Investigation of GPR143 as a promising novel marker for the progression of skin cutaneous melanoma through bioinformatic analyses and cell experiments.

BACKGROUND: Skin cutaneous melanoma (SKCM) is an aggressive and life-threatening skin cancer. G-protein coupled receptor 143 (GPR143) belongs to the superfamily of G protein-coupled receptors. METHODS: We used the TCGA, GTEx, CCLE, and the Human Protein Atlas databases to examine the mRNA and protein expression of GPR143. In addition, we performed a survival analysis and evaluated the diagnostic efficacy using the Receiver-Operating Characteristic (ROC) curve. Through CIBERSORT, R programming, TIMER, Gene Expression Profiling Interactive Analysis, Sangerbox, and Kaplan-Meier plotter database analyses, we explored the relationships between GPR143, immune infiltration, and gene marker expression of immune infiltrated cells. Furthermore, we investigated the proteins that potentially interact with GPR143 and their functions using R programming and databases including STRING, GeneMANIA, and GSEA. Meanwhile, the cBioPortal, UALCNA, and the MethSurv databases were used to examine the genomic alteration and methylation of GPR143 in SKCM. The Connectivity Map database was used to discover potentially effective therapeutic molecules against SKCM. Finally, we conducted cell experiments to investigate the potential role of GPR143 in SKCM. RESULTS: We demonstrated a significantly high expression level of GPR143 in SKCM compared with normal tissues. High GPR143 expression and hypomethylation status of GPR143 were associated with a poorer prognosis. ROC analysis showed that the diagnostic efficacy of the GPR143 was 0.900. Furthermore, GPR143 expression was significantly correlated with immune infiltration in SKCM. We identified 20 neighbor genes and the pathways they enriched were anabolic process of pigmentation, immune regulation, and so on. Genomic alteration analysis revealed significantly different copy number variations related to GPR143 expression in SKCM, and shallow deletion could lead to high expression of GPR143. Ten potential therapeutic drugs against SKCM were identified. GPR143 knockdown inhibited melanoma cell proliferation, migration, and colony formation while promoting apoptosis. CONCLUSIONS: Our findings suggest that GPR143 serves as a novel diagnostic and prognostic biomarker and is associated with the progression of SKCM.

Construction of a tumor mutational burden-derived LncRNA prognostic computational framework associated with therapy sensitivity in skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) poses a significant public health challenge due to its aggressive nature and limited treatment options. To address this, the study introduces the Tumor Mutational Burden-Derived Immune lncRNA Prognostic Index (TILPI) as a potential prognostic tool for SKCM. METHODS: TILPI was developed using a combination of gene set variation analysis, differential expression analysis, and COX regression analysis. Additionally, functional experiments were conducted to validate the findings, focusing on the effects of STARD4-AS1 knockdown on SKCM tumor cell behavior. These experiments encompassed assessments of tumor cell proliferation, gene and protein expression, migration, invasion, and in vivo tumor growth. RESULTS: The results demonstrated that knockdown of STARD4-AS1 led to a significant reduction in tumor cell proliferation and impaired migration and invasion abilities. Moreover, it resulted in the downregulation of ADCY4, PRKACA, and SOX10 gene expression, as well as decreased protein expression of ADCY4, PRKACA, and SOX10. In vivo experiments further confirmed the efficacy of STARD4-AS1 knockdown in reducing tumor growth. CONCLUSIONS: This study elucidates the mechanistic role of STARD4-AS1 and its downstream targets in SKCM progression, highlighting the importance of the ADCY4/PRKACA/SOX10 pathway. The integration of computational analysis with experimental validation enhances the understanding of TILPI and its clinical implications. Overall, the findings underscore the potential of novel computational frameworks like TILPI in predicting and managing SKCM, particularly through targeting the ADCY4/PRKACA/SOX10 pathway.

RNF144A-AS1 stabilizes TAF15 and promotes malignant biological behaviors of skin cutaneous melanoma.

LncRNAs have been demonstrated to regulate biological processes in malignant tumors. In our previous study, we identified the immune-related LncRNA RNF144A-AS1 as a potential regulator in SKCM. However, its precise function and regulatory mechanism remain unclear. In this study, we observed upregulation of RNF144A-AS1 in SKCM and found that knockdown of RNF144A-AS1 suppressed proliferation, migration, invasion, and epithelial-mesenchymal transition abilities of melanoma cells. Mechanistically, as a high-risk prognostic factor, RNF144A-AS1 regulated biological processes of SKCM by interacting with TAF15 through an RNA-binding protein-dependent (RBP-dependent) manner. Furthermore, we confirmed that TAF15 activated downstream transcriptional regulation of YAP1 to modulate malignant behaviors in melanoma cells. In vivo experiments revealed that knockdown of RNF144A-AS1 inhibited tumorigenic capacity of melanoma cells and exhibited promising therapeutic effects. Collectively, these findings highlight the significance of the RNF144A-AS1/TAF15/YAP1 axis in promoting malignant behaviors in SKCM and provide novel insights into potential prognostic biomarkers and therapeutic targets for this disease.

Identification of potential therapeutic targets for skin cutaneous melanoma on the basic of transcriptomics.

BACKGROUND: Advanced skin cutaneous melanoma (SKCM) is responsible for the majority of skin cancer-related deaths. Apart from the rare BRAF V600F mutation, which can be targeted with specific drugs, there are currently no other novel effective therapeutic targets. METHODS: We used SMR analysis with cis-expressed quantitative trait locus (cis-eQTL) as the exposure variable and SKCM as the outcome variable to identify potential therapeutic targets for SKCM. Colocalization assays and HEIDI tests are used to test whether SKCM risk and gene expression are driven by common SNPs. Replication analysis further validated the findings, and we also constructed protein-protein interaction networks to explore the relationship between the identified genes and known SKCM targets. Drug prediction and molecular docking further validated the medicinal value of drug targets. Transcriptome differential analysis further validated that there were differences between normal tissues and SKCM for the selected targets. RESULTS: We identified 13 genes significantly associated with the risk of SKCM, including five protective genes and eight harmful genes. The HEIDI test and co-localization analysis further indicates a causal association between genes (SOX4, MAFF) and SKCM, categorized as Class 1 evidence targets. The remaining 11 genes, except for HELZ2 show a moderately causal association with SKCM, categorized as Class 2 evidence targets. Target druggability predictions from DGIdb suggest that SOX4, MAFF, ACSF3, CDK10, SPG7, and TCF25 are likely to be future drug targets. CONCLUSION: The study provides genetic evidence for targeting available drug genes for the treatment of SKCM.

Inhibiting IL11RA to mitigate hepatic metastasis in skin cutaneous melanoma: Comprehensive insights from in vitro and in vivo investigations.

OBJECTIVE: This study aimed to investigate the role of Interleukin-11 receptor alpha (IL11RA) in skin cutaneous melanoma (SKCM) metastasis to the liver. METHODS: Human SKCM cell lines (A375, A375-MA2, SK-MEL-28, RPMI-7951) and primary dermal fibroblasts (HDFa) were utilized to assess IL11RA expression. IL11RA siRNA was transfected into RPMI-7951 and A375-MA2 cells for Wound healing and Transwell invasion assays. Il11ra knockout (KO) mice and wild-type (WT) mice were injected with B16-F10 cells into the spleen to evaluate hepatic melanoma metastasis. Correlation between IL11RA and MMP family genes was explored using online databases, including LinkedOmics, TIMER (Tumor Immune Estimation Resource), and GEPIA (Gene Expression Profiling Interactive Analysis). RT-qPCR and Western blotting were performed for expression analysis of Mmp2 and Mmp9 in liver tissues of mice. The impact of IL11RA on the STAT3 pathway was investigated in vitro and in vivo. RESULTS: Elevated expression of IL11RA was observed in SKCM cell lines compared to normal cells. IL11RA downregulation significantly inhibited migratory and invasive capabilities of A375-MA2 and RPMI-7951 in vitro. Il11ra gene knockout in mice demonstrated a substantial reduction in hepatic melanoma metastasis. Correlation analyses revealed associations between IL11RA and MMP2/MMP8. Il11ra gene knockout significantly decreased Mmp2 expression while increasing Mmp8 in liver tissues. IL11RA correlated positively with STAT3, and its inhibition led to a suppressed STAT3 pathway in SKCM cells and mouse liver tissue. CONCLUSION: IL11RA plays a crucial role in SKCM metastasis, affecting migratory and invasive abilities. Targeting IL11RA may offer a promising avenue for therapeutic interventions in cutaneous melanoma progression.

Identification of novel disulfidptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of skin cutaneous melanoma patients.

BACKGROUND: Skin cutaneous melanoma (SKCM) is an aggressive form of malignant melanoma with poor prognosis and high mortality rates. Disulfidptosis is a newly discovered cell death regulatory mechanism caused by the abnormal accumulation of disulfides. This unique pathway is guiding significant new research to understand cancer progression for targeted treatment. However, the correlation between disulfidptosis with long non-coding RNAs (lncRNAs) in SKCM remains unknown at present. METHODS: The Cancer Genome Atlas database furnished lncRNA expression data and clinical information for SKCM patients. Pearson correlation and Cox regression analyses identified disulfidptosis-related lncRNAs associated with SKCM prognosis. ROC curves and a nomogram validated the model. TME, immune infiltration, GSEA analysis, immune checkpoint gene expression profiling, and drug sensitivity were assessed in high and low-risk groups. Consistent clustering categorized SKCM patients for personalized clinical treatment guidance. RESULTS: A total of twelve disulfidptosis-related lncRNAs were identified for the development of prognosis prediction models. The area under the curve (AUC) values of the ROC curve and the nomogram provided reliable discrimination to evaluate the prognostic potential for SKCM patients. The TME played a crucial role in tumorigenesis, progression and prognosis, and the risk scores were closely related to immune cell infiltration. Meanwhile, the combination of chemotherapy, targeted therapy, and immunotherapy was recommended for low-risk patients based on drug sensitivity and immune efficacy analyses. CONCLUSION: We identified a risk model of twelve disulfidptosis-related lncRNAs that could be used to predict the prognosis of SKCM patients and help guide immunotherapy and chemotherapy for personalized treatment plans.

Prognostic Value of CXCR6 and the Correlation with Immune Infiltration in the Microenvironment of Skin Cutaneous Melanoma.

Increasing evidence has demonstrated that CXCRs are associated with the tumor infiltration of immune cells and regulate the tumor immune response. However, the prognostic value of CXCRs expression in patients with skin cutaneous melanoma (SKCM) remains unclear. In this study, we aimed to investigate the expression characteristics of CXCRs in SKCM tissues, analyze their prognostic value and the correlation with immune cell infiltration. Multiple public databases were used for exploration, including GEPIA2, GSCA, ULCAN, Metascape, STRING, TIMER2.0, HPA, and Cistrome DB database. And a confirmation experiment was conducted on melanoma mice with flow cytometry. Compared with normal tissues, lower expression of CXCR2/7/8 and higher expression of CXCR3/4 were found in SKCM tissues. CXCR3/4/6 were abnormally expressed in each pathological stage. Moreover, CXCRs were closely related to immune-related biological functions, and mainly interacted with CXCLs. The high expression of CXCR3/5/6 indicated better overall survival of patients. Among them, CXCR6 had the most significant prognostic value, and was most related to tumor infiltration of CD8+T cells, which was verified in melanoma mice. Finally, ETS1 and STAT5B were predicted as the transcription factor of CXCR6. Our findings play an important role in the study of prognostic markers in patients with SKCM.

Identification and validation of TME-related signatures to predict prognosis and response to anti-tumor therapies in skin cutaneous melanoma.

The tumor microenvironment (TME) dynamically regulates cancer progression and affects clinical outcomes. This study aimed to identify molecular subtypes and construct a prognostic risk model based on TME-related signatures in skin cutaneous melanoma (SKCM) patients. We categorized SKCM patients based on transcriptome data of SKCM from The Cancer Genome Atlas (TCGA) database and 29 TME-related gene signatures. Differentially expressed genes were identified using univariate Cox regression and Lasso regression analysis, which were used for risk model construction. The robustness of this model was validated in independent external cohorts. Genetic landscape alterations, immune characteristics, and responsiveness to immunotherapy/chemotherapy were evaluated. Three TME-related subtypes were identified, and subtype C3 exhibited the most favorable prognosis, had enriched immune-related pathways, and possessed more infiltration of T_cells_CD8, T_cells_CD4_memory_activated, and Macrophages_M1 but a lower TumorPurity, whereas Macrophages_M2 were increased in subtype C1 and subtype C2. Subtype C1 was more sensitive to Cisplatin, subtype C2 was more sensitive to Temozolomide, and subtype C3 was more sensitive to Paclitaxel; 8 TME-related genes (NOTCH3, HEYL, ZNF703, ABCC2, PAEP, CCL8, HAPLN3, and HPDL) were screened for risk model construction. High-risk patients had dismal prognosis with good prediction performance. Moreover, low-risk patients were more sensitive to Paclitaxel and Temozolomide, whereas high-risk patients were more sensitive to Cisplatin. This risk model had robustness in predicting prognosis in SKCM patients. The results facilitate the understanding of TME-related genes in SKCM and provide a TME-related genes-based predictive model in prognosis and direction of personalized options for SKCM patients.

System analysis based on the pyroptosis-related genes identifes GSDMD as a novel therapy target for skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is the most aggressive skin cancer, accounting for more than 75% mortality rate of skin-related cancers. As a newly identified programmed cell death, pyroptosis has been found to be closely associated with tumor progression. Nevertheless, the prognostic significance of pyroptosis in SKCM remains elusive. METHODS: A total of 469 SKCM samples and 812 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Firstly, differentially expressed pyroptosis-related genes (PRGs) between normal samples and SKCM samples were identified. Secondly, we established a prognostic model based on univariate Cox and LASSO Cox regression analyses, which was validated in the test cohort from GSE65904. Thirdly, a nomogram was used to predict the survival probability of SKCM patients. The R package "pRRophetic" was utilized to identify the drug sensitivity between the low- and high-risk groups. Tumor immune infiltration was evaluated using "immuneeconv" R package. Finally, the function of GSDMD and SB525334 was explored in A375 and A2058 cells. RESULTS: Based on univariate Cox and LASSO regression analyses, we established a prognostic model with identified eight PRGs (AIM2, CASP3, GSDMA, GSDMC, GSDMD, IL18, NLRP3, and NOD2), which was validated in the test cohort. SKCM patients were divided into low- and high-risk groups based on the median of risk score. Kaplan-Meier survival analysis showed that high-risk patients had shorter overall survival than low-risk patients. Additionally, time-dependent ROC curves validated the accuracy of the risk model in predicting the prognosis of SKCM. More importantly, 4 small molecular compounds (SB525334, SR8278, Gemcitabine, AT13387) were identified, which might be potential drugs for patients in different risk groups. Finally, overexpression of GSDMD and SB525334 treatment inhibit the proliferation, migration, and invasion of SKCM cells. CONCLUSION: In this study, we constructed a prognostic model based on PRGs and identified GSDMD as a potential therapeutic target, which provide new insights into SKCM treatment.

Machine learning-derived identification of tumor-infiltrating immune cell-related signature for improving prognosis and immunotherapy responses in patients with skin cutaneous melanoma.

BACKGROUND: Immunoblockade therapy based on the PD-1 checkpoint has greatly improved the survival rate of patients with skin cutaneous melanoma (SKCM). However, existing anti-PD-1 therapeutic efficacy prediction markers often exhibit a poor situation of poor reliability in identifying potential beneficiary patients in clinical applications, and an ideal biomarker for precision medicine is urgently needed. METHODS: 10 multicenter cohorts including 4 SKCM cohorts and 6 immunotherapy cohorts were selected. Through the analysis of WGCNA, survival analysis, consensus clustering, we screened 36 prognostic genes. Then, ten machine learning algorithms were used to construct a machine learning-derived immune signature (MLDIS). Finally, the independent data sets (GSE22153, GSE54467, GSE59455, and in-house cohort) were used as the verification set, and the ROC index standard was used to evaluate the model. RESULTS: Based on computing framework, we found that patients with high MLDIS had poor overall survival and has good prediction performance in all cohorts and in-house cohort. It is worth noting that MLDIS performs better in each data set than almost all models which from 51 prognostic signatures for SKCM. Meanwhile, high MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells in the tumor microenvironment. Additionally, patients suffering from SKCM with high MLDIS were more sensitive to immunotherapy. CONCLUSIONS: Our study identified that MLDIS could provide new insights into the prognosis of SKCM and predict the immunotherapy response in patients with SKCM.

HOPX is a tumor-suppressive biomarker that corresponds to T cell infiltration in skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is the most threatening type of skin cancer. Approximately 55,000 people lose their lives every year due to SKCM, illustrating that it seriously threatens human life and health. Homeodomain-only protein homeobox (HOPX) is the smallest member of the homeodomain family and is widely expressed in a variety of tissues. HOPX is involved in regulating the homeostasis of hematopoietic stem cells and is closely related to the development of tumors such as breast cancer, nasopharyngeal carcinoma, and head and neck squamous cell carcinoma. However, its function in SKCM is unclear, and further studies are needed. METHODS: We used the R language to construct ROC (Receiver-Operating Characteristic) curves, KM (Kaplan‒Meier) curves and nomograms based on databases such as the TCGA and GEO to analyze the diagnostic and prognostic value of HOPX in SKCM patients. Enrichment analysis, immune scoring, GSVA (Gene Set Variation Analysis), and single-cell sequencing were used to verify the association between HOPX expression and immune infiltration. In vitro experiments were performed using A375 cells for phenotypic validation. Transcriptome sequencing was performed to further analyze HOPX gene-related genes and their signaling pathways. RESULTS: Compared to normal cells, SKCM cells had low HOPX expression (p < 0.001). Patients with high HOPX expression had a better prognosis (p < 0.01), and the marker had good diagnostic efficacy (AUC = 0.744). GO/KEGG (Gene Ontology/ Kyoto Encyclopedia of Genes and Genomes) analysis, GSVA and single-cell sequencing analysis showed that HOPX expression is associated with immune processes and high enrichment of T cells and could serve as an immune checkpoint in SKCM. Furthermore, cellular assays verified that HOPX inhibits the proliferation, migration and invasion of A375 cells and promotes apoptosis and S-phase arrest. Interestingly, tumor drug sensitivity analysis revealed that HOPX also plays an important role in reducing clinical drug resistance. CONCLUSION: These findings suggest that HOPX is a blocker of SKCM progression that inhibits the proliferation of SKCM cells and promotes apoptosis. Furthermore, it may be a new diagnostic and prognostic indicator and a novel target for immunotherapy in SKCM patients.

Aspartate beta-hydroxylase domain containing 1 as a prognostic marker associated with immune infiltration in skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is an extremely malignant tumor and accounts for the majority of skin cancer deaths. Aspartate beta-hydroxylase domain containing 1 (ASPHD1) may participate in cancer progression through controlling α-ketoglutarate-dependent dioxygenases. However, its role in skin cutaneous melanoma (SKCM) has not been well studied. METHODS: The gene expression data of ASPDH1 and differentially expressed genes (DEGs) from TCGA and GTEx were evaluated, and verified via the GEO database. Then, we performed GO/KEGG, GSEA, PPI network analysis to analyze the functional implications of the DEGs related to ASPHD1. Then, the association between the ASPHD1 expression and clinical parameters was investigated by Cox regression analysis. Subsequently, the survival time of SKCM patients was evaluated by plotting Kaplan-Meier curves. Moreover, we investigated the correlation between the ASPHD1 expression and lymphocytic infiltration by using the data from TISIDB and TIMER 2.0. Next, we explored the association between ASPHD1 expression and drug sensitivity. Finally, we validate the expression differences by analyzing the results of qPCR, Western blot from human normal epidermal melanocytes and melanoma cells, and immunohistochemistry (IHC) from non-tumor skin as well as melanoma tissues. RESULTS: The ASPHD1 expression level was significantly upregulated in several cancers, including SKCM especially SKCM-metastasis tissues, and patients with an increased ASPHD1 expression had longer overall survival time than low expression ones. The functional enrichment analysis of ASPHD1-related DEGs showed an association with cell development regulation and tumorigenic pathways. Furthermore, the increased ASPHD1 expression level was associated with the level of immunostimulors, immunoinhibitors, chemokines, and TILs, such as CD4+, CD8+ T cell, mast cell, Th2 cell, and dendritic cell. More interesting, we found that ASPHD1 expression was tightly associated with CTLA4 and CD276 which are immune checkpoint markers. Moreover, the upregulated expression of ASPHD1 exhibited higher IC50 values for 24 chemotherapy drugs, including doxorubicin, and masitinib. Finally, the differential expression of ASPHD1 in SKCM was validated by the results of qPCR, Western blot, and IHC. CONCLUSION: The expression of ASPHD1 in SKCM patients is closely related to patient survival. ASPHD1 may participate in the regulation of tumor immune microenvironment. Additionally, it may serve as a prognostic biomarker for SKCM and future in-depth studies are necessary to explore its value.

Disruptions of Circadian Genes in Cutaneous Melanoma-An In Silico Analysis of Transcriptome Databases.

Circadian genes are a set of genes that regulate the body's internal clock and influence various physiological processes, including sleep-wake cycles, metabolism and immune function. Skin cutaneous melanoma (SKCM) is a type of skin cancer that arises from the pigment-producing cells in the skin and is the most deadly form of skin cancer. This study has investigated the relevance of circadian gene expression and immune infiltrations in the outcomes of cutaneous melanoma patients. In the present study, in silico methods based on the GEPIa, TIMER 2.0 and cBioPortal databases were performed, so as to investigate the transcript level and prognostic value of 24 circadian genes in SKCM and their relationship with the immune infiltration level. The in silico analysis showed that significantly more than half of the investigated circadian genes have an altered transcript pattern in cutaneous melanoma compared to normal skin. The mRNA levels of TIMELES and BHLHE41 were upregulated, whereas those of NFIL3, BMAL1, HLF, TEF, RORA, RORC, NR1D1, PER1, PER2, PER3, CRY2 and BHLHE40 were downregulated. The presented research shows that SKCM patients with at least one alteration of their circadian genes have decreased overall survival. Additionally, majority of the circadian genes are significantly corelated with the immune cells' infiltration level. The strongest correlation was found for neutrophils and was followed by circadian genes: NR1D2 r = 0.52 p < 0.0001, BMAL1 r = 0.509 p < 0.0001; CLOCK r = 0.45 p < 0.0001; CSNKA1A1 r = 0.45 p < 0.0001; RORA r = 0.44 p < 0.0001. The infiltration level of immune cells in skin tumors has been associated with patient prognosis and treatment response. Circadian regulation of immune cell infiltration may further contribute to these prognostic and predictive markers. Examining the correlation between circadian rhythm and immune cell infiltration can provide valuable insights into disease progression and guide personalized treatment decisions.

Systematic Multiomic Analysis of PKHD1L1 Gene Expression and Its Role as a Predicting Biomarker for Immune Cell Infiltration in Skin Cutaneous Melanoma and Lung Adenocarcinoma.

The identification of genetic factors that regulate the cancer immune microenvironment is important for understanding the mechanism of tumor progression and establishing an effective treatment strategy. Polycystic kidney and hepatic disease 1-like protein 1 (PKHD1L1) is a large transmembrane protein that is highly expressed in immune cells; however, its association with tumor progression remains unclear. Here, we systematically analyzed the clinical relevance of PKHD1L1 in the tumor microenvironment in multiple cancer types using various bioinformatic tools. We found that the PKHD1L1 mRNA expression levels were significantly lower in skin cutaneous melanoma (SKCM) and lung adenocarcinoma (LUAD) than in normal tissues. The decreased expression of PKHD1L1 was significantly associated with unfavorable overall survival (OS) in SKCM and LUAD. Additionally, PKHD1L1 expression was positively correlated with the levels of infiltrating B cells, cluster of differentiation (CD)-8+ T cells, and natural killer (NK) cells, suggesting that the infiltration of immune cells could be associated with a good prognosis due to increased PKHD1L1 expression. Gene ontology (GO) analysis also revealed the relationship between PKHD1L1-co-altered genes and the activation of lymphocytes, including B and T cells. Collectively, this study shows that PKHD1L1 expression is positively correlated with a good prognosis via the induction of immune infiltration, suggesting that PKHD1L1 has potential prognostic value in SKCM and LUAD.

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OBJECTIVES: Malignant melanoma is a highly malignant and heterogeneous skin cancer. Although immunotherapy has improved survival rates, the inhibitory effect of tumor microenvironment has weakened its efficacy. To improve survival and treatment strategies, we need to develop immune-related prognostic models. Based on the analysis of the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Sequence Read Archive (SRA) database, this study aims to establish an immune-related prognosis prediction model, and to evaluate the tumor immune microenvironment by risk score to guide immunotherapy. METHODS: Skin cutaneous melanoma (SKCM) transcriptome sequencing data and corresponding clinical information were obtained from the TCGA database, differentially expressed genes were analyzed, and prognostic models were developed using univariate Cox regression, the LASSO method, and stepwise regression. Differentially expressed genes in prognostic models confirmed by real-time reverse transcription PCR (real-time RT-PCR) and Western blotting. Survival analysis was performed by using the Kaplan-Meier method, and the effect of the model was evaluated by time-dependent receiver operating characteristic curve as well as multivariate Cox regression, and the prognostic model was validated by 2 GEO melanoma datasets. Furthermore, correlations between risk score and immune cell infiltration, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) score, immune checkpoint mRNA expression levels, tumor immune cycle, or tumor immune micro-environmental pathways were analyzed. Finally, we performed association analysis for risk score and the efficacy of immunotherapy. RESULTS: We identified 4 genes that were differentially expressed in TCGA-SKCM datasets, which were mainly associated with the tumor immune microenvironment. A prognostic model was also established based on 4 genes. Among 4 genes, the mRNA and protein levels of killer cell lectin like receptor D1 (KLRD1), leukemia inhibitory factor (LIF), and cellular retinoic acid binding protein 2 (CRABP2) genes in melanoma tissues differed significantly from those in normal skin (all P<0.01). The prognostic model was a good predictor of prognosis for patients with SKCM. The patients with high-risk scores had significantly shorter overall survival than those with low-risk scores, and consistent results were achieved in the training cohort and multiple validation cohorts (P<0.001). The risk score was strongly associated with immune cell infiltration, ESTIMATE score, immune checkpoint mRNA expression levels, tumor immune cycle, and tumor immune microenvironmental pathways (P<0.001). The correlation analysis showed that patients with the high-risk scores were in an inhibitory immune microenvironment based on the prognostic model (P<0.01). CONCLUSIONS: The immune-related SKCM prognostic model constructed in this study can effectively predict the prognosis of SKCM patients. Considering its close correlation to the tumor immune microenvironment, the model has some reference value for clinical immunotherapy of SKCM.

The Identification of Prognostic and Metastatic Alternative Splicing in Skin Cutaneous Melanoma.

Skin cutaneous melanoma (SKCM) is a type of highly invasive cancer originated from melanocytes. It is reported that aberrant alternative splicing (AS) plays an important role in the neoplasia and metastasis of many types of cancer. Therefore, we investigated whether ASEs of pre-RNA have such an influence on the prognosis of SKCM and the related mechanism of ASEs in SKCM. The RNA-seq data and ASEs data for SKCM patients were obtained from the TCGA and TCGASpliceSeq database. The univariate Cox regression revealed 1265 overall survival-related splicing events (OS-SEs). Screened by Lasso regression, 4 OS-SEs were identified and used to construct an effective prediction model (AUC: .904), whose risk score was proved to be an independent prognostic factor. Furthermore, Kruskal-Wallis test and Mann-Whitney-Wilcoxon test showed that an aberrant splicing type of aminoacyl tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2) regulated by CDC-like kinase 1 (CLK1) was associated with the metastasis and stage of SKCM. Besides, the overlapped signal pathway for AIMP2 was galactose metabolism identified by the co-expression analysis. External database validation also confirmed that AIMP2, CLK1, and the galactose metabolism were associated with the metastasis and stage of SKCM patients. ChIP-seq and ATAC-seq methods further confirmed the transcription regulation of CLK1, AIMP2, and other key genes, whose cellular expression was detected by Single Cell Sequencing. In conclusion, we proposed that CLK1-regulated AIMP2-78704-ES might play a critical role in the tumorigenesis and metastasis of SKCM via galactose metabolism. Besides, we established an effective model with MTMR14-63114-ES, URI1-48867-ES, BATF2-16724-AP, and MED22-88025-AP to predict the metastasis and prognosis of SKCM patients.

Identification of a novel tumour microenvironment-based prognostic biomarker in skin cutaneous melanoma.

Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)-based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype-Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16-15, PRSS35, SLC7A10, IGHV1-69D and IGHV2-26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME-based prognostic biomarker in SKCM, and functional analysis enriched immune-related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM.

Correlation of CCL8 expression with immune cell infiltration of skin cutaneous melanoma: potential as a prognostic indicator and therapeutic pathway.

BACKGROUND: The tumor microenvironment (TME) is critical in the progression and metastasis of skin cutaneous melanoma (SKCM). Differences in tumor-infiltrating immune cells (TICs) and their gene expression have been linked to cancer prognosis. Given that immunotherapy can be effective against SKCM, we aimed to identify key genes that regulate the immunological state of the TME in SKCM. METHODS: Data from 471 SKCM patients in the The Cancer Genome Atlas were analyzed using ESTIMATE algorithms to generate an ImmuneScore, StromalScore, and EstimateScore for each patient. Patients were classified into low- or high-score groups based on median values, then compared in order to identify differentially expressed genes (DEGs). Then a protein-protein interaction (PPI) network was developed, and a prognostic model was created using uni- and multivariate Cox regression as well as the least absolute shrinkage and selection operator (LASSO). Key DEGs were identified using the web-based tool GEPIA. Profiles of TIC subpopulations in each patient were analyzed using CIBORSORT, and possible correlations between key DEG expression and TICs were explored. Levels of CCL8 were determined in SKCM and normal skin tissue using immunohistochemistry. RESULTS: Two scores correlated positively with the prognosis of SKCM patients. Comparison of the low- and high-score groups revealed 1684 up-regulated and 18 down-regulated DEGs, all of which were enriched in immune-related functions. The prognostic model identified CCL8 as a key gene, which CIBERSORT found to correlate with M1 macrophages. Immunohistochemistry revealed strong expression in SKCM tissue, but failed to detect the protein in normal skin tissue. CONCLUSIONS: CCL8 is a potential prognostic marker for SKCM, and it may become an effective target for melanoma in which M1 macrophages play an important role.

Comprehensive analysis identifies IFI16 as a novel signature associated with overall survival and immune infiltration of skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is the most common skin tumor with high mortality. The unfavorable outcome of SKCM urges the discovery of prognostic biomarkers for accurate therapy. The present study aimed to explore novel prognosis-related signatures of SKCM and determine the significance of immune cell infiltration in this pathology. METHODS: Four gene expression profiles (GSE130244, GSE3189, GSE7553 and GSE46517) of SKCM and normal skin samples were retrieved from the GEO database. Differentially expressed genes (DEGs) were then screened, and the feature genes were identified by the LASSO regression and Boruta algorithm. Survival analysis was performed to filter the potential prognostic signature, and GEPIA was used for preliminary validation. The area under the receiver operating characteristic curve (AUC) was obtained to evaluate discriminatory ability. The Gene Set Variation Analysis (GSVA) was performed, and the composition of the immune cell infiltration in SKCM was estimated using CIBERSORT. At last, paraffin-embedded specimens of primary SKCM and normal skin tissues were collected, and the signature was validated by fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC). RESULTS: Totally 823 DEGs and 16 feature genes were screened. IFI16 was identified as the signature associated with overall survival of SKCM with a great discriminatory ability (AUC > 0.9 for all datasets). GSVA noticed that IFI16 might be involved in apoptosis and ultraviolet response in SKCM, and immune cell infiltration of IFI16 was evaluated. At last, FISH and IHC both validated the differential expression of IFI16 in SKCM. CONCLUSIONS: In conclusion, our comprehensive analysis identified IFI16 as a signature associated with overall survival and immune infiltration of SKCM, which may play a critical role in the occurrence and development of SKCM.

Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule.

Prostate cancer is one of the leading causes of death in men worldwide, revealing a substantial heterogeneity in terms of molecular and clinical behaviors. Tumor infiltrating immune cell is associated with prognosis and response to immunotherapy in several cancer types. However, until now, the immune infiltrate profile of distinct subtypes for prostate cancer remains poorly characterized. In this study, using immune infiltration profiles as well as transcriptomic datasets, we characterized this subtype of prostate tumors. We observed that the FLI1 subtype of prostate tumors was highly enriched in immune system processes, immune related KEGG pathways and biological processes. We also expanded this approach to explore the immune infiltration profile of the high FLI1 expression subtype for skin cutaneous melanoma, similar results were found. Investigation of the association of immune infiltration features with the FLI1 expression demonstrated that many important features were associated with the FLI1 expression.