Using Immune-Related lncRNA Signature for Prognosis and Response to Immunotherapy in Cutaneous Melanoma

Neutrophil extracellular trap related risk score exhibits crucial prognostic value in skin cutaneous melanoma, associating with distinct immune characteristics

BACKGROUND

Neutrophil extracellular traps (NETs) are related to the prognosis of cancer patients. Nevertheless, the potential prognostic values of NETs in skin cutaneous melanoma (SKCM) remains largely unknown.

MATERIALS AND METHODS

The NET-related gene signature was constructed by LASSO Cox regression analysis using the TCGA-SKCM cohort. The overall survival (OS) and immune status in SKCM patients between the high- and low-NET score (high-score, low-score) groups were explored. The scRNA-seq dataset GSE115978 was used to understand the role of NET score in SKCM at single cell resolution.

RESULTS

A five NET genes-based signature (TLR2, CLEC6A, PDE4B, SLC22A4 and CYP4F3) was constructed as the NET-related prognostic model for SKCM. The OS of SKCM patients with low-score was better than that in patients with high-score. Additionally, NET score was negatively associated with infiltration of some immune cells (e.g. type I Macrophages, CD8-T cells, CD4-T cells). Moreover, patients with high-score had low stromal, immune and ESTIMATE scores. Furthermore, drug sensitivity analysis results showed that Lapatinib, Trametinib and Erlotinib may have better therapeutic advantages in patients with high-score.

CONCLUSION

We established a NET-related five gene signature in SKCM and found that the NET-related signature may exhibit a good predictive ability for SKCM prognosis. The NET score may not only predict the survival outcome and drug sensitivity in SKCM, but also reflect the immune conditions of SKCM patients.

Integrated machine learning algorithms reveal a bone metastasis-related signature of circulating tumor cells in prostate cancer

Bone metastasis is an essential factor affecting the prognosis of prostate cancer (PCa), and circulating tumor cells (CTCs) are closely related to distant tumor metastasis. Here, the protein-protein interaction (PPI) networks and Cytoscape application were used to identify diagnostic markers for metastatic events in PCa. We screened ten hub genes, eight of which had area under the ROC curve (AUC) values > 0.85. Subsequently, we aim to develop a bone metastasis-related model relying on differentially expressed genes in CTCs for accurate risk stratification. We developed an integrative program based on machine learning algorithm combinations to construct reliable bone metastasis-related genes prognostic index (BMGPI). On the basis of BMGPI, we carefully evaluated the prognostic outcomes, functional status, tumor immune microenvironment, somatic mutation, copy number variation (CNV), response to immunotherapy and drug sensitivity in different subgroups. BMGPI was an independent risk factor for disease-free survival in PCa. The high risk group demonstrated poor survival as well as higher immune scores, higher tumor mutation burden (TMB), more frequent co-occurrence mutation, and worse efficacy of immunotherapy. This study highlights a new prognostic signature, the BMGPI. BMGPI is an independent predictor of prognosis in PCa patients and is closely associated with the immune microenvironment and the efficacy of immunotherapy.

Identification and prognostic evaluation of differentially expressed long noncoding RNAs associated with immune infiltration in osteosarcoma

Osteosarcoma is a malignant bone cancer that originates from the bone with the strongest invasiveness. Tumor formation strongly correlates with immune cell infiltration into the tumor immune microenvironment (TIME). Therefore, we aimed to identify TIME-related biomarkers as potential prognostic markers of osteosarcoma. The mRNA and long noncoding RNA (lncRNA) transcriptome data of 88 patients with osteosarcoma and the expression profile of GSE99671 were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Immune infiltration scores and types were evaluated using ESTIMATE and CIBERSORT. A linear model was established to identify the differentially expressed genes (DEGs) and lncRNAs (DElncRNAs). Functional enrichment analysis of DEGs was conducted by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and gene set variation analysis. DElncRNAs were analyzed using a weighted gene co-expression network. Least absolute shrinkage and selection operator regression was applied to screen for prognostic markers. Patient survival was predicted by the risk score and analyzed by receiver operating characteristic curve. Clinical features affecting patient survival were assessed. Immune infiltration positively correlated with osteosarcoma patient survival. Different immune cell infiltrates in patients with osteosarcma may serve as prognostic indicators and targets for immunotherapy. In total, 1125 DEGs, 80 DElncRNAs, and 11 pairs of co-expressed lncRNA-mRNAs were identified. DEGs in the three modules were associated with immune infiltration into the TIME. Four DElncRNAs, namely AC015819.1, AC015911.3, AL365361.1, and USP30-AS1, showed good prognostic ability for osteosarcoma and were positively correlated with the immune score. Tumor metastasis and risk scores alone were good prognostic indicators, and a combination of the two variables can better predict the prognosis of osteosarcoma. We identified four lncRNAs, AC015819.1, AC015911.3, AL365361.1, and USP30-AS1, as potential biomarkers for osteosarcoma prognosis.

The promotive role of lncRNA MIR205HG in proliferation, invasion, and migration of melanoma cells via the JMJD2C/ALKBH5 axis

Melanoma is a highly malignant skin cancer. This study aimed to investigate the role of long non-coding RNA MIR205 host gene (lncRNA MIR205HG) in proliferation, invasion, and migration of melanoma cells via jumonji domain containing 2C (JMJD2C) and ALKB homolog 5 (ALKBH5). Real-time quantitative polymerase chain reaction or Western blot assay showed that MIR205HG, JMJD2C, and ALKBH5 were increased in melanoma cell lines. Cell counting kit-8, colony formation, and Transwell assays showed that silencing MIR205HG inhibited proliferation, invasion, and migration of melanoma cells. RNA immunoprecipitation, actinomycin D treatment, and chromatin immunoprecipitation showed that MIR205HG may bind to human antigen R (HuR, ELAVL1) and stabilized JMJD2C expression, and JMJD2C may increase the enrichment of H3K9me3 in the ALKBH5 promotor region to promote ALKBH5 transcription. The tumor xenograft assay based on subcutaneous injection of sh-MIR205HG-treated melanoma cells showed that silencing MIR205HG suppressed tumor growth and reduced Ki67 positive rate by inactivating the JMJD2C/ALKBH5 axis. Generally, MIR205HG facilitated proliferation, invasion, and migration of melanoma cells through HuR-mediated stabilization of JMJD2C and increasing ALKBH5 transcription by erasing H3K9me3.

Regulatory miRNAs and lncRNAs in Skin Cancer: A Narrative Review

Non-coding RNAs (ncRNAs) have a significant regulatory role in the pathogenesis of skin cancer, despite the fact that protein-coding genes have generally been the focus of research efforts in the field. We comment on the actions of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in the current review with an eye toward potential therapeutic treatments. LncRNAs are remarkably adaptable, acting as scaffolding, guides, or decoys to modify key signaling pathways (i.e., the Wnt/β-catenin pathway) and gene expression. As post-transcriptional gatekeepers, miRNAs control gene expression by attaching to messenger RNAs and causing their degradation or suppression during translation. Cell cycle regulation, cellular differentiation, and immunological responses are all affected by the dysregulation of miRNAs observed in skin cancer. NcRNAs also show promise as diagnostic biomarkers and prognostic indicators. Unraveling the complexity of the regulatory networks governed by ncRNAs in skin cancer offers unprecedented opportunities for groundbreaking targeted therapies, revolutionizing the landscape of dermatologic care.

Clinical Importance of the lncRNA NEAT1 in Cancer Patients Treated with Immune Checkpoint Inhibitors

PURPOSE

mAbs targeting the PD-1/PD-L1 immune checkpoint are powerful tools to improve the survival of patients with cancer. Understanding the molecular basis of clinical response to these treatments is critical to identify patients who can benefit from this immunotherapy. In this study, we investigated long noncoding RNA (lncRNA) expression in patients with cancer treated with anti-PD-1/PD-L1 immunotherapy.

EXPERIMENTAL DESIGN

lncRNA expression profile was analyzed in one cohort of patients with melanoma and two independent cohorts of patients with glioblastoma (GBM) undergoing anti-PD-1/PD-L1 immunotherapy. Single-cell RNA-sequencing analyses were performed to evaluate lncRNA expression in tumor cells and tumor-infiltrating immune cells.

RESULTS

We identified the lncRNA NEAT1 as commonly upregulated between patients with melanoma with complete therapeutic response and patients with GBM with longer survival following anti-PD-1/PD-L1 treatment. Gene set enrichment analyses revealed that NEAT1 expression was strongly associated with the IFNγ pathways, along with downregulation of cell-cycle-related genes. Single-cell RNA-sequencing analyses revealed NEAT1 expression across multiple cell types within the GBM microenvironment, including tumor cells, macrophages, and T cells. High NEAT1 expression levels in tumor cells correlated with increased infiltrating macrophages and microglia. In these tumor-infiltrating myeloid cells, we found that NEAT1 expression was linked to enrichment in TNFα/NFκB signaling pathway genes. Silencing NEAT1 suppressed M1 macrophage polarization and reduced the expression of TNFα and other inflammatory cytokines.

CONCLUSIONS

These findings suggest an association between NEAT1 expression and patient response to anti-PD-1/PD-L1 therapy in melanoma and GBM and have important implications for the role of lncRNAs in the tumor microenvironment.

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.

Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis

Background

To identify differentially expressed lipid metabolism-related genes (DE-LMRGs) responsible for immune dysfunction in sepsis.

Methods

The lipid metabolism-related hub genes were screened using machine learning algorithms, and the immune cell infiltration of these hub genes were assessed by CIBERSORT and Single-sample GSEA. Next, the immune function of these hub genes at the single-cell level were validated by comparing multiregional immune landscapes between septic patients (SP) and healthy control (HC). Then, the support vector machine-recursive feature elimination (SVM-RFE) algorithm was conducted to compare the significantly altered metabolites critical to hub genes between SP and HC. Furthermore, the role of the key hub gene was verified in sepsis rats and LPS-induced cardiomyocytes, respectively.

Results

A total of 508 DE-LMRGs were identified between SP and HC, and 5 hub genes relevant to lipid metabolism (MAPK14, EPHX2, BMX, FCER1A, and PAFAH2) were screened. Then, we found an immunosuppressive microenvironment in sepsis. The role of hub genes in immune cells was further confirmed by the single-cell RNA landscape. Moreover, significantly altered metabolites were mainly enriched in lipid metabolism-related signaling pathways and were associated with MAPK14. Finally, inhibiting MAPK14 decreased the levels of inflammatory cytokines and improved the survival and myocardial injury of sepsis.

Conclusion

The lipid metabolism-related hub genes may have great potential in prognosis prediction and precise treatment for sepsis patients.

SMG7-AS1 as a prognostic biomarker and predictor of immunotherapy responses for skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is the most life-threatening skin cancer and lacks early detection and effective treatment strategies. Many long noncoding RNAs are associated with the development of tumors and may serve as potential immunotherapeutic targets. In this study, microarray analysis was performed to screen for differentially expressed lncRNAs between SKCM and normal tissues, and SMG7-AS1 was identified as an upregulated lncRNA in SKCM. Subsequently, bioinformatic analysis revealed that dysregulation of SMG7-AS1 influences metastasis and immune infiltration. qRT-PCR of clinical samples demonstrated that the expression of SMG7-AS1 was higher in melanoma tissues. Flow cytometry showed that SMG7-AS1 plays a vital role in the cell cycle. Additionally, SMG7-AS1 was found to be associated with immunotherapy responses. To the best of our knowledge, this study is the first to report that SMG7-AS1 is associated with SKCM and may serve as a prognostic biomarker and predictor of immunotherapy responses in SKCM.

A novel endoplasmic reticulum stress-related lncRNA prognostic risk model for cutaneous melanoma

OBJECTIVE

Endoplasmic reticulum stress (ERS) and long non-coding RNAs (lncRNAs) are important in melanoma development and progression. This study aimed to explore the prognostic value of ERS-associated lncRNA profiles in cutaneous melanoma (CM).

METHODS

The Cancer Genome Atlas (TCGA) provides the raw data of CM. GSEA website was used to obtain ERS-related genes, and mRNA and LncRNA co-expression network were used to obtain ERS-related lncRNAs. A Lasso regression analysis was used to identify a prognostic risk model for the composition of ERS-related lncRNAs. Patients were divided into high- and low-risk groups based on the model's risk score. The researchers then compared the two groups' survival rates, immune infiltration, chemotherapeutic drug sensitivity, and immune checkpoint gene expression.

RESULTS

Thirty-nine ERS-related lncRNAs were discovered to be prognostic. A prognostic risk model made up of ten ERS-related lncRNAs was discovered. Patients in the low-risk group had a better prognosis than those in the high-risk group. An examination of tumor microenvironment revealed that risk scores correlated with immune cell infiltration in eight cases. Dacarbazine, paclitaxel, and cisplatin, three chemotherapy drugs, were more sensitive in the low-risk group than in the high-risk group.

CONCLUSION

This study identified a risk model of ten ERS-related lncRNAs that have significant prognostic value in CM and could help guide clinical treatment.

A novel prognostic model for cutaneous melanoma based on an immune-related gene signature and clinical variables

Abundant evidence has indicated that the prognosis of cutaneous melanoma (CM) patients is highly complicated by the tumour immune microenvironment. We retrieved the clinical data and gene expression data of CM patients in The Cancer Genome Atlas (TCGA) database for modelling and validation analysis. Based on single-sample gene set enrichment analysis (ssGSEA) and consensus clustering analysis, CM patients were classified into three immune level groups, and the differences in the tumour immune microenvironment and clinical characteristics were evaluated. Seven immune-related CM prognostic molecules, including three mRNAs (SUCO, BTN3A1 and TBC1D2), three lncRNAs (HLA-DQB1-AS1, C9orf139 and C22orf34) and one miRNA (hsa-miR-17-5p), were screened by differential expression analysis, ceRNA network analysis, LASSO Cox regression analysis and univariate Cox regression analysis. Their biological functions were mainly concentrated in the phospholipid metabolic process, transcription regulator complex, protein serine/threonine kinase activity and MAPK signalling pathway. We established a novel prognostic model for CM integrating clinical variables and immune molecules that showed promising predictive performance demonstrated by receiver operating characteristic curves (AUC ≥ 0.74), providing a scientific basis for predicting the prognosis and improving the clinical outcomes of CM patients.

Development and Validation of a DNA Methylation-related Classifier of Circulating Tumour Cells to Predict Prognosis and to provide a therapeutic strategy in Lung Adenocarcinoma

Background: A significant factor influencing the prognosis of lung adenocarcinoma (LUAD) is tumor metastasis. Studies have shown that abnormal DNA methylation in circulating tumor cells (CTCs) is associated with tumour metastasis. Based on the genes expressed in CTCs that play an important role in DNA methylation, we hope to build a risk model to predict prognosis and provide a therapeutic strategy in LUAD.

Methods: The CTC sequencing data for LUAD were obtained from GSE74639, which contains 10 CTC samples and 6 primary tumour samples. To carefully assess the clinical value, functional status, involvement of the tumor microenvironment (TME) based on the risk model, and genetic variants based on based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), a reliable risk model was successfully built.

Results: Three differentially methylated genes (DMGs) of CTCs for LUAD, including mitochondrial ribosomal protein L51 (MRPL51), STE20-like kinase (SLK), and protein regulator of cytokinesis 1(PRC1), were effectively used to construct a risk model. Both the training and validation cohorts' stability and accuracy of the risk model were evaluated. Each patient in the TCGA-LUAD cohort received a risk score, and based on the median score, they were divided into high- and low-risk groups. The tumors in the high-risk group in this study were classified as "cold" and immunosuppressed, which may be linked to a poor prognosis. The tumors in the low-risk group, however, were deemed "hot" and had immune hyperfunction linked to a positive prognosis. Additionally, patients in the low-risk group showed greater sensitivity to immunotherapy than those in the high-risk group.

Conclusions: Based on DMGs of CTCs from LUAD, we successfully developed a predictive risk model and discovered differences in biological function, TME, genetic variation, and clinical outcomes between those at high and low risk group.

A novel risk model based on cuproptosis-related lncRNAs predicted prognosis and indicated immune microenvironment landscape of patients with cutaneous melanoma

Cutaneous melanoma (CM) is an aggressive form of malignancy with poor prognostic value. Cuproptosis is a novel type of cell death regulatory mechanism in tumors. However, the role of cuproptosis-related long noncoding RNAs (lncRNAs) in CM remains elusive. The cuproptosis-related lncRNAs were identified using the Pearson correlation algorithm. Through the univariate and multivariate Cox regression analysis, the prognosis of seven lncRNAs associated with cuproptosis was established and a new risk model was constructed. ESTIMATE, CIBERSORT, and single sample gene set enrichment analyses (ssGSEA) were applied to evaluate the immune microenvironment landscape. The Kaplan–Meier survival analysis revealed that the overall survival (OS) of CM patients in the high-risk group was remarkably lower than that of the low-risk group. The result of the validated cohort and the training cohort indicated that the risk model could produce an accurate prediction of the prognosis of CM. The nomogram result demonstrated that the risk score based on the seven prognostic cuproptosis-related lncRNAs was an independent prognostic indicator feature that distinguished it from other clinical features. The result of the immune microenvironment landscape indicated that the low-risk group showed better immunity than high-risk group. The immunophenoscore (IPS) and immune checkpoints results conveyed a better benefit potential for immunotherapy clinical application in the low-risk groups. The enrichment analysis and the gene set variation analysis (GSVA) were adopted to reveal the role of cuproptosis-related lncRNAs mediated by the immune-related signaling pathways in the development of CM. Altogether, the construction of the risk model based on cuproptosis-related lncRNAs can accurately predict the prognosis of CM and indicate the immune microenvironment of CM, providing a new perspective for the future clinical treatment of CM.

[Correlation analysis of Poor Prognosis and Immunotherapy of lncRNAs Related with m 6A Modification in Cervical Cancer]

Objective

To study the correlation between N 6-methyladenosine (m 6A)-modification-associated long non-coding RNAs (lncRNAs) and poor prognosis and immunotherapy in cervical cancer based on data mining of The Cancer Genome Atlas (TCGA) cervical cancer dataset, so as to assess effectively the prognosis of cervical cancer patients and the feasibility of immunotherapy.

Methods

We identified m 6A-modification-associated lncRNAs correlated to the prognosis of cervical cancer by conducting bioinformatics analysis of cervical cancer samples from the TCGA datasets and constructed a prognostic risk model of cervical cancer accordingly.

Results

A total of 343 m 6A-modification-associated lncRNAs were identified from the samples of 304 cervical cancer patients. Univariate Cox regression analysis showed that 26 out of the 343 m 6A-modification-associated lncRNAs were significantly associated with the prognosis of cervical cancer patients. We identified 7 m 6A-modification-associated lncRNAs, including DLEU1, AC099850.4, DDN-AS1, EP300-AS1, AC131159.1, AL441992.2, and AL021707.6 through Lasso regression analysis and then developed a prognostic risk model based on them. According to the Kaplan-Meier survival analysis, cervical cancer patients in the low-risk group exhibited significantly improved overall survival (OS) in comparison with those in the high-risk group ( P<0.001). The area under the curve ( AUC) of receiver operating characteristic (ROC) curve analysis demonstrated the high sensitivity and credibility of the risk model. Multivariate Cox analysis showed that the risk score was an independent prognostic factor of cervical cancer patients. Tumor immune dysfunction and exclusion (TIDE) analysis predicted that the high-risk group would benefit more from immunotherapy. In addition, we found that immune checkpoint PD1 was associated with the expression of m6A-modification-related lncRNAs such as DDN-AS1, and the expression was higher in the high-risk group ( P<0.05).

Conclusion

The prognostic risk model constructed on the basis of the aforementioned 7 m 6A-modification-associated lncRNAs can be used to effectively predict the prognosis of cervical cancer patients and assess the efficacy of immunotherapy targeting PD1.

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.

lncRNA USP30-AS1 sponges miR-765 and modulates the progression of colon cancer

Background

The incidence and mortality of colon cancer is increasing recently. It is necessary to identify effective biomarkers for the progression and prognosis of colon cancer. To assess the potential of lncRNA USP30-AS1 (USP30-AS1) in serving as the biomarker of colon cancer and unearth the underlying mechanism.

Methods

There were 123 colon cancer patients enrolled. The expression of USP30-AS1 was evaluated with PCR in tissue and cell samples. The clinical significance of USP30-AS1 was assessed with a series of statistical methods, while the CCK8 and Transwell assay were conducted to estimate its biological effect on the colon cancer cellular processes. In mechanism, the interaction of USP30-AS1 with miR-765 was evaluated with the dual-luciferase reporter assay.

Results

In colon cancer tissues, the USP30-AS1 downregulation and the miR-765 upregulation were observed, and there was a negative correlation between the USP30-AS1 expression level and the miR-765 expression level. The downregulation of USP30-AS1 related to the malignant progression and served as an adverse prognostic indicator of colon cancer. The overexpression of USP30-AS1 dramatically suppressed colon cancer cellular processes, which was alleviated by miR-765.

Conclusions

USP30-AS1 predicts the malignancy and prognosis of colon cancer patients. USP30-AS1 suppressed the progression of colon cancer through modulating miR-765.

A Novel Ferroptosis-Related lncRNA Prognostic Model and Immune Infiltration Features in Skin Cutaneous Melanoma

Background: Skin cutaneous melanoma (SKCM) is an aggressive malignant skin tumor. Ferroptosis is an iron-dependent cell death that may mobilize tumor-infiltrating immunity against cancer. The potential mechanism of long non-coding RNAs (lncRNAs) in ferroptosis in SKCM is not clear. In this study, the prognostic and treatment value of ferroptosis-related lncRNAs was explored in SKCM, and a prognostic model was established.

Methods: We first explored the mutation state of ferroptosis-related genes in SKCM samples from The Cancer Genome Atlas database. Then, we utilized consensus clustering analysis to divide the samples into three clusters based on gene expression and evaluated their immune infiltration using gene-set enrichment analysis (GSEA) ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) algorithms. In addition, we applied univariate Cox analysis to screen prognostic lncRNAs and then validated their prognostic value by Kaplan–Meier (K-M) and transcripts per kilobase million (TPM) value analyses. Finally, we constructed an 18-ferroptosis-related lncRNA prognostic model by multivariate Cox analysis, and SKCM patients were allocated into different risk groups based on the median risk score. The prognostic value of the model was evaluated by K-M and time-dependent receiver operating characteristic (ROC) analyses. Additionally, the immunophenoscore (IPS) in different risk groups was detected.

Results: The top three mutated ferroptosis genes were TP53, ACSL5, and TF. The SKCM patients in the cluster C had the highest ferroptosis-related gene expression with the richest immune infiltration. Based on the 18 prognosis-related lncRNAs, we constructed a prognostic model of SKCM patients. Patients at low risk had a better prognosis and higher IPS.

Conclusion: Our findings revealed that ferroptosis-related lncRNAs were expected to become potential biomarkers and indicators of prognosis and immunotherapy treatment targets of SKCM.

Long Non-Coding RNA CD27-AS1-208 Facilitates Melanoma Progression by Activating STAT3 Pathway

Melanoma is the most lethal skin cancer that originates from epidermal melanocytes. Recently, long non-coding RNAs (lncRNAs) are emerging as critical regulators of cancer pathogenesis and potential therapeutic targets. However, the expression profile of lncRNAs and their role in melanoma progression have not been thoroughly investigated. Herein, we firstly obtained the expression profile of lncRNAs in primary melanomas using microarray analysis and unveiled the differentially-expressed lncRNAs compared with nevus. Subsequently, a series of bioinformatics analysis showed the great involvement of dysregulated lncRNAs in melanoma biology and immune response. Further, we identified lncRNA CD27-AS1-208 as a novel nuclear-localized factor with prominent facilitative role in melanoma cell proliferation, invasion and migration. Mechanistically, CD27-AS1-208 could directly interact with STAT3 and contribute to melanoma progression in a STAT3-dependent manner. Ultimately, the role of CD27-AS1-208 in melanoma progression in vivo was also investigated. Collectively, the present study offers us a new horizon to better understand the role of lncRNAs in melanoma pathogenesis and demonstrates that CD27-AS1-208 up-regulation contributes to melanoma progression by activating STAT3 pathway. Targeting CD27-AS1-208 in melanoma cells can be exploited as a potential therapeutic approach that needs forward validation in clinical trials in the future.

Silencing FYVE, RhoGEF, and PH domain containing 1 (FGD1) suppresses melanoma progression by inhibiting PI3K/AKT signaling pathway

Cutaneous melanoma is the leading cause of death among skin cancers despite the availability of diverse treatments. FGD1 plays an important role in multiple cancers, but how it works in cutaneous melanoma has not been illustrated. Thus, this study was intended to investigate the roles of FGD1 and its underlying mechanisms in cutaneous melanoma. Bioinformatics tools and quantitative real-time polymerase chain reaction (qRT-PCR) were used to analyze the expression of FGD1 in cutaneous melanoma. After the knockdown of FGD1 in melanoma cells, the proliferation, migration, and invasion of cells were analyzed by cell counting kit-8 (CCK8) assay, colony formation assays and transwell assays. Western blot was used to check the expression of key factors in PI3K/AKT pathway. In addition, nude mice models were used to study the role of FGD1 in melanoma development and metastasis in vivo. The data demonstrated that FGD1 was up-regulated and predicted a poor clinical outcome for cutaneous melanoma patients. Knockdown of FGD1 inhibited melanoma cell proliferation, migration, and invasion. The expressions of p-PI3K and p-AKT were significantly decreased, while the expressions of PI3K and AKT showed no marked difference in the knockdown group. Meanwhile, knockdown of FGD1 suppressed the development of melanoma in vivo. This study suggested that knockdown of FGD1 could block melanoma formation and proliferation by inhibiting PI3K/AKT signaling pathway. FGD1 might be a promising therapeutic target for melanoma.