A prognostic exosome-related LncRNA risk model correlates with the immune microenvironment in liver cancer

Ferroptosis-related LncRNAs in diseases

Ferroptosis is a form of regulated cell death (RCD) caused by the accumulation of intracellular iron and lipids and is involved in many pathological processes, including neurodegenerative and cardiovascular diseases, and cancer. Long non-coding RNAs (lncRNAs), RNA molecules exceeding 200 nt in length that do not possess protein coding function can interfere with ferroptosis by binding ferroptosis-related miRNAs or proteins. Recently, ferroptosis-related lncRNAs (FRlncRNAs) have been identified in cancer and non-malignant disease models, including inprediction of drug resistance, intra-tumoral immune infiltration, metabolic reprogramming and mutation landscape. Here, we review FRlncRNAs in cancer and non-malignant diseases, from prognosis to treatment.

Influencing immunity: role of extracellular vesicles in tumor immune checkpoint dynamics

Immune checkpoint proteins (ICPs) serve as critical regulators of the immune system, ensuring protection against damage due to overly activated immune responses. However, within the tumor environment, excessive ICP activation weakens antitumor immunity. Despite the development of numerous immune checkpoint blockade (ICB) drugs in recent years, their broad application has been inhibited by uncertainties about their clinical efficacy. A thorough understanding of ICP regulation in the tumor microenvironment is essential for advancing the development of more effective and safer ICB therapies. Extracellular vesicles (EVs), which are pivotal mediators of cell-cell communication, have been extensively studied and found to play key roles in the functionality of ICPs. Nonetheless, a comprehensive review summarizing the current knowledge about the crosstalk between EVs and ICPs in the tumor environment is lacking. In this review, we summarize the interactions between EVs and several widely studied ICPs as well as their potential clinical implications, providing a theoretical basis for further investigation of EV-related ICB therapeutic approaches.

Extracellular Vesicle-Related Non-Coding RNAs in Hepatocellular Carcinoma: An Overview

Hepatocellular carcinoma (HCC) is the most common primary liver cancer. It is a major public health problem worldwide, and it is often diagnosed at advanced stages, when no effective treatment options are available. Extracellular vesicles (EVs) are nanosized double-layer lipid vesicles containing various biomolecule cargoes, such as lipids, proteins, and nucleic acids. EVs are released from nearly all types of cells and have been shown to play an important role in cell-to-cell communication. In recent years, many studies have investigated the role of EVs in cancer, including HCC. Emerging studies have shown that EVs play primary roles in the development and progression of cancer, modulating tumor growth and metastasis formation. Moreover, it has been observed that non-coding RNAs (ncRNAs) carried by tumor cell-derived EVs promote tumorigenesis, regulating the tumor microenvironment (TME) and playing critical roles in the progression, angiogenesis, metastasis, immune escape, and drug resistance of HCC. EV-related ncRNAs can provide information regarding disease status, thus encompassing a role as biomarkers. In this review, we discuss the main roles of ncRNAs present in HCC-derived EVs, including micro(mi) RNAs, long non-coding (lnc) RNAs, and circular (circ) RNAs, and their potential clinical value as biomarkers and therapeutic targets.

An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer

Background

Ovarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.

Methods

420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.

Results

64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.

Conclusion

Our study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.

Risk factors of primary liver cancer initiation associated with tumour initiating cell emergence: novel targets for promising preventive therapies

Primary liver cancers ranked as the sixth most commonly diagnosed cancers and the third-leading cause of cancer-related death in 2020. Despite encouraging findings on diagnosis and treatments, liver cancer remains a life-threatening disease with a still increasing incidence. Therefore, it is of interest to better characterise and understand the mechanistic process occurring at early steps of carcinogenesis. Inflammatory responses in liver diseases participate in the activation of liver progenitor cells (LPCs) facultative compartment but also to their transformation into cancer stem cells (CSCs) and give rise to primary liver cancer including hepatocellular carcinoma and cholangiocarcinoma. Higher intratumoural heterogeneity has been associated with poorer prognosis and linked to tumour escape from the immune surveillance and to resistance to chemotherapy. A better understanding of the malignant transformation of LPC as tumour initiating cells (ie, CSC) should also provide a potential new therapeutic target for anticancer therapy. In this review, we summarise the recent reports identifying underlying mechanisms by which chronic liver inflammatory responses could trigger the early steps in liver carcinogenesis, notably through the transformation of LPCs into tumour initiating cells.