A Novel Risk Model Based on Autophagy-Related LncRNAs Predicts Prognosis and Indicates Immune Infiltration Landscape of Patients With Cutaneous Melanoma

Emerging dimensions of autophagy in melanoma

Macroautophagy/autophagy has previously been regarded as simply a way for cells to deal with nutrient emergency. But explosive work in the last 15 years has given increasingly new knowledge to our understanding of this process. Many of the functions of autophagy that are unveiled from recent studies, however, cannot be reconciled with this conventional view of cell survival but, instead, point to autophagy being integrally involved at a deeper level of cell biology, playing a critical role in maintaining homeostasis and promoting an integrated stress/immune response. The new appreciation of the role of autophagy in the evolutionary trajectory of cancer and cancer interaction with the immune system provides a mechanistic framework for understanding the clinical benefits of autophagy-based therapies. Here, we examine current knowledge of the mechanisms and functions of autophagy in highly plastic and aggressive melanoma as a model disease of human malignancy, while highlighting emerging dimensions indicating that autophagy is at play beyond its classical face.Abbreviation: AMBRA1: autophagy and beclin 1 regulator 1; AMPK: AMP-activated protein kinase; ATF4: activating transcription factor 4; ATG: autophagy related; BRAF: B-Raf proto-oncogene, serine/threonine kinase; CAFs: cancer-associated fibroblasts; CCL5: C-C motif chemokine ligand 5; CQ: chloroquine; CRISPR: clustered regularly interspaced short palindromic repeats; CTLA4: cytotoxic T-lymphocyte associated protein 4; CTL: cytotoxic T lymphocyte; DAMPs: danger/damage-associated molecular patterns; EGFR: epidermal growth factor receptor; EIF2A/eIF2α: eukaryotic translation initiation factor 2A; EIF2AK3/PERK: eukaryotic translation initiation factor 2 alpha kinase 3; ER: endoplasmic reticulum; FITM2: fat storage inducing transmembrane protein 2; HCQ: hydroxychloroquine; ICB: immune checkpoint blockade; ICD: immunogenic cell death; LDH: lactate dehydrogenase; MAPK: mitogen-activated protein kinase; MTORC1: mechanistic target of rapamycin kinase complex 1; NDP52: nuclear dot protein 52; NFKB/NF-κ B: nuclear factor kappa B; NBR1: the neighbor of BRCA1; NK: natural killer; NRF1: nuclear respiratory factor 1; NSCLC: non-small-cell lung cancer; OPTN: optineurin; PDAC: pancreatic ductal adenocarcinoma; PDCD1/PD-1: programmed cell death 1; PPT1: palmitoyl-protein thioesterase 1; PTEN: phosphatase and tensin homolog; PTK2/FAK1: protein tyrosine kinase 2; RAS: rat sarcoma; SQSTM1/p62: sequestosome 1; STK11/LKB1: serine/threonine kinase 11; TAX1BP1: Tax1 binding protein 1; TFEB: transcription factor EB; TGFB/TGF-β: transforming growth factor beta; TMB: tumor mutational burden; TME: tumor microenvironment; TSC1: TSC complex subunit 1; TSC2: TSC complex subunit 2; ULK1: unc-51 like autophagy activating kinase 1; UVRAG: UV radiation resistance associated.

Validation and identification of anoikis-related lncRNA signatures for improving prognosis in clear cell renal cell carcinoma

BACKGROUND

Clear cell carcinoma (ccRCC) usually has a high metastasis rate and high mortality rate. To enable precise risk stratification, there is a need for novel biomarkers. As one form of apoptosis, anoikis results from the disruption of cell-cell connection or cell-ECM attachment. However, the impact of anoikis-related lncRNAs on ccRCC has not yet received adequate attention.

METHODS

The study utilized univariate Cox regression analysis in order to identify the overall survival (OS) associated anoikis-related lncRNAs (ARLs), followed by the LASSO algorithm for selection. On this basis, a risk model was subsequently established using five anoikis-related lncRNAs. To dig the inner molecular mechanism, KEGG, GO, and GSVA analyses were conducted. Additionally, the immune infiltration landscape was estimated using the ESTIMATE, CIBERSORT, and ssGSEA algorithms.

RESULTS

The study constructed a novel risk model based on five ARLs (AC092611.2, AC027601.2, AC103809.1, AL133215.2, and AL162586.1). Patients categorized as low-risk exhibited significantly better OS. Notably, the study observed marked different immune infiltration landscapes and drug sensitivity by risk stratification. Additionally, the study preliminarily explored potential signal pathways associated with risk stratification.

CONCLUSION

The study exhibited the crucial role of ARLs in the carcinogenesis of ccRCC, potentially through differential immune infiltration. Furthermore, the established risk model could serve as a valuable stratification factor for predicting OS prognosis.

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.

Characteristic of molecular subtypes based on PANoptosis-related genes and experimental verification of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a type of liver cancer that originates from liver cells. It is one of the most common types of liver cancer and a leading cause of cancer-related death worldwide. Early detection and treatment can improve the HCC prognosis. Therefore, it is necessary to further improve HCC markers and risk stratification. PANoptosome is a cytoplasmic polymer protein complex that regulates a proinflammatory programmed cell death pathway called "PANoptosis". The role of PANoptosis in HCC remains unclear. In this study, the molecular changes of PANoptosis related genes (PAN-RGs) in HCC were systematically evaluated. We characterized the heterogeneity of HCC by using consensus clustering to identify two distinct subtypes. The two subtypes showed different survival rate, biological function, chemotherapy drug sensitivity and immune microenvironment. After identification of PAN-RG differential expression genes (DEGs), a prognostic model was established by Cox regression analysis using minimum absolute contraction and selection operator (LASSO), and its prognostic value was verified by Cox regression analysis, Kaplan-Meier curve and receiver operating characteristic (ROC) curve. Our own specimens were also used to further validate the prognostic significance and possible clinical value of the selected targets. Subsequently, we conducted a preliminary discussion on the reasons for the influence of the model on the prognosis through TME analysis, drug resistance analysis, TMB analysis and other studies. This study provides a new idea for individualized and precise treatment of HCC.

Machine learning-based signature of necrosis-associated lncRNAs for prognostic and immunotherapy response prediction in cutaneous melanoma and tumor immune landscape characterization

Background

Cutaneous melanoma (CM) is one of the malignant tumors with a relative high lethality. Necroptosis is a novel programmed cell death that participates in anti-tumor immunity and tumor prognosis. Necroptosis has been found to play an important role in tumors like CM. However, the necroptosis-associated lncRNAs' potential prognostic value in CM has not been identified.

Methods

The RNA sequencing data collected from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Project (GTEx) was utilized to identify differentially expressed genes in CM. By using the univariate Cox regression analysis and machine learning LASSO algorithm, a prognostic risk model had been built depending on 5 necroptosis-associated lncRNAs and was verified by internal validation. The performance of this prognostic model was assessed by the receiver operating characteristic curves. A nomogram was constructed and verified by calibration. Furthermore, we also performed sub-group K-M analysis to explore the 5 lncRNAs' expression in different clinical stages. Function enrichment had been analyzed by GSEA and ssGSEA. In addition, qRT-PCR was performed to verify the five lncRNAs' expression level in CM cell line (A2058 and A375) and normal keratinocyte cell line (HaCaT).

Results

We constructed a prognostic model based on five necroptosis-associated lncRNAs (AC245041.1, LINC00665, AC018553.1, LINC01871, and AC107464.3) and divided patients into high-risk group and low-risk group depending on risk scores. A predictive nomogram had been built to be a prognostic indicator to clinical factors. Functional enrichment analysis showed that immune functions had more relationship and immune checkpoints were more activated in low-risk group than that in high-risk group. Thus, the low-risk group would have a more sensitive response to immunotherapy.

Conclusion

This risk score signature could be used to divide CM patients into low- and high-risk groups, and facilitate treatment strategy decision making that immunotherapy is more suitable for those in low-risk group, providing a new sight for CM prognostic evaluation.

SLC25A25-AS1 over-expression could be predicted the dismal prognosis and was related to the immune microenvironment in prostate cancer

It has been established that long-chain coding RNA (lncRNA) SLC25A25-AS1 is associated with cancer progression. However, the roles and mechanisms of SLC25A25-AS1 in prostate cancer (PC) have not been reported in the literature. The present study explored the relationship between SLC25A25-AS1 expression and PC progression via comprehensive analysis. The pan-cancer expression of SLC25A25-AS1 was identified using data from The Cancer Genome Atlas (TCGA) database and tissue specimens from our hospital. The expression levels of SLC25A25-AS1 in various subgroups based on the clinical features were identified. The prognostic value of SLC25A25-AS1 and SLC25A25-AS1 co-expressed lncRNAs in PC patients was assessed by survival analysis and ROC analysis, and prognosis-related risk models of SLC25A25-AS1 were constructed. The relationship between SLC25A25-AS1 and the PC immune microenvironment was investigated using correlation analysis. SLC25A25-AS1 expression in PC was significantly increased and correlated with the T stage, clinical stage, Gleason score (GS), and dismal prognosis. SLC25A25-AS1 overexpression exhibited good performance in evaluating the prognosis of PC patients. The area under the curves (AUCs) of the 1-, 3-, and 5-year overall survival (OS) for SLC25A25-AS1 was 1, 0.876, and 0.749. Moreover, the AUCs for the 1-, 3-, and 5-year progress free interval (PFI) for SLC25A25-AS1 were 0.731, 0.701, and 0.718. SLC25A25-AS1 overexpression correlated with the infiltration of CD8 T cells, interstitial dendritic cells (IDC), macrophages and other cells. AC020558.2, ZNF32-AS2, AP4B1-AS1, AL355488.1, AC109460.3, SNHG1, C3orf35, LMNTD2-AS1, and AL365330.1 were significantly associated with SLC25A25-AS1 expression, and short OS and PFI in PC patients. The risk models of the SLC25A25-AS1-related lncRNAs were associated with a dismal prognosis in PC. Overall, SLC25A25-AS1 expression was increased in PC and related to the prognosis and PC immune microenvironment. The risk model of SLC25A25-AS1 have huge prospect for application as prognostic tools in PC.