lncCRLA enhanced chemoresistance in lung adenocarcinoma that underwent epithelial-mesenchymal transition

Long non-coding RNAs in ferroptosis, pyroptosis and necroptosis: from functions to clinical implications in cancer therapy

As global population ageing accelerates, cancer emerges as a predominant cause of mortality. Long non-coding RNAs (lncRNAs) play crucial roles in cancer cell growth and death, given their involvement in regulating downstream gene expression levels and numerous cellular processes. Cell death, especially non-apoptotic regulated cell death (RCD), such as ferroptosis, pyroptosis and necroptosis, significantly impacts cancer proliferation, invasion and metastasis. Understanding the interplay between lncRNAs and the diverse forms of cell death in cancer is imperative. Modulating lncRNA expression can regulate cancer onset and progression, offering promising therapeutic avenues. This review discusses the mechanisms by which lncRNAs modulate non-apoptotic RCDs in cancer, highlighting their potential as biomarkers for various cancer types. Elucidating the role of lncRNAs in cell death pathways provides valuable insights for personalised cancer interventions.

The GJB3 correlates with the prognosis, immune cell infiltration, and therapeutic responses in lung adenocarcinoma

Gap junction protein beta 3 (GJB3) has been reported as a tumor suppressor in most tumors. However, its role in lung adenocarcinoma (LUAD) remains unknown. The purpose of this study is to explore the role of GJB3 in the prognosis and tumor microenvironment of LUAD patients. The data used in this study were acquired from The Cancer Genome Atlas, Gene Expression Omnibus, and imvigor210 cohorts. We found that GJB3 expression was increased in LUAD patients and correlated with LUAD stages. LUAD patients with high GJB3 expression exhibited a worse prognosis. A total of 164 pathways were significantly activated in the GJB3 high group. GJB3 expression was positively associated with nine transcription factors and might be negatively regulated by hsa-miR-6511b-5p. Finally, we found that immune cell infiltration and immune checkpoint expression were different between the GJB3 high and GJB3 low groups. In summary. GJB3 demonstrated high expression levels in LUAD patients, and those with elevated GJB3 expression displayed unfavorable prognoses. Additionally, there was a correlation between GJB3 and immune cell infiltration, as well as immune checkpoint expression in LUAD patients.

The fate and function of non-coding RNAs during necroptosis

Necroptosis is a novel form of cell death which is activated when apoptotic cell death signals are disrupted. Accumulating body of observations suggests that noncoding RNAs, which are the lately discovered mystery of the human genome, are significantly associated with necroptotic signaling circuitry. The fate and function of miRNAs have been well documented in human disease, especially cancer. Recently, lncRNAs have gained much attention due to their diverse regulatory functions. Although available studies are currently based on bioinformatic analysis, predicted interactions desires further attention, as these hold significant promise and should not be overlooked. In the light of these, here we comprehensively review and discuss noncoding RNA molecules that play significant roles during execution of necroptotic cell death.

Unraveling the intriguing interplay: Exploring the role of lncRNAs in caspase-independent cell death

Cell death plays a crucial role in various physiological and pathological processes. Until recently, programmed cell death was mainly attributed to caspase-dependent apoptosis. However, emerging evidence suggests that caspase-independent cell death (CICD) mechanisms also contribute significantly to cellular demise. We and others have reported and functionally characterized numerous long noncoding RNAs (lncRNAs) that modulate caspase-dependent apoptotic pathways potentially in a pathway-dependent manner. However, the interplay between lncRNAs and CICD pathways has not been comprehensively documented. One major reason for this is that most CICD pathways have been recently discovered with some being partially characterized at the molecular level. In this review, we discuss the emerging evidence that implicates specific lncRNAs in the regulation and execution of CICD. We summarize the diverse mechanisms through which lncRNAs modulate different forms of CICD, including ferroptosis, necroptosis, cuproptosis, and others. Furthermore, we highlight the intricate regulatory networks involving lncRNAs, protein-coding genes, and signaling pathways that orchestrate CICD in health and disease. Understanding the molecular mechanisms and functional implications of lncRNAs in CICD may unravel novel therapeutic targets and diagnostic tools for various diseases, paving the way for innovative strategies in disease management and personalized medicine. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Exosomal lncCRLA is predictive for the evolvement and development of lung adenocarcinoma

Lung adenocarcinoma, the most common histological subtype of non-small cell lung cancer, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Our team uncovers that lncRNA related to chemotherapy resistance in lung adenocarcinoma (lncCRLA) is preferentially expressed in lung adenocarcinoma cells with the mesenchymal phenotype. lncCRLA can not enhance chemotherapy resistance in lung adenocarcinoma due to its binding to RIPK1 in exosomes, which is released into intercellular media and transferred by exosomes from mesenchymal-like to epithelial-like cells. However, plasmatic lncCRLA corresponding to tissue lncCRLA functions as a preferred biomarker to reflect the response to chemotherapy and disease progression of lung adenocarcinoma. Through single-cell sequencing, RNA-Mutect technique and spatial transcriptomics, a handful of hybrid EMT cells with elevated lncCRLA are characterized as the origin of lung adenocarcinoma, which are indiscriminated from hybrid EMT cells by the in-depth sequencing. Plasmatic lncCRLA is properly predictive for the preinvasive lesion of lung adenocarcinoma that would evolve to invasive lesion. That notion is confirmed by a brand-new transgenic mouse model in which EMT is tracked by Cre and Dre system. Dasatinib is potential to hinder the spontaneous progression from preinvasive to invasive lesion of lung adenocarcinoma. Together, plasmatic lncCRLA is defined as a brand-new circulating biomarker to predict the occurrence and evolvement of lung adenocarcinoma, a light for early detection of lung adenocarcinoma.

Long non-coding RNAs in non-small cell lung cancer: implications for preventing therapeutic resistance

Lung cancer has the highest mortality and morbidity rates among all cancers worldwide. Despite many complex treatment options, including radiotherapy, chemotherapy, targeted drugs, immunotherapy, and combinations of these treatments, efficacy is low in cases of resistance to therapy, metastasis, and advanced disease, contributing to low overall survival. There is a pressing need for the discovery of novel biomarkers and therapeutic targets for the early diagnosis of lung cancer and to determine the efficacy and outcomes of drug treatments. There is now substantial evidence for the diagnostic and prognostic value of long noncoding RNAs (lncRNAs). This review briefly discusses recent findings on the roles and mechanisms of action of lncRNAs in the responses to therapy in non-small cell lung cancer.

Drug resistance related genes in lung adenocarcinoma predict patient prognosis and influence the tumor microenvironment

Lung adenocarcinoma (LUAD) is the predominant type of non-small lung cancer (NSCLC) with strong invasive ability and poor prognosis. The drug resistance related genes are potentially associated with prognosis of LUAD. Our research aimed to identify the drug resistance related genes and explore their potential prognostic value in LUAD patients. The data used in this study were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Firstly, we screened drug resistance related genes in LUAD by differential gene analysis, univariate Cox regression and drug sensitivity analyses. Subsequently, we constructed a risk score model using LASSO Cox regression analysis, and verified whether the risk score can predict the survival of LUAD patients independent of other factors. Moreover, we explored the immune infiltration of 22 immune cells between high-risk and low-risk patients. Totally 10 drug-resistance positively related genes (PLEK2, TFAP2A, KIF20A, S100P, GDF15, HSPB8, SASH1, WASF3, LAMA3 and TCN1) were identified in LUAD. The risk score model of LUAD constructed with these 10 genes could reliably predict the prognosis of LUAD patients. 18 pathways were significantly activated in high-risk group compared with low-risk group. In addition, the infiltration proportion of multiple immune cells was significantly different between high-risk and low-risk groups, and the proportion of M1 phagocytes was significantly higher in the high-risk group compared with the low-risk group. The drug resistance related genes (PLEK2, TFAP2A, KIF20A, S100P, GDF15, HSPB8, SASH1, WASF3, LAMA3 and TCN1) could predict the prognosis of LUAD patients. Clarifying the roles and mechanisms of these 10 genes in regulating drug resistance in LUAD will help to improve individualized clinical treatment protocols and predict patient sensitivity to treatment.

Necroptosis-related lncRNAs: Combination of bulk and single-cell sequencing reveals immune landscape alteration and a novel prognosis stratification approach in lung adenocarcinoma

Necroptosis, which is recently recognized as a form of programmed cell death, plays a critical role in cancer biology, including tumorigenesis and cancer immunology. It was recognized not only to defend against tumor progression by suppressing adaptive immune responses but also to promote tumorigenesis and cancer metastasis after recruiting inflammatory responses. Thus the crucial role of necrosis in tumorigenesis has attracted increasing attention. Due to the heterogeneity of the tumor immune microenvironment (TIME) in lung adenocarcinoma (LUAD), the prognosis and the response to immunotherapy vary distinctly across patients, underscoring the need for a stratification algorithm for clinical practice. Although previous studies have formulated the crucial role of lncRNAs in tumorigenicity, the relationship between necroptosis-related lncRNAs, TIME, and the prognosis of patients with LUAD was still elusive. In the current study, a robust and novel prognostic stratification model based on Necroptosis-related LncRNA Risk Scoring (NecroLRS) and clinicopathological parameters was constructed and systemically validated in both internal and external validation cohorts. The expression profile of four key lncRNAs was further validated by qRT-PCR in 4 human LUAD cell lines. And a novel immune landscape alteration was observed between NecroLRS-High and -Low patients. To further elucidate the mechanism of necroptosis in the prognosis of LUAD from a single-cell perspective, a novel stratification algorithm based on K-means clustering was introduced to extract both malignant and NecroLRS-High subsets from epithelial cells. And the necroptosis-related immune infiltration landscape and developmental trajectory were investigated respectively. Critically, NecroLRS was found to be positively correlated with neutrophil enrichment, inflammatory immune response, and malignant phenotypes of LUAD. In addition, novel ligand-receptor pairs between NecroLRS-High cells and other immunocytes were investigated and optimal therapeutic compounds were screened to provide potential targets for future studies. Taken together, our findings reveal emerging mechanisms of necroptosis-induced immune microenvironment alteration on the deteriorative prognosis and may contribute to improved prognosis and individualized precision therapy for patients with LUAD.

Macrophage Infiltration Initiates RIP3/MLKL-Dependent Necroptosis in Paclitaxel-Induced Neuropathic Pain

Paclitaxel (PTX) is a commonly used antitumor drug. Approximately 80% of all patients receiving PTX chemotherapy develop chemotherapy-induced peripheral neuropathy (CIPN), limiting the use of PTX. Moreover, CIPN responds poorly to conventional analgesics. Experimental evidence suggests that the neuroinflammatory response plays an essential role in paclitaxel-induced peripheral neuropathy (PIPN). Previous studies have confirmed that dorsal root ganglion (DRG) neuron necroptosis and accompanying inflammation are linked with PIPN; however, the potential upstream regulatory mechanisms remain unclear. Preclinical studies have also established that macrophage infiltration in the DRG is associated with PIPN. TNF-α released by activated macrophages is the primary regulatory signal of necroptosis. In this study, we established a rat model of PIPN via quartic PTX administration (accumulated dose: 8 mg/kg, i.p.). The regulatory effect of macrophage infiltration on necroptosis in PIPN was observed using a macrophage scavenging agent (clodronate disodium). The results showed that PTX increased macrophage infiltration and the levels of TNF-α and IL-1β in the DRG. PTX also upregulated the levels of necroptosis-related proteins, including receptor-interacting protein kinase (RIP3) and mixed-lineage kinase domain-like protein (MLKL) in DRG neurons and promoted MLKL phosphorylation, resulting in neuronal necrosis and hyperalgesia. In contrast, clodronate disodium effectively removed macrophages, reduced the levels of RIP3, MLKL, and pMLKL, and decreased the number of necrotic cells in the DRG of PIPN rats, alleviating the behavioral pain abnormalities. These results suggest that PTX promotes macrophage infiltration, which results in the release of TNF-α and IL-1β in the DRG and the initiation of neuronal necroptosis via the RIP3/MLKL pathway, ultimately leading to neuropathic pain.

Identification and Validation of the lncRNA MYOSLID as a Regulating Factor of Necroptosis and Immune Cell Infiltration in Colorectal Cancer following Necroptosis-Related LncRNA Model Establishment

Simple Summary

Colorectal cancer is one of the most common cancers and the second leading cause of deaths due to cancer. In this study, we developed a neural model based on only four lncRNAs to predict the overall survival rate of colorectal cancer patients. Moreover, we validated the value of analysing the lncRNA MYOSLID, one of the hub lncRNAs in our model, which promotes colorectal cancer by regulating necroptosis. Our study offered some essential insights into predicting the prognosis of colorectal cancer patients and may help to assist diagnosis and treatment in the future.

Abstract

Necroptosis is a newly defined form of programmed cell death that plays an important role in cancers. However, necroptosis-related lncRNAs (NRLs) involved in colorectal cancer (CRC) have not yet been thoroughly studied. Methods: In this study, a 4-NRL model was developed based on the least absolute shrinkage and selection operator (LASSO) algorithm. A series of informatic, in vitro and in vivo analyses were applied to validate the prognostic value of the model and the potential function of the hub lncRNA MYOSLID. Results: The model exhibited an excellent capacity for the prediction of overall survival and other clinicopathological features of CRC patients using Kaplan–Meier (K–M) survival curves and receiver operating characteristic (ROC) curves. Furthermore, a significant difference in the levels of immune cells, such as CD4 memory T cells and activated mast cells, between two risk groups was observed. The low-risk patients had a higher expression of immune checkpoints, such as PDCD1 (PD-1) and CD274 (PD-L1). The levels of MYOSLID, a hub lncRNA in our model, were higher in CRC tissues than in normal tissues. Knockdown of MYOSLID induced necroptosis and inhibited the proliferation of CRC cells in vitro and in vivo. Interestingly, knockdown of MYOSLID also increased the percentage of CD4⁺ and CD8⁺ T cells in subcutaneously transplanted tumours. Conclusion: Our model is a promising biomarker that can be used to predict clinical outcomes in CRC patients, and MYOSLID plays an important role in regulating necroptosis and immune cell infiltration in CRC.

Identification of Prognostic Signature of Necroptosis-Related lncRNAs and Molecular Subtypes in Glioma

Background

In tumor progression and epigenetic regulation, long non-coding RNA (lncRNA) and necroptosis are crucial regulators. However, in glioma microenvironment, the role of necroptosis-related lncRNAs (NRLs) remains unknown.

Method

In this study, the RNA-seq and clinical annotation of glioma patients were analyzed using the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. To investigate prognosis and tumor microenvironment of NRLs in gliomas, we conducted a prediction model based on the training cohort. The accuracy of the model was verified in the verification cohort.

Results

A signature composed of 13 NRLs was identified, and all glioma patients were divided into two groups. We found that each group has unique survival outcomes, biological behaviors, and immune infiltrating status. The necroptosis-related lncRNA signature (NRLS) model was found to be an independent risk factor in multivariate Cox analysis. Immunosuppressive microenvironment was positively correlated with the high-risk group. Due to significantly different IC50 between risk groups, NRLS could be used as a guide for chemotherapeutic treatment. Further, the entire cohort was divided into two clusters depending on NRLs. Consensus clustering method and the risk scoring system were basically similar. Survival probability was higher in Cluster 2, while Cluster 1 has stronger immunologic infiltration.

Conclusion

The predictive signature could be a prognostic factor independently and serve to detect the role of NRLs in glioma immunotherapy response.

Necroptosis Identifies Novel Molecular Phenotypes and Influences Tumor Immune Microenvironment of Lung Adenocarcinoma

This study aims to investigate the immune and epigenetic mutational landscape of necroptosis in lung adenocarcinoma (LUAD), identify novel molecular phenotypes, and develop a prognostic scoring system based on necroptosis regulatory molecules for a better understanding of the tumor immune microenvironment (TIME) in LUAD. Based on the Cancer Genome Atlas and Gene Expression Omnibus database, a total of 29 overlapped necroptosis-related genes were enrolled to classify patients into different necroptosis phenotypes using unsupervised consensus clustering. We systematically correlated the phenotypes with clinical features, immunocyte infiltrating levels, and epigenetic mutation characteristics. A novel scoring system was then constructed, termed NecroScore, to quantify necroptosis of LUAD by principal component analysis. Three distinct necroptosis phenotypes were confirmed. Two clusters with high expression of necroptosis-related regulators were “hot tumors”, while another phenotype with low expression was a “cold tumor”. Molecular characteristics, including mutational frequency and types, copy number variation, and regulon activity differed significantly among the subtypes. The NecroScore, as an independent prognostic factor (HR=1.086, 95%CI=1.040-1.133, p<0.001), was able to predict the survival outcomes and show that patients with higher scores experienced a poorer prognosis. It could also evaluate the responses to immunotherapy and chemotherapeutic efficiency.

In conclusion, necroptosis-related molecules are correlated with genome diversity in pan-cancer, playing a significant role in forming the TIME of LUAD. Necroptosis phenotypes can distinguish different TIME and molecular features, and the NecroScore is a promising biomarker for predicting prognosis, as well as immuno- and chemotherapeutic benefits in LUAD.

Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs

Background

In reference to previous studies, necroptosis played an important role in cancer development. Our team decided to explore the potential prognostic values of long non-coding RNAs (lncRNAs) associated with necroptosis in bladder urothelial carcinoma (BLCA) and their relationship with the tumor microenvironment (TME) and the immunotherapeutic response for accurate dose.

Methods

To obtain the required data, bladder urothelial carcinoma transcriptome data were searched from Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). We used co-expression analysis, differential expression analysis, and univariate Cox regression to screen out prognostic lncRNAs associated with necroptosis in BLCA. Then the least absolute shrinkage and selection operator (LASSO) was conducted to construct the necroptosis-associated lncRNAs model. Based on this model, we also performed the Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC) to estimate the prognostic power of risk score. Multivariate and univariate Cox regression analysis were performed to build up a nomogram. Calibration curves, and time-dependent ROC were also conducted to evaluate nomogram. Principal component analysis (PCA) revealed a difference between high- and low-risk groups. In addition, we explored immune analysis, gene set enrichment analyses (GSEA), and evaluation of the half-maximal inhibitory concentration (IC50) in constructed model. Finally, the entire samples were divided into three clusters based on model of necroptosis-associated lncRNAs to further compare immunotherapy in cold and hot tumors.

Results

A model was built up based on necroptosis-associated lncRNAs. The model revealed good consistence between calibration plots and prognostic prediction. The area of 1-, 3-, and 5-year OS under the ROC curve (AUC) were 0.707, 0.679, and 0.675. Risk groups could be helpful for systemic therapy due to the markedly diverse IC50 between risk groups. To our delight, clusters could effectively identify cold and hot tumors, which would be beneficial to accurate mediation. Clusters 2 and 3 were considered the hot tumor, which was more sensitive to immunotherapeutic drugs.

Conclusions

The outcomes of our study suggested that necroptosis-associated lncRNAs could effectively predict patients with BLCA prognosis, which may be helpful for distinguishing the cold and hot tumors and improving individual treatment of BLCA.

Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma

Model algorithms were used in constructing the risk coefficient model of necroptosis-related long non-coding RNA in identifying novel potential biomarkers in the prediction of the sensitivity to chemotherapeutic agents and prognosis of patients with lung adenocarcinoma (LUAD). Clinic and transcriptomic data of LUAD were obtained from The Cancer Genome Atlas. Differently expressed necroptosis-related long non-coding RNAs got identified by performing both the univariate and co-expression Cox regression analyses. Subsequently, the least absolute shrinkage and selection operator technique was adopted in constructing the nrlncRNA model. We made a comparison of the areas under the curve, did the count of the values of Akaike information criterion of 1-year, 2-year, as well as 3-year receiver operating characteristic curves, after which the cut-off value was determined for the construction of an optimal model to be used in identifying high risk and low risk patients. Genes, tumor-infiltrating immune cells, clinical correlation analysis, and chemotherapeutic agents data of both the high-risk and low-risk subgroups were also performed. We identified 26 DEnrlncRNA pairs, which were involved in the Cox regression model constructed. The curve areas under survival periods of 1 year, 2 years, and 3 years of patients with LUAD were 0.834, 0.790, and 0.821, respectively. The cut-off value set was 2.031, which was used in the identification of either the high-risk or low-risk patients. Poor outcomes were observed in patients belonging to the high-risk group. The risk score was the independent predictor of the LUAD outcome (p < 0.001). The expression levels of immune checkpoint and infiltration of specific immune cells were anticipated by the gene risk model. The high-risk group was found to be highly sensitive to docetaxel, erlotinib, cisplatin, and paclitaxel. The model established through nrlncRNA pairs irrespective of the levels of expression could give a prediction on the LUAD patients’ prognosis and assist in identifying the patients who might gain more benefit from chemotherapeutic agents.

Clinicopathological and Prognostic Value of Necroptosis-Associated lncRNA Model in Patients with Kidney Renal Clear Cell Carcinoma

Background. Necroptosis, a recently identified type of programmed necrotic cell death, is closely related to the tumorigenesis and development of cancer. However, it remains unclear whether necroptosis-associated long noncoding RNAs (lncRNAs) can be used to predict the prognosis of kidney renal clear cell carcinoma (KIRC). This work was designed to probe the possible prognostic worth of necroptosis-associated lncRNAs along with their impact on the tumor microenvironment (TME) in KIRC. Methods. The Cancer Genome Atlas (TCGA) database was used to extract KIRC gene expression and clinicopathological data. Pearson correlation analysis was used to evaluate necroptosis-associated lncRNAs against 159 known necroptosis-associated genes. To define molecular subtypes, researchers used univariate Cox regression analysis and consensus clustering, as well as clinical significance, TME, and tumor immune cells in each molecular subtype. We develop the necroptosis-associated lncRNA prognostic model using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients were divided into high- and low-risk groups according to prognostic model. Moreover, comprehensive analyses, including prognostic value, gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint gene expression, were performed between the two risk groups. Finally, anticancer drug sensitivity analyses were employed for assessing associations for necroptosis-associated lncRNA expression profile and anticancer drug chemosensitivity. Results. Through univariate analysis, sixty-nine necroptosis-associated lncRNAs were found to have a significant relationship with KIRC prognosis. Two molecular clusters were identified, and significant differences were found with respect to clinicopathological features and prognosis. The segregation of patients into two risk groups was done by the constructed necroptosis-associated lncRNA model. The survival prognosis, clinical features, degree of immune cell infiltration, and expression of immune checkpoint genes of high-risk and low-risk groups were all shown to vary. Conclusions. Our study identified a model of necroptosis-associated lncRNA signature and revealed its prognostic role in KIRC. It is expected to provide a reference for the screening of KIRC prognostic markers and the evaluation of immune response.