A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update

INTRODUCTION

Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma.

AREAS COVERED

The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma.

EXPERT OPINION

While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.

The development and experimental validation of hypoxia-related long noncoding RNAs prognostic signature in predicting prognosis and immunotherapy of cutaneous melanoma

Cutaneous melanoma (CM) is widely acknowledged as a highly aggressive form of malignancy that is associated with a considerable degree of morbidity and poor prognosis. Despite this recognition, the precise role of hypoxia-related long noncoding RNAs (HRLs) in the pathogenesis of CM remains an area of active research. This study sought to elucidate the contribution of HRLs in CM by conducting a thorough screening and extraction of hypoxia-related genes (HRGs). In particular, we conducted univariate and multivariate Cox regression analyses to assess the independence of the prognostic signature of HRLs. Our results demonstrated that a novel risk model could be established based on five prognostic HRLs. Remarkably, patients with low-risk scores exhibited significantly higher overall survival rates compared to their high-risk counterparts, as confirmed by Kaplan-Meier survival analysis. Furthermore, we utilized consensus clustering analysis to categorize CM patients into two distinct subtypes, which revealed marked differences in their prognosis and immune infiltration landscapes. Our nomogram results confirmed that the HRLs prognostic signature served as an independent prognostic indicator, offering an accurate evaluation of the survival probability of CM patients. Notably, our findings from ESTIMATE and ssGSEA analyses highlighted significant disparities in the immune infiltration landscape between low- and high-risk groups of CM patients. Additionally, IPS and TIDE results suggested that CM patients in different risk subtypes may exhibit favorable responses to immunotherapy. Enrichment analysis and GSVA results indicated that immune-related signaling pathways may mediate the role of HRLs in CM. Finally, our tumor mutation burden (TMB) results indicated that patients with low-risk scores had a higher TMB status. In summary, the establishment of a risk model based on HRLs in this study provided an accurate prognostic prediction and correlated with the immune infiltration landscape of CM, thereby providing novel insights for the future clinical management of this disease.

New algorithms based on autophagy-related lncRNAs pairs to predict the prognosis of skin cutaneous melanoma patients

Skin cutaneous melanoma (SKCM) is the most malignant skin tumor for it is enormously easy to develop invasion and metastasis. Autophagy is a process by which cellular material is degraded by lysosomes or vacuoles and recycled. Autophagy-related long non-coding RNAs (lncRNAs) have been thought to correlate with SKCM. This study aims to explore the prognostic significance of autophagy-related lncRNAs and establish a prognostic model of autophagy-related lncRNA pairs in SKCM. Firstly, the RNA-seq data and related clinical information were downloaded from the TCGA database. 446 qualified samples were enrolled. 222 autophagy-related genes were obtained from the HADb database. Pearson correlation analysis was conducted to identify autophagy-related lncRNAs (ARLs). After that, we obtained prognosis-related ARLs and autophagy-related lncRNA pairs (ARLPs). Using Lasso-Cox regression analysis, an autophagy-related lncRNA-pair prognostic signature was established. The accuracy of the signature were confirmed through a series of validations in terms of mutation profiles, immunity infiltration, and cellular pathways. And we used the random forest method to find USP30-AS1 as a key mediating factor in SKCM.

Pyroptosis-related lncRNA prognostic signatures for cutaneous melanoma and tumor microenvironment status

Aims: To explore whether the expression of pyroptosis-related lncRNAs makes a difference in the prognosis and antitumor immunity of cutaneous melanoma (CM) patients. Methods: A series of analyses were conducted to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to further assess the associations among immune status, tumor microenvironment and the prognostic risk model. Results: Eight pyroptosis-related lncRNAs relevant to prognosis were ascertained and applied to establish the prognostic risk model. The low-risk group had a higher overall survival rate. Conclusion: The established prognostic risk model presents better prediction ability for the prognosis of CM patients and provides new possible therapeutic targets for CM.

T2DB: A Web Database for Long Non-Coding RNA Genes in Type II Diabetes

Type II diabetes (T2D) is a growing health problem worldwide due to increased levels of obesity and can lead to other life-threatening diseases, such as cardiovascular and kidney diseases. As the number of individuals diagnosed with T2D rises, there is an urgent need to understand the pathogenesis of the disease in order to prevent further harm to the body caused by elevated blood glucose levels. Recent advances in long non-coding RNA (lncRNA) research may provide insights into the pathogenesis of T2D. Although lncRNAs can be readily detected in RNA sequencing (RNA-seq) data, most published datasets of T2D patients compared to healthy donors focus only on protein-coding genes, leaving lncRNAs to be undiscovered and understudied. To address this knowledge gap, we performed a secondary analysis of published RNA-seq data of T2D patients and of patients with related health complications to systematically analyze the expression changes of lncRNA genes in relation to the protein-coding genes. Since immune cells play important roles in T2D, we conducted loss-of-function experiments to provide functional data on the T2D-related lncRNA USP30-AS1, using an in vitro model of pro-inflammatory macrophage activation. To facilitate lncRNA research in T2D, we developed a web application, T2DB, to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in T2D patients compared to healthy donors or subjects without T2D.

Ferroptosis-related lncRNA signature predicts prognosis and immunotherapy efficacy in cutaneous melanoma

Purpose

Ferroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of this study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM.

Methods

Ferroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. The lncRNA signature was evaluated using the areas under the receiver operating characteristic curves (AUCs) and Kaplan-Meier analyses in the training, testing, and entire cohorts. Multivariate Cox regression analyses including the lncRNA signature and common clinicopathological characteristics were performed to identify independent predictors of overall survival (OS). A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed.

Results

We identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups.

Conclusion

Our novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

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.

Identification of a novel autophagy-related prognostic signature and small molecule drugs for glioblastoma by bioinformatics

OBJECTIVE

To explore the autophagy-related prognostic signature (ARPs) via data mining in gene expression profiles for glioblastoma (GBM).

METHODS

Using the Cancer Genome Atlas (TCGA) database, we obtained 156 GBM samples and 5 adjacent normal samples, and denoted them as discovery cohort. Univariate Cox regression analysis was used to screen autophagy genes that related to GBM prognosis. Then, the least absolute shrinkage and selection operator Cox regression model was used to construct an autophagy-based ARPs, which was validated in an external cohort containing 80 GBM samples. The patients in the above-mentioned cohorts were divided into low-risk group and high-risk group according to the median prognostic risk score, and the diagnostic performance of the model was assessed by receiver operating characteristic curve analyses. The gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were performed between the high-risk and low-risk patients. Additionally, the genetic features of ARPs, such as genetic variation profiles, correlations with tumor-infiltrating lymphocytes (TILs), and potential drug sensitivity, were further assessed in the TCGA-GBM data set.

RESULTS

A signature of ARPs including NDUFB9, BAK1, SUPT3H, GAPDH, CDKN1B, CHMP6, and EGFR were detected and validated. We identified a autophagy-related prognosis 7-gene signature correlated survival prognosis, immune infiltration, level of cytokines, and cytokine receptor in tumor microenvironment. Furthermore, the signature was tested in several pathways related to disorders of tumor microenvironment, as well as cancer-related pathways. Additionally, a range of small molecular drugs, shown to have a potential therapeutic effect on GBM.

CONCLUSIONS

We constructed an autophagy-based 7-gene signature, which could serve as an independent prognostic indicator for cases of GBM and sheds light on the role of autophagy as a potential therapeutic target in GBM.

LINC00520: A Potential Diagnostic and Prognostic Biomarker in Cancer

Long non-coding RNA (lncRNA) is important in the study of cancer mechanisms. LINC00520 is located on human chromosome 14q22.3 and is a highly conserved long non-coding RNA. LINC00520 is widely expressed in various tissues. The expression of LINC00520 is regulated by transcription factors such as Sp1, TFAP4, and STAT3. The high expression of LINC00520 is significantly related to the risk of 11 cancers. LINC00520 can competitively bind 10 miRNAs to promote tumor cell proliferation, invasion, and migration. In addition, LINC00520 is involved in the regulation of P13K/AKT and JAK/STAT signaling pathways. The expression of LINC00520 is significantly related to the clinicopathological characteristics and prognosis of tumor patients and is also related to the sensitivity of HNSCC to radiotherapy. Here, this article summarizes the abnormal expression pattern of LINC00520 in cancer and its potential molecular regulation mechanism and points out that LINC00520 can be used as a potential biomarker for cancer diagnosis, prognosis, and treatment.

Construction of an algorithm based on oncosis-related LncRNAs comprising the molecular subtypes and a risk assessment model in lung adenocarcinoma

Background

As an important non‐apoptotic cell death method, oncosis has been reported to be closely associated with tumors in recent years. However, few research reported the relationship between oncosis and lung cancer.

Methods

In this study, we established an oncosis‐based algorithm comprised of cluster grouping and a risk assessment model to predict the survival outcomes and related tumor immunity of patients with lung adenocarcinomas (LUAD). We selected 11 oncosis‐related lncRNAs associated with the prognosis (CARD8‐AS1, LINC00941, LINC01137, LINC01116, AC010980.2, LINC00324, AL365203.2, AL606489.1, AC004687.1, HLA‐DQB1‐AS1, and AL590226.1) to divide the LUAD patients into different clusters and different risk groups. Compared with patients in clsuter1, patients in cluster2 had a survival advantage and had a relatively more active tumor immunity. Subsequently, we constructed a risk assessment model to distinguish between patients into different risk groups, in which low‐risk patients tend to have a better prognosis. GO enrichment analysis revealed that the risk assessment model was closely related to immune activities. In addition, low‐risk patients tended to have a higher content of immune cells and stromal cells in tumor microenvironment, higher expression of PD‐1, CTLA‐4, HAVCR2, and were more sensitive to immune checkpoint inhibitors (ICIs), including PD‐1/CTLA‐4 inhibitors. The risk score had a significantly positive correlation with tumor mutation burden (TMB). The survival curve of the novel oncosis‐based algorithm suggested that low‐risk patients in cluster2 have the most obvious survival advantage.

Conclusion

The novel oncosis‐based algorithm investigated the prognosis and the related tumor immunity of patients with LUAD, which could provide theoretical support for customized individual treatment for LUAD patients.

Identification of m6A-Related lncRNAs Associated With Prognoses and Immune Responses in Acute Myeloid Leukemia

Background: Acute myeloid leukemia (AML) remains the most common type of hematopoietic malignancy in adults and has an unfavorable outcome. Herein, we aimed to construct an N6-methylandenosine (m6A)-related long noncoding RNAs (lncRNAs) signature to accurately predict the prognosis of patients with AML using the data downloaded from The Cancer Genome Atlas (TCGA) database.

Methods: The RNA-seq and clinical data were obtained from the TCGA AML cohort. First, Pearson correlation analysis was performed to identify the m6A-related lncRNAs. Next, univariate Cox regression analysis was used to determine the candidate lncRNAs with prognostic value. Then, feature selection was carried out by Least absolute shrinkage and selection operator (LASSO) analysis, and seven eligible m6A-related lncRNAs were included to construct the prognostic risk signature. Kaplan–Meier and receiver operating characteristic (ROC) curve analyses were performed to evaluate the predictive capacity of the risk signature both in the training and testing datasets. A nomogram was used to predict 1-year, 2-year, and 3-year overall survival (OS) of AML patients. Next, the expression levels of lncRNAs in the signature were validated in AML samples by qRT-PCR. Functional enrichment analyses were carried out to identify probable biological processes and cellular pathways. The ceRNA network was developed to explore the downstream targets and mechanisms of m6A-related lncRNAs in AML.

Results: Seven m6A-related lncRNAs were identified as a prognostic signature. The low-risk group hold significantly prolonged OS. The nomogram showed excellent accuracy of the signature for predicting 1-year, 2-year and 3-year OS (AUC = 0.769, 0.820, and 0.800, respectively). Moreover, the risk scores were significantly correlated with enrichment in cancer hallmark- and malignancy-related pathways and immunotherapy response in AML patients.

Conclusion: We developed and validated a novel risk signature with m6A-related lncRNAs which could predict prognosis accurately and reflect the immunotherapy response in AML patients.