Autophagy-Related Long Non-coding RNA Signature as Indicators for the Prognosis of Uveal Melanoma.
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ABSTRACT:
SUBMITTER: Cui Y
PROVIDER: S-EPMC8047156 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Glioma is one of the most common types of malignant primary central nervous system tumor, and prognosis for this disease is poor. As autophagic drugs have been reported to induce glioma cell death, we investigated the potential prognostic role of autophagy-associated long non-coding RNA (lncRNA) in glioma patients. In this study, we obtained 879 lncRNAs and 216 autophagy genes from the Chinese Glioma Genome Atlas microarray, and found that 402 lncRNAs are correlated with the autophagy genes. Subsequently, 10 autophagy-associated lncRNAs with prognostic value (PCBP1-AS1, TP53TG1, DHRS4-AS1, ZNF674-AS1, GABPB1-AS1, DDX11-AS1, SBF2-AS1, MIR4453HG, MAPKAPK5-AS1 and COX10-AS1) were identified in glioma patients using multivariate Cox regression analyses. A prognostic signature was then established based on these prognostic lncRNAs, dividing patients into low-risk and high-risk groups. The overall survival time was shorter in the high-risk group than that in the low-risk group [hazard ratio (HR) = 5.307, 95% CI: 4.195-8.305; P < 0.0001]. Gene set enrichment analysis revealed that the gene sets were significantly enriched in cancer-related pathways, including interleukin (IL) 6/Janus kinase/signal transducer and activator of transcription (STAT) 3 signaling, tumor necrosis factor α signaling via nuclear factor κB, IL2/STAT5 signaling, the p53 pathway and the KRAS signaling pathway. The Cancer Genome Atlas dataset was used to validate that high-risk patients have worse survival outcomes than low-risk patients (HR = 1.544, 95% CI: 1.110-2.231; P = 0.031). In summary, our signature of 10 autophagy-related lncRNAs has prognostic potential for glioma, and these autophagy-related lncRNAs may play a key role in glioma biology.
| S-EPMC6443865 | biostudies-literature
Project description:Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer. Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer. Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs. Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway. Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.
| S-EPMC8007922 | biostudies-literature
Project description:Hypoxia‑related long non‑coding RNAs (lncRNAs) are important indicators of the poor prognosis of cancers. The present study aimed to explore the potential relationship between melanoma and hypoxia‑related lncRNAs. The transcriptome and clinical data of patients with melanoma were downloaded from The Cancer Genome Atlas database. The prognostic hypoxia‑related lncRNAs were screened out using Pearson's correlation test and univariate Cox analysis. As a result, a hypoxia‑related‑lncRNA signature based on the expression of 7 lncRNAs was constructed, with one unfavourable [MIR205 host gene (MIR205HG)] and six favourable (T cell receptor β variable 11‑2, HLA‑DQB1 antisense RNA 1, AL365361.1, AC004847.1, ubiquitin specific peptidase 30 antisense RNA 1 and AC022706.1) lncRNAs as prognostic factors for melanoma. Patients with melanoma were divided into high‑ and low‑risk groups based on the risk score obtained. Survival analyses were performed to assess the prognostic value of the present risk model. Potential tumour‑associated biological pathways associated with the present signature were explored using gene set enrichment analysis. The CIBERSORT algorithm demonstrated the important role of the hypoxia‑related lncRNAs in regulating tumour‑infiltrating immune cells. Clinical samples collected from our center partly confirmed our findings. Cell Counting Kit‑8 and flow cytometry assays indicated the suppression of proliferation of melanoma cells following inhibition of MIR205HG expression. Indicators of the canonical Wnt/β‑catenin signalling pathway were detected by western blotting. The present study demonstrated that MIR205HG could promote melanoma cell proliferation partly via the canonical Wnt/β‑catenin signalling pathway. These findings indicated a 7‑hypoxia‑related‑lncRNA signature that can serve as a novel predictor of melanoma prognosis.
| S-EPMC9218734 | biostudies-literature
Project description:Uveal Melanoma (UM) is a rare cancer deriving from melanocytes within the uvea. It has a high rate of metastasis, especially to the liver, and a poor prognosis thereafter. Autophagy, an intracellular programmed digestive process, has been associated with the development and progression of cancers, with controversial pro- and anti-tumour roles. Although previous studies have been conducted on autophagy-related genes (ARGs) in various cancer types, its role in UM requires a deeper understanding for improved diagnosis and development of novel therapeutics. In the present study, Zheng et al. used univariate Cox regression followed by least absolute shrinkage and selection operator (Lasso) regression to identify a robust 9-ARG signature prognostic of survival in a total of 230 patients with UM. The authors used the Cancer Genome Atlas (TCGA) UM cohort as a training cohort (n=80) to identify the signature and validated it in another four independent cohorts of 150 UM patients from the Gene Expression Omnibus (GEO) repository (GSE22138, GSE27831, GSE44295 and GSE84976). This 9-ARG signature was also significantly associated with the enrichment of cancer hallmarks, including angiogenesis, IL6-KJAK-STAT3 signalling, reactive oxygen species pathway and oxidative phosphorylation. More importantly, this signature is associated with immune-related functional pathways and immune cell infiltration. Thus, this 9-ARG signature predicts prognosis and provides deeper insights into the immune mechanisms in UM, with potential implications for future immunotherapy.
| S-EPMC8380919 | biostudies-literature
Project description:BackgroundUveal melanoma (UVM) is the most common primary intraocular malignancy in adults. Epithelial-mesenchymal transition (EMT) is an essential regulator of the UVM's immune microenvironment. However, the precise role of EMT in UVM remains to be explored and the development of a related treatment strategy is urgently needed.MethodsMultiomics data and clinical information for TCGA-UVM were used to identify the EMT subtypes and analyze their regulatory role in the immune microenvironment in UVM. A machine-learning method based on the identified subtypes was utilized to construct the EMT feature-based prognostic model. External validation cohorts GSE84976 and GSE22138 were employed to validate the model's robustness. Immunotherapy cohort IMvigor210 was used to explore the model's potential to predict immunotherapy responsiveness.ResultsTwo EMT subtypes were identified in UVM. The role of EMT in shaping the immune microenvironment and regulating cancer-immunity circle of UVM was analyzed. A robust prognostic model was presented and validated to predict patient prognosis. The model also predicted patient's immune features and immunotherapy responsiveness.ConclusionThe EMT-mediated immune features in UVM were illustrated, providing a reliable model to facilitate precise UVM treatment. This research may assist in decision-making during clinical UVM therapy.
| S-EPMC9391141 | biostudies-literature
Project description:Increasing evidence suggests that aberrant long non-coding RNAs (lncRNAs) are significantly correlated with the pathogenesis, development and metastasis of cancers. RHPN1 antisense RNA 1 (RHPN1-AS1) is a 2030-bp transcript originating from human chromosome 8q24. However, the role of RHPN1-AS1 in uveal melanoma (UM) remains to be clarified. In this study, we aimed to elucidate the molecular function of RHPN1-AS1 in UM. The RNA levels of RHPN1-AS1 in UM cell lines were examined using the quantitative real-time polymerase chain reaction (qRT-PCR). Short interfering RNAs (siRNAs) were designed to quench RHPN1-AS1 expression, and UM cells stably expressing short hairpin (sh) RHPN1-AS1 were established. Next, the cell proliferation and migration abilities were determined using a colony formation assay and a transwell migration/invasion assay. A tumor xenograft model in nude mice was established to confirm the function of RHPN1-AS1 in vivo. RHPN1-AS1 was significantly upregulated in a number of UM cell lines compared with the normal human retinal pigment epithelium (RPE) cell line. RHPN1-AS1 knockdown significantly inhibited UM cell proliferation and migration in vitro and in vivo. Our data suggest that RHPN1-AS1 could be an oncoRNA in UM, which may serve as a candidate prognostic biomarker and target for new therapies in malignant UM.
| S-EPMC5297855 | biostudies-other
Project description:Autophagy-related long non-coding RNAs (lncRNAs) disorders are related to the occurrence and development of breast cancer. The purpose of this study is to explore whether autophagy-related lncRNA can predict the prognosis of breast cancer patients. The autophagy-related lncRNAs prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression. We identified five autophagy-related lncRNAs (MAPT-AS1, LINC01871, AL122010.1, AC090912.1, AC061992.1) associated with prognostic value, and they were used to construct an autophagy-related lncRNA prognostic signature (ALPS) model. ALPS model offered an independent prognostic value (HR = 1.664, 1.381-2.006), where this risk score of the model was significantly related to the TNM stage, ER, PR and HER2 status in breast cancer patients. Nomogram could be utilized to predict survival for patients with breast cancer. Principal component analysis and Sankey Diagram results indicated that the distribution of five lncRNAs from the ALPS model tends to be low-risk. Gene set enrichment analysis showed that the high-risk group was enriched in autophagy and cancer-related pathways, and the low-risk group was enriched in regulatory immune-related pathways. These results indicated that the ALPS model composed of five autophagy-related lncRNAs could predict the prognosis of breast cancer patients.
| S-EPMC8051719 | biostudies-literature
Project description:BackgroundTongue squamous cell carcinoma (TSCC) is the most common oral cancer with a poor prognosis. At present, there is not any systematic study on autophagy-related long non-coding RNA (lncRNA) to predict the survival of patients with TSCC.Material and methodsIn this research, the cohort of TSCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis showed that ten lncRNAs related to autophagy AC010326.3, AL160006.1, AL122010.1, AC139530.1, AC092747.4, AL139287.1, MIR503HG, AC009318.2, LINC01711, and LINC02560 are significantly correlated with prognosis. Based on these lncRNAs, a prognostic signature was established. This signature has an AUC value of 0.782, which accurately distinguishes patients of TSCC into high-risk and low-risk groups in different clinical hierarchical information (such as gender, age, etc.).ResultsThe clinical nomogram with autophagy-related lncRNA prognostic characteristics has a concordance index of 0.81, and accurately predicts the survival time at 1-year and 3-year of TSCC patients. Related functional enrichment results indicate that the pathways of the high-risk group are enriched on cancer and autophagy.ConclusionsThe autophagy-related lncRNA prognostic signature established in this study could accurately predict the prognosis of TSCC patients and may be a molecular biomarker and therapeutic target.
| S-EPMC9945660 | biostudies-literature
Project description:Background: Gastric carcinoma (GC) is a molecularly and phenotypically highly heterogeneous disease, making the prognostic prediction challenging. On the other hand, Inflammation as part of the active cross-talk between the tumor and the host in the tumor or its microenvironment could affect prognosis. Method: We established a prognostic multi lncRNAs signature that could better predict the prognosis of GC patients based on inflammation-related differentially expressed lncRNAs in GC. Results: We identified 10 differently expressed lncRNAs related to inflammation associated with GC prognosis. Kaplan-Meier survival analysis demonstrated that high-risk inflammation-related lncRNAs signature was related to poor prognosis of GC. Moreover, the inflammation-related lncRNAs signature had an AUC of 0.788, proving their utility in predicting GC prognosis. Indeed, our risk signature is more precise in predicting the prognosis of GC patients than traditional clinicopathological manifestations. Immune and tumor-related pathways for individuals in the low and high-risk groups were further revealed by GSEA. Moreover, TCGA based analysis revealed significant differences in HLA, MHC class-I, cytolytic activity, parainflammation, co-stimulation of APC, type II INF response, and type I INF response between the two risk groups. Immune checkpoints revealed CD86, TNFSF18, CD200, and LAIR1 were differently expressed between lowand high-risk groups. Conclusion: A novel inflammation-related lncRNAs (AC015660.1, LINC01094, AL512506.1, AC124067.2, AC016737.1, AL136115.1, AP000695.1, AC104695.3, LINC00449, AC090772.1) signature may provide insight into the new therapies and prognosis prediction for GC patients.
| S-EPMC8607501 | biostudies-literature
Project description:PurposeColon adenocarcinoma (COAD) is the most common type of colorectal cancer (CRC) and is associated with poor prognosis. Emerging evidence has demonstrated that glycosylation by long noncoding RNAs (lncRNAs) was associated with COAD progression. To date, however, the prognostic values of glycosyltransferase (GT)-related lncRNAs in COAD are still largely unknown.MethodsWe obtained the expression matrix of mRNAs and lncRNAs in COAD from The Cancer Genome Atlas (TCGA) database. Then, the univariate Cox regression analysis was conducted to identify 33 prognostic GT-related lncRNAs. Subsequently, LASSO and multivariate Cox regression analysis were performed, and 7 of 33 GT-related lncRNAs were selected to conduct a risk model. Gene set enrichment analysis (GSEA) was used to analyze gene signaling pathway enrichment of the risk model. ImmuCellAI, an online tool for estimating the abundance of immune cells, and correlation analysis were used to explore the tumor-infiltrating immune cells in COAD. Finally, the expression levels of seven lncRNAs were detected in colorectal cancer cell lines by reverse transcription-quantitative polymerase chain reaction (RT-qPCR).ResultsA total of 1,140 GT-related lncRNAs were identified, and 7 COAD-specific GT-related lncRNAs (LINC02381, MIR210HG, AC009237.14, AC105219.1, ZEB1-AS1, AC002310.1, and AC020558.2) were selected to conduct a risk model. Patients were divided into high- and low-risk groups based on the median of risk score. The prognosis of the high-risk group was worse than that of the low-risk group, indicating the good reliability and specificity of our risk model. Additionally, a nomogram based on the risk score and clinical traits was built to help clinical decisions. GSEA showed that the risk model was significantly enriched in metabolism-related pathways. Immune infiltration analysis revealed that five types of immune cells were significantly different between groups, and two types of immune cells were negatively correlated with the risk score. Besides, we found that the expression levels of these seven lncRNAs in tumor cells were significantly higher than those in normal cells, which verified the feasibility of the risk model.ConclusionThe efficient risk model based on seven GT-related lncRNAs has prognostic potential for COAD, which may be novel biomarkers and therapeutic targets for COAD patients.
| S-EPMC9530784 | biostudies-literature