturbations in mitophagy pathways lead to suboptimal mitochondrial quality control, catalyzing various aspects of carcinogenesis, including establishing metabolic plasticity, stemness, metabolic reconfiguration of cancer-associated fibroblasts, and immunomodulation. While mitophagy performs a delicate balancing act at the intersection of cell survival and cell death, mounting evidence indicates that, particularly in the context of stress responses induced by cancer therapy, it predominantly promotes cell survival. Here, we showcase an overview of the current understanding of the role of mitophagy in cancer biology and its potential as a target for cancer therapy. Gaining a more comprehensive insight into the interaction between cancer therapy and mitophagy has the potential to reveal novel targets and pathways, paving the way for enhanced treatment strategies for therapy-resistant tumors in the near future.

Unraveling the Janus-Faced Role of Autophagy in Hepatocellular Carcinoma: Implications for Therapeutic Interventions

This review aims to provide a comprehensive understanding of the molecular mechanisms underlying autophagy and mitophagy in hepatocellular carcinoma (HCC). Autophagy is an essential cellular process in maintaining cell homeostasis. Still, its dysregulation is associated with the development of liver diseases, including HCC, which is one of leading causes of cancer-related death worldwide. We focus on elucidating the dual role of autophagy in HCC, both in tumor initiation and progression, and highlighting the complex nature involved in the disease. In addition, we present a detailed analysis of a small subset of autophagy- and mitophagy-related molecules, revealing their specific functions during tumorigenesis and the progression of HCC cells. By understanding these mechanisms, we aim to provide valuable insights into potential therapeutic strategies to manipulate autophagy effectively. The goal is to improve the therapeutic response of liver cancer cells and overcome drug resistance, providing new avenues for improved treatment options for HCC patients. Overall, this review serves as a valuable resource for researchers and clinicians interested in the complex role of autophagy in HCC and its potential as a target for innovative therapies aimed to combat this devastating disease.

Combining bulk and single-cell RNA-sequencing data to develop an NK cell-related prognostic signature for hepatocellular carcinoma based on an integrated machine learning framework

BACKGROUND

The application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients' prognosis and immunotherapeutic response.

METHODS

NK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA-LIHC data set, GSE14520 data set, GSE76427 data set and ICGC-LIRI-JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set.

RESULTS

With the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients' prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS-PD1 blocker, IPS-CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group.

CONCLUSIONS

Overall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.

Insights from a Computational-Based Approach for Analyzing Autophagy Genes across Human Cancers

In the last decade, there has been a boost in autophagy reports due to its role in cancer progression and its association with tumor resistance to treatment. Despite this, many questions remain to be elucidated and explored among the different tumors. Here, we used omics-based cancer datasets to identify autophagy genes as prognostic markers in cancer. We then combined these findings with independent studies to further characterize the clinical significance of these genes in cancer. Our observations highlight the importance of innovative approaches to analyze tumor heterogeneity, potentially affecting the expression of autophagy-related genes with either pro-tumoral or anti-tumoral functions. In silico analysis allowed for identifying three genes (TBC1D12, KERA, and TUBA3D) not previously described as associated with autophagy pathways in cancer. While autophagy-related genes were rarely mutated across human cancers, the expression profiles of these genes allowed the clustering of different cancers into three independent groups. We have also analyzed datasets highlighting the effects of drugs or regulatory RNAs on autophagy. Altogether, these data provide a comprehensive list of targets to further the understanding of autophagy mechanisms in cancer and investigate possible therapeutic targets.

A signature based on five immune-related genes to predict the survival and immune characteristics of neuroblastoma

BACKGROUND

MYCN amplification (MNA) has been proved to be related to poor prognosis in neuroblastoma (NBL), but the MYCN-related immune signatures and genes remain unclear.

METHODS

Enrichment analysis was used to identify the significant enrichment pathways of differentially expressed immune-related genes (DEIRGs). Weight gene coexpression network analysis (WGCNA) was applied to reveal the correlation between these DEIRGs and MYCN status. Univariate and multivariate Cox analyses were used to construct risk model. The relevant fractions of immune cells were evaluated by CIBERSORT and single-sample gene set enrichment analysis (ssGSEA).

RESULTS

Five genes, including CHGA, PTGER1, SHC3, PLXNC1, and TRIM55 were enrolled into the risk model. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve showed that our model performed well in predicting the outcomes of NBL (3-years AUC = 0.720, 5-year AUC = 0.775, 10-years AUC = 0.782), which has been validated in the GSE49711 dataset and the E-MTAB-8248 dataset. By comparing with the tumor immune dysfunction and exclusion (TIDE) and tumor inflammation signature (TIS), we further proved that our model is reliable. Univariate and multivariate Cox regression analyses indicated that the risk score, age, and MYCN can serve as independent prognostic factors in the E-MATB-8248. Functional enrichment analysis showed the DEIRGs were enriched in leukocyte adhesion-related signaling pathways. Gene set enrichment analysis (GSEA) revealed the significantly enriched pathways of the five MYCN-related DEIRGs. The risk score was negatively correlated with the immune checkpoint CD274 (PD-L1) but no significant difference with the TMB. We also confirmed the prognostic value of our model in predicting immunotherapeutics.

CONCLUSION

We constructed and verified a signature based on DEIRG that related to MNA and predicted the survival of NBL based on relevant immune signatures. These findings could provide help for predicting prognosis and developing immunotherapy in NBL.

The prognostic value and clinical significance of mitophagy-related genes in hepatocellular carcinoma

Background: Mitophagy has been found to play a significant part in the cancer process in a growing number of studies in recent years. However, there is still a lack of study on mitophagy-related genes' (MRGs) prognostic potential and clinical significance in hepatocellular carcinoma (HCC). Methods: We employed bioinformatics and statistical knowledge to examine the transcriptome data of HCC patients in the TCGA and GEO databases, with the goal of constructing a multigene predictive model. Then, we separated the patients into high- and low-risk groups based on the score. The model's dependability was determined using principal components analysis (PCA), survival analysis, independent prognostic analysis, and receiver operating characteristic (ROC) analysis. Following that, we examined the clinical correlations, pharmacological treatment sensitivity, immune checkpoint expression, and immunological correlations between patients in high and low risk groups. Finally, we evaluated the variations in gene expression between high- and low-risk groups and further analyzed the network core genes using protein-protein interaction network analysis. Results: Prognostic models were built using eight genes (OPTN, ATG12, CSNK2A2, MFN1, PGAM5, SQSTM1, TOMM22, TOMM5). During validation, the prognostic model demonstrated high reliability, indicating that it could accurately predict the prognosis of HCC patients. Additionally, we discovered that typical HCC treatment medicines had varying impacts on patients classified as high or low risk, and that individuals classified as high risk are more likely to fail immunotherapy. Additionally, the high-risk group expressed more immunological checkpoints. The immunological status of patients in different risk categories varies as well, and patients with a high-risk score have a diminished ability to fight cancer. Finally, PPI analysis identified ten related genes with potential for research. Conclusion: Our prognostic model had good and reliable predictive ability, as well as clinical diagnosis and treatment guiding significance. Eight prognostic MRGs and ten network core genes merited further investigation.

Changes in the Proteome in the Development of Chronic Human Papillomavirus Infection-A Prospective Study in HIV Positive and HIV Negative Rwandan Women

BACKGROUND

Effects on the proteome when a high risk (HR)-HPV infection occurs, when it is cleared and when it becomes chronic were investigated. Moreover, biomarker panels that could identify cervical risk lesions were assessed.

METHODS

Cytology, HPV screening and proteomics were performed on cervical samples from Rwandan HIV+ and HIV- women at baseline, at 9 months, at 18 months and at 24 months. Biological pathways were identified using the String database.

RESULTS

The most significantly affected pathway when an incident HR-HPV infection occurred was neutrophil degranulation, and vesicle-mediated transport was the most significantly affected pathway when an HR-HPV infection was cleared; protein insertion into membrane in chronic HR-HPV lesions and in lesions where HR-HPVs were cleared were compared; and cellular catabolic process in high-grade lesions was compared to that in negative lesions. A four-biomarker panel (EIF1; BLOC1S5; LIMCH1; SGTA) was identified, which was able to distinguish chronic HR-HPV lesions from cleared HR-HPV/negative lesions (sensitivity 100% and specificity 91%). Another four-biomarker panel (ERH; IGKV2-30; TMEM97; DNAJA4) was identified, which was able to distinguish high-grade lesions from low-grade/negative lesions (sensitivity 100% and specificity 81%).

CONCLUSIONS

We have identified the biological pathways triggered in HR-HPV infection, when HR-HPV becomes chronic and when cervical risk lesions develop. Moreover, we have identified potential biomarkers that may help to identify women with cervical risk lesions.