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

Abstract

Background: Cutaneous melanoma is a highly malignant skin tumor, and most patients have a poor prognosis. In recent years, immunotherapy has assumed an important role in the treatment of advanced cutaneous melanoma, but only a small percentage of patients benefit from immunotherapy. A growing number of studies have demonstrated that the prognosis of patients with cutaneous melanoma is closely related to long non-coding RNA and the tumor immune microenvironment. Methods: We downloaded RNA expression data and immune-related gene lists of cutaneous melanoma patients separately from The Cancer Genome Atlas database and ImmPort website and identified immune-related lncRNAs by co-expression analysis. The prognostic model was constructed by applying least absolute shrinkage and selection operator regression, and all patients were classified into high-and low-risk groups according to the risk score of the model. We evaluated the differences between the two groups in terms of survival outcomes, immune infiltration, pathway enrichment, chemotherapeutic drug sensitivity and immune checkpoint gene expression to verify the impact of lncRNA signature on clinical prognosis and immunotherapy efficacy. Results: By correlation analysis and LASSO regression analysis, we constructed an immune-related lncRNA prognostic model based on five lncRNA: HLA-DQB1-AS1, MIR205HG, RP11-643G5.6, USP30-AS1 and RP11-415F23.4. Based on this model, we plotted Kaplan-Meier survival curves and time-dependent ROC curves and analyzed its ability as an independent prognostic factor for cutaneous melanoma in combination with clinicopathological features. The results showed that these lncRNA signature was an independent prognostic factor of cutaneous melanoma with favorable prognostic ability. Our results also show a higher degree of immune infiltration, higher expression of immune checkpoint-associated genes, and better outcome of immunotherapy in the low-risk group of the lncRNA signature. Conclusion:The 5 immune-related lncRNA signatures constructed in our study can predict the prognosis of cutaneous melanoma and contribute to the selection of immunotherapy.

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

“…In addition, Wang et al have identified eight immune-related lncRNAs with prognostic value in melanoma dataset. Among them, MIR205HG expression is associated with the poor outcome of patient and its expression is positively correlated with the expressions of immune checkpoints like PD-1, CTLA4, LAG3, and TIM3 in adenocarcinoma and melanoma ( 51 ). Our present study demonstrates that immunology-related pathway including innate and adaptive immune response were among the most significantly-enriched biological processes of dys-regulated lncRNAs in primary melanoma, implying that dys-regulated lncRNAs may participate in the regulation of anti-tumor immunity and inflammatory responses during melanoma progression.…”

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

“…With increasing interests in lncRNAs' function in the development of human disease, a huge number of studies have established corresponding ceRNA networks in colon cancer and dug out differently expressed genes, including lncRNAs, correlated with the clinical prognosis and disease development [13][14][15][16]. The abnormal expression of USP30-AS1 has been widely reported, such as in cervical cancer, glioblastoma, ovarian cancer, bladder urothelial carcinoma, and cutaneous melanoma [17][18][19][20][21]. The dysregulation of USP30-AS1 was observed in a lncRNA expression profile of colorectal cancer [8].…”

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

“…It could play a role as a potential biomarker of neuroblastoma (Prajapati et al, 2019). AC026369.3 (Wan et al, 2021), LINC01871 (Chen Q. et al, 2020;He and Wang, 2020;Ma et al, 2020), USP30-AS1 (Chen P. et al, 2020;Xue et al, 2021), and AC093297.2 could become potential prognostic biomarkers for various malignant tumors in bioinformatics approaches.…”