The Prognostic and Molecular Landscape of Autophagy-Related Long Noncoding RNA in Colorectal Cancer

Deciphering the impact of aggregated autophagy-related genes TUBA1B and HSP90AA1 on colorectal cancer evolution: a single-cell sequencing study of the tumor microenvironment

BACKGROUND

Colorectal cancer (CRC) is the third most prevalent cancer worldwide, with the tumor microenvironment (TME) playing a crucial role in its progression. Aggregated autophagy (AA) has been recognized as a factor that exacerbates CRC progression. This study aims to study the relationship between aggregated autophagy and CRC using single-cell sequencing techniques. Our goal is to explain the heterogeneity of the TME and to explore the potential for targeted personalized therapies.

OBJECTIVE

To study the role of AA in CRC, we employed single-cell sequencing to discern distinct subpopulations within the TME. These subpopulations were characterized by their autophagy levels and further analyzed to identify specific biological processes and marker genes.

RESULTS

Our study revealed significant correlations between immune factors and both clinical and biological characteristics of the tumor microenvironment (TME), particularly in cells expressing TUBA1B and HSP90AA1. These immune factors were associated with T cell depletion, a reduction in protective factors, diminished efficacy of immune checkpoint blockade (ICB), and enhanced migration of cancer-associated fibroblasts (CAFs), resulting in pronounced inflammation. In vitro experiments showd that silencing TUBA1B and HSP90AA1 using siRNA (Si-TUBA1B and Si-HSP90AA1) significantly reduced the expression of IL-6, IL-7, CXCL1, and CXCL2 and inhibition of tumor cell growth in Caco-2 and Colo-205 cell lines. This reduction led to a substantial alleviation of chronic inflammation and highlighted the heterogeneous nature of the TME.

CONCLUSION

This study marks an initial foray into understanding how AA-associated processes may potentiate the TME and weaken immune function. Our findings provide insights into the complex dynamics of the TME and highlight potential targets for therapeutic intervention, suggesting a key role for AA in the advancement of colorectal cancer.

Unravelling the complexity of lncRNAs in autophagy to improve potential cancer therapy

Autophagy is well-known as an internal catabolic process that is evolutionarily conserved and performs the key biological function in maintaining cellular homeostasis. It is tightly controlled by several autophagy-related (ATG) proteins, which are closely associated with many types of human cancers. However, what has remained controversial is the janus roles of autophagy in cancer progression. Interestingly, the biological function of long non-coding RNAs (lncRNAs) in autophagy has been gradually understood in different types of human cancers. More recently, numerous studies have demonstrated that several lncRNAs may regulate some ATG proteins and autophagy-related signaling pathways to either activate or inhibit the autophagic process in cancer. Thus, in this review, we summarize the latest advance in the knowledge of the complicated relationships between lncRNAs and autophagy in cancer. Also, the in-depth dissection of the lncRNAs-autophagy-cancers axis involved in this review would shed new light on discovery of more potential cancer biomarkers and therapeutic targets in the future.

An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients

Purpose

Pancreatic cancer (PC) is a common, highly lethal cancer with a low survival rate. Autophagy is involved in the occurrence and progression of PC. This study aims to explore the feasibility of using an autophagy-related long noncoding RNA (lncRNA) signature for assessing PC patient survival.

Methods

We obtained RNA sequencing and clinical data of patients from the TCGA website. Autophagy genes were obtained from the Human Autophagy Database. The prognostic model, generated through univariate and multivariate Cox regression analyses, included 10 autophagy-related lncRNAs. Receiver operating characteristic (ROC) curves and forest plots were generated for univariate and multivariate Cox regression analyses, to examine the predictive feasibility of the risk model. Gene set enrichment analysis (GSEA) was used to screen enriched gene sets.

Results

Twenty-eight autophagy-related lncRNAs were filtered out through univariate Cox regression analysis (P < 0.001). Ten autophagy-related lncRNAs, including 4 poor prognosis factors and 6 beneficial prognosis factors, were further screened via multivariate Cox regression analysis. The AUC value of the ROC curve was 0.815. GSEA results demonstrated that cancer-related gene sets were significantly enriched.

Conclusion

A signature based on ten autophagy-related lncRNAs was identified. This signature could be potentially used for evaluating clinical prognosis and might be used for targeted therapy against PC.