The integration of single-cell sequencing, TCGA, and GEO data analysis revealed that PRRT3-AS1 is a biomarker and therapeutic target of SKCM

Study on the metastatic mechanism of LINC00115 in adenocarcinoma of the Esophagogastric junction

Adenocarcinoma of the esophagogastric junction (AEG) is a common and deadly cancer, and an in-depth investigation of its molecular mechanisms of metastasis is crucial for discovering new therapeutic targets. This study explores the role of the long non-coding RNA (lncRNA) LINC00115 in AEG metastasis and its underlying mechanisms. Through the analysis of 108 pairs of AEG cancer tissues and matched adjacent tissues, we found a significant upregulation of LINC00115 in AEG tissues, closely associated with TNM staging and lymph node metastasis. Utilizing cell counting kit-8 (CCK-8) assays, colony formation experiments, wound healing assays, flow cytometry for apoptosis and cell cycle analysis, and Transwell assays, we have confirmed that LINC00115 significantly promotes proliferation, migration, and invasion of AEG cells in vitro. Animal experiments further validate the role of LINC00115 in promoting tumor growth and metastasis in vivo. Additionally, our nuclear-cytoplasmic fractionation experiments and RNA fluorescence in situ hybridization (FISH) reveal that LINC00115, along with its interacting protein KH-Type splicing regulatory protein (KHSRP), predominantly localizes to the cell nucleus. By conducting RNA pull-down assays and mass spectrometry (MS) analysis, we have identified a direct interaction between LINC00115 and KHSRP protein and further determined their binding sites through catRAPID and ENCORI databases. This study provides evidence of LINC00115 as a novel biomarker and potential therapeutic target for AEG and offers a fresh perspective on understanding the molecular mechanisms of AEG metastasis.

Comprehensive Analysis Reveals the Potential Diagnostic Value of Biomarkers Associated With Aging and Circadian Rhythm in Knee Osteoarthritis

OBJECTIVE

Knee osteoarthritis (KOA) is characterized by structural changes. Aging is a major risk factor for KOA. Therefore, the objective of this study was to examine the role of genes related to aging and circadian rhythms in KOA.

METHODS

This study identified differentially expressed genes (DEGs) by comparing gene expression levels between normal and KOA samples from the GEO database. Subsequently, we intersected the DEGs with aging-related circadian rhythm genes to obtain a set of aging-associated circadian rhythm genes differentially expressed in KOA. Next, we conducted Mendelian randomization (MR) analysis, using the differentially expressed aging-related circadian rhythm genes in KOA as the exposure factors, their SNPs as instrumental variables, and KOA as the outcome event, to explore the causal relationship between these genes and KOA. We then performed Gene Set Enrichment Analysis (GSEA) to investigate the pathways associated with the selected biomarkers, conducted immune infiltration analysis, built a competing endogenous RNA (ceRNA) network, and performed molecular docking studies. Additionally, the findings and functional roles of the biomarkers were further validated through experiments on human cartilage tissue and cell models.

RESULTS

A total of 75 differentially expressed aging-circadian rhythm related genes between the normal group and the KOA group were identified by difference analysis, primarily enriched in the circadian rhythm pathway. Two biomarkers (PFKFB4 and DDIT4) were screened by MR analysis. Then, immune infiltration analysis showed significant differences in three types of immune cells (resting dendritic cells, resting mast cells, and M2 macrophages), between the normal and KOA groups. Drug prediction and molecular docking results indicated stable binding of PFKFB4 to estradiol and bisphenol_A, while DDIT4 binds stably to nortriptyline and trimipramine. Finally, cell lines with stable expression of the biomarkers were established by lentiviral infection and resistance screening, Gene expression was significantly elevated in overexpressing cells of PFKFB4 and DDIT4 and reversed the proliferation and migration ability of cells after IL-1β treatment.

CONCLUSIONS

Two diagnostic and therapeutic biomarkers associated with aging-circadian rhythm in KOA were identified. Functional analysis, molecular mechanism exploration, and experimental validation further elucidated their roles in KOA, offering novel perspectives for the prevention and treatment of the disease.

From Genes to Healing: The Protective Mechanisms of Poria cocos Polysaccharide in Endometrial Health

The aim of this study is to investigate the therapeutic effect of Poria cocos polysaccharide (PCP) on bovine endometritis. Initially, an inflammation model was induced using LPS-treated bovine endometrial epithelial cells (BEND) to identify the differentially expressed genes (DEGs) between the control and LPS groups by transcriptome sequencing, and GO functional annotation and KEGG enrichment analysis were performed. Subsequently, the mechanism of PCP treatment for endometritis was further evaluated using protein immunoblotting and real-time fluorescence quantitative analysis. Finally, the efficacy of PCP in treating endometritis was evaluated using a rat model of endometritis established with a mixed bacterial infection. The results show that transcriptome sequencing identified 4367 DEGs, with enrichment analysis highlighting the primary influences on the cell cycle and apoptosis signaling pathways. Following treatment of BEND with LPS resulted in cell apoptosis and inflammatory response. However, the introduction of PCP intervention significantly inhibited the progression of apoptosis and inflammation. Animal test results indicate that PCP significantly decreases the levels of serum inflammatory in rats suffering from endometritis and enhances antioxidant capacity. Furthermore, it effectively improved uterine swelling and tissue vacuolization caused by bacterial infection. These findings suggest that PCP could alleviate endometritis by modulating the inflammatory response and suppressing cell apoptosis. Poria cocos polysaccharides demonstrate significant potential for applications in immune modulation, anti-inflammatory responses, and antioxidant activities. Their high safety profile makes them suitable candidates as alternative therapeutic agents for the treatment of endometritis in the veterinary field.

Diagnostic and therapeutic role of non-coding RNAs regulating programmed cell death in melanoma

lncRNAs (long non-coding RNAs) are heterogeneous RNA molecules that modulate various cellular processes, such as proliferation, differentiation, migration, invasion, and apoptosis, via different mechanisms. An increasing amount of research indicates that abnormal expression of lncRNA influences the development of drug resistance as well as the genesis and advancement of cancer, including melanoma. Furthermore, they are attractive biomarkers for non-invasive cancer diagnostics due to their strongly modulated expression and improved tissue and disease specificity. This review offers a succinct overview of the present understanding concerning the potential diagnostic biomarker potential of lncRNAs in melanoma. Cell death occurs frequently during growth and throughout life and is an active, organized, and genetically determined process. It is essential for the regulation of homeostasis. Controlled cell death and non-programmed cell death are both forms of cell death. The most prevalent forms of regulatory cell death are pyroptosis, ferroptosis, autophagy, necroptosis, necrosis, and apoptosis. Ferroptosis, pyroptosis, and autophagy are less common forms of cell death compared to necrosis, apoptosis, and necroptosis. ncRNAs are regulatory RNA molecules that are not involved in encoding proteins. They primarily consist of circular RNAs (circ RNAs), lncRNAs, and microRNAs (miRNAs). Moreover, non-coding RNAs have the ability to modulate tumor cell autophagy, pyroptosis, and ferroptosis at the transcriptional or post-transcriptional stage, as well as function as oncogenes and tumor suppressor genes, which can have considerable effects on the incidence and growth of tumors. This review concentrated on the recent advancements in the research of the diagnostic and therapeutic functions of ncRNAs in the regulation of programmed cell death in melanoma.

Single-cell RNA sequencing analysis reveals potential key prognostic markers in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the sixth most frequently diagnosed cancer worldwide accompanied by a low 5-year survival rate. In our study, we aimed to analyze relevant genetic features that can predict the prognosis of HCC patients by single-cell RNA sequencing (scRNA-seq).

METHODS

Single-cell RNA-seq data of HCC were analyzed from the Gene Expression Omnibus (GEO) database. Using the Seurat package, we performed quality control to remove cells with low quality. After normalization, we detected highly variable genes across the single cells. Then, cell clustering and Cell type annotation were performed using highly variable genes. Then, functional enrichment analyses were performed by GO and KEGG, and cell-cell communication analysis, trajectory analysis were conducted. LASSO-Cox regression analysis was used to perform Survival analysis and ROC evaluation for high and low-risk groups. Validation of the expressions and survival prognosis of the screened genes in HCC. Expression levels of the genes were analyzed by RT-qPCR and western blot in normal liver cell line THLE-3 and HCC cell lines (HuH7, HCCLM9, and HCCLM13).

RESULTS

A total of 2208 up-regulated and 1447 down-regulated genes were identified in HCC samples. These differentially expressed genes (DEGs) were enriched in several cytokine-related pathways and the MIF-CD74/CXCR4 signaling pathway. By integrating large amounts of RNA sequencing data, we identified 566 prognostic genes associated with HCC cells. Eleven genes were screened using the LASSO-COX risk factor model. Stratifying patients into high- or low-risk groups based on these genes allowed us to effectively predict their survival and ROC curve. Five genes were further found to be associated with poor survival prognosis in HCC and were notably overexpressed in HCC cell lines compared to normal liver cell line.

CONCLUSION

This study revealed potential prognostic marker genes in HCC patients, providing insights into predicting patients' survival rates.

Investigating MATN3 and ASPN as novel drivers of gastric cancer progression via EMT pathways

Gastric cancer (GC) is a leading cause of cancer-related deaths globally, necessitating the identification of novel therapeutic targets. This study investigates the roles of MATN3 and ASPN in GC progression via the epithelial-mesenchymal transition (EMT) pathway. Analysis of the Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) dataset revealed that both MATN3 and ASPN are significantly upregulated in GC tissues and correlate with poor patient survival. Protein-protein interaction and co-expression analyses confirmed a direct interaction between MATN3 and ASPN, suggesting their synergistic role in EMT activation. Functional assays demonstrated that MATN3 promotes GC cell proliferation, migration, and invasion, while its knockdown inhibits these malignant behaviors and induces apoptosis. ASPN overexpression further amplified these oncogenic effects. In vivo, studies in a mouse model corroborated that co-overexpression of MATN3 and ASPN enhances tumor growth and metastasis. These findings highlight the MATN3-ASPN axis as a potential therapeutic target in GC, offering new insights into the molecular mechanisms driving GC progression.

Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanoma

Background

Immunotherapy has revolutionized skin cutaneous melanoma treatment, but response variability due to tumor heterogeneity necessitates robust biomarkers for predicting immunotherapy response.

Methods

We used weighted gene co-expression network analysis (WGCNA), consensus clustering, and 10 machine learning algorithms to develop the immunotherapy-related gene model (ITRGM) signature. Multi-omics analyses included bulk and single-cell RNA sequencing of melanoma patients, mouse bulk RNA sequencing, and pathology sections of melanoma patients.

Results

We identified 66 consensus immunotherapy prognostic genes (CITPGs) using WGCNA and differentially expressed genes (DEGs) from two melanoma cohorts. The CITPG-high group showed better prognosis and enriched immune activities. DEGs between CITPG-high and CITPG-low groups in the TCGA-SKCM cohort were analyzed in three additional melanoma cohorts using univariate Cox regression, resulting in 44 consensus genes. Using 101 machine learning algorithm combinations, we constructed the ITRGM signature based on seven model genes. The ITRGM outperformed 37 published signatures in predicting immunotherapy prognosis across the training cohort, three testing cohorts, and a meta-cohort. It effectively stratified patients into high-risk or low-risk groups for immunotherapy response. The low-risk group, with high levels of model genes, correlated with increased immune characteristics such as tumor mutation burden and immune cell infiltration, indicating immune-hot tumors with a better prognosis. The ITRGM's relationship with the tumor immune microenvironment was further validated in our experiments using pathology sections with GBP5, an important model gene, and CD8 IHC analysis. The ITRGM also predicted better immunotherapy response in eight cohorts, including urothelial carcinoma and stomach adenocarcinoma, indicating broad applicability.

Conclusions

The ITRGM signature is a stable and robust predictor for stratifying melanoma patients into 'immune-hot' and 'immune-cold' tumors, enhancing prognosis and response to immunotherapy.

A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology

The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.

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.

FAM20A: a potential diagnostic biomarker for lung squamous cell carcinoma

Background

Lung squamous cell carcinoma (LUSC) ranks among the carcinomas with the highest incidence and dismal survival rates, suffering from a lack of effective therapeutic strategies. Consequently, biomarkers facilitating early diagnosis of LUSC could significantly enhance patient survival. This study aims to identify novel biomarkers for LUSC.

Methods

Utilizing the TCGA, GTEx, and CGGA databases, we focused on the gene encoding Family with Sequence Similarity 20, Member A (FAM20A) across various cancers. We then corroborated these bioinformatic predictions with clinical samples. A range of analytical tools, including Kaplan-Meier, MethSurv database, Wilcoxon rank-sum, Kruskal-Wallis tests, Gene Set Enrichment Analysis, and TIMER database, were employed to assess the diagnostic and prognostic value of FAM20A in LUSC. These tools also helped evaluate immune cell infiltration, immune checkpoint genes, DNA repair-related genes, DNA methylation, and tumor-related pathways.

Results

FAM20A expression was found to be significantly reduced in LUSC, correlating with lower survival rates. It exhibited a negative correlation with key proteins in DNA repair signaling pathways, potentially contributing to LUSC's radiotherapy resistance. Additionally, FAM20A showed a positive correlation with immune checkpoints like CTLA-4, indicating potential heightened sensitivity to immunotherapies targeting these checkpoints.

Conclusion

FAM20A emerges as a promising diagnostic and prognostic biomarker for LUSC, offering potential clinical applications.

Integration of single-cell and bulk RNA sequencing data reveals that CYTOR is a potential prognostic and immunotherapeutic response marker for skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is a highly malignant tumor that is prone to immune escape and distant metastasis. Immunotherapy is considered to be the best treatment for patients with SKCM. However, not all patients benefit from it. We observed a significant differential expression of the lncRNA CYTOR in patients with SKCM based on single-cell and bulk RNA sequencing data mining results. The results showed that compared to normal tissue lncRNA CYTOR expression was significantly upregulated in SKCM tissue. Subsequently, we validated this finding in clinical samples, and we also found that the expression of lncRNA CYTOR in SKCM was higher as it progressed. lncRNA CYTOR was differentially expressed in patients who responded to immunotherapy, suggesting that it may serve as a biomarker to predict the efficacy of SKCM immunotherapy. In-depth analysis revealed that lncRNA CYTOR expression was strongly correlated with immune cell infiltration, immune response, and immune checkpoint expression. Meanwhile, our experiments revealed that CYTOR affects SKCM cell invasion and clone formation and is associated with the activation of the EMT pathway. In summary, our findings illustrate, for the first time, the value of CYTOR as a potential prognostic and immunotherapeutic response marker in SKCM.

Comprehensive data mining reveals RTK/RAS signaling pathway as a promoter of prostate cancer lineage plasticity through transcription factors and CNV

Prostate cancer lineage plasticity is a key driver in the transition to neuroendocrine prostate cancer (NEPC), and the RTK/RAS signaling pathway is a well-established cancer pathway. Nevertheless, the comprehensive link between the RTK/RAS signaling pathway and lineage plasticity has received limited investigation. In particular, the intricate regulatory network governing the interplay between RTK/RAS and lineage plasticity remains largely unexplored. The multi-omics data were clustered with the coefficient of argument and neighbor joining algorithm. Subsequently, the clustered results were analyzed utilizing the GSEA, gene sets related to stemness, multi-lineage state datasets, and canonical cancer pathway gene sets. Finally, a comprehensive exploration of the data based on the ssGSEA, WGCNA, GSEA, VIPER, prostate cancer scRNA-seq data, and the GPSAdb database was conducted. Among the six modules in the clustering results, there are 300 overlapping genes, including 3 previously unreported prostate cancer genes that were validated to be upregulated in prostate cancer through RT-qPCR. Function Module 6 shows a positive correlation with prostate cancer cell stemness, multi-lineage states, and the RTK/RAS signaling pathway. Additionally, the 19 leading-edge genes of the RTK/RAS signaling pathway promote prostate cancer lineage plasticity through a complex network of transcriptional regulation and copy number variations. In the transcriptional regulation network, TP63 and FOXO1 act as suppressors of prostate cancer lineage plasticity, whereas RORC exerts a promoting effect. This study provides a comprehensive perspective on the role of the RTK/RAS pathway in prostate cancer lineage plasticity and offers new clues for the treatment of NEPC.

A novel long non-coding RNA LINC00524 facilitates invasion and metastasis through interaction with TDP43 in breast cancer

Breast cancer (BC) remains a significant health concern worldwide, with metastasis being a primary contributor to patient mortality. While advances in understanding the disease's progression continue, the underlying mechanisms, particularly the roles of long non-coding RNAs (lncRNAs), are not fully deciphered. In this study, we examined the influence of the lncRNA LINC00524 on BC invasion and metastasis. Through meticulous analyses of TCGA and GEO data sets, we observed a conspicuous elevation of LINC00524 expression in BC tissues. This increased expression correlated strongly with a poorer prognosis for BC patients. A detailed Gene Ontology analysis suggested that LINC00524 likely exerts its effects through RNA-binding proteins (RBPs) mechanisms. Experimentally, LINC00524 was demonstrated to amplify BC cell migration, invasion and proliferation in vitro. Additionally, in vivo tests showed its potent role in promoting BC cell growth and metastasis. A pivotal discovery was LINC00524's interaction with TDP43, which leads to the stabilization of TDP43 protein expression, an element associated with unfavourable BC outcomes. In essence, our comprehensive study illuminates how LINC00524 accelerates BC invasion and metastasis by binding to TDP43, presenting potential avenues for therapeutic interventions.

Long non-coding RNA as a potential diagnostic and prognostic biomarker in melanoma: A systematic review and meta-analysis

Recently, long noncoding RNAs (lncRNAs) have been applied as biomarkers for melanoma patients. In this systematic review and meta-analysis, we investigated the diagnostic and prognostic value of lncRNAs. We used the keywords 'lncRNA' and 'melanoma' to search databases for studies published before June 14th, 2023. The specificity, sensitivity and AUC were utilized to assess diagnostic accuracy and the prognostic value was assessed using overall survival, progression-free survival and disease-free survival hazard ratios. After screening 1191 articles, we included seven studies in the diagnostic evaluation section and 17 studies in the prognosis evaluation section. The Reitsma bivariate model estimated a cumulative sensitivity of 0.724 (95% CI: 0.659-0.781, p < 0.001) and specificity of 0.812 (95% CI: 0.752-0.859, p < 0.001). The pooled AUC was 0.780 (95% CI: 0.749-0.811, p < 0.0001). The HR for overall survival was 2.723 (95% CI: 2.259-3.283, p < 0.0001). Two studies reported an HR for overall survival less than one, with an HR of 0.348 (95% CI: 0.200-0.607, p < 0.0002). The HR for progression-free survival was 2.913 (95% CI: 2.050-4.138, p < 0.0001). Four studies reported an HR less than one, with an HR of 0.457 (95% CI: 0.256-0.817). The HR for disease-free survival was 2.760 (95% CI: 2.009-3.792, p < 0.0001). In conclusion, the expression of lncRNAs in melanoma patients affects survival and prognosis. LncRNAs can also be employed as diagnostic biomarkers.

Combined signature of G protein-coupled receptors and tumor microenvironment provides a prognostic and therapeutic biomarker for skin cutaneous melanoma

BACKGROUND

G protein-coupled receptors (GPCRs) have been shown to have an important role in tumor development and metastasis, and abnormal expression of GPCRs is significantly associated with poor prognosis of tumor patients. In this study, we analyzed the GPCRs-related gene (GPRGs) and tumor microenvironment (TME) in skin cutaneous melanoma (SKCM) to construct a prognostic model to help SKCM patients obtain accurate clinical treatment strategies.

METHODS

SKCM expression data and clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differential expression analysis, LASSO algorithm, and univariate and multivariate cox regression analysis were used to screen prognosis-related genes (GPR19, GPR146, S1PR2, PTH1R, ADGRE5, CXCR3, GPR143, and OR2I1P) and multiple prognosis-good immune cells; the data set was analyzed according to above results and build up a GPR-TME classifier. The model was further subjected to immune infiltration, functional enrichment, tumor mutational load, immunotherapy prediction, and scRNA-seq data analysis. Finally, cellular experiments were conducted to validate the functionality of the key gene GPR19 in the model.

RESULTS

The findings indicate that high expression of GPRGs is associated with a poor prognosis in patients with SKCM, highlighting the significant role of GPRGs and the tumor microenvironment (TME) in SKCM development. Notably, the group characterized by low GPR expression and a high TME exhibited the most favorable prognosis and immunotherapeutic efficacy. Furthermore, cellular assays demonstrated that knockdown of GPR19 significantly reduced the proliferation, migration, and invasive capabilities of melanoma cells in A375 and A2058 cell lines.

CONCLUSION

This study provides novel insights for the prognosis evaluation and treatment of melanoma, along with the identification of a new biomarker, GPR19.

An update on methods for detection of prognostic and predictive biomarkers in melanoma

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

Construction and Identification of an NLR-Associated Prognostic Signature Revealing the Heterogeneous Immune Response in Skin Cutaneous Melanoma

Background

Skin cutaneous melanoma (SKCM) is the deadliest dermatology tumor. Ongoing researches have confirmed that the NOD-like receptors (NLRs) family are crucial in driving carcinogenesis. However, the function of NLRs signaling pathway-related genes in SKCM remains unclear.

Objective

To establish and identify an NLRs-related prognostic signature and to explore its predictive power for heterogeneous immune response in SKCM patients.

Methods

Establishment of the predictive signature using the NLRs-related genes by least absolute shrinkage and selection operator-Cox regression analysis (LASSO-COX algorithm). Through univariate and multivariate COX analyses, NLRs signature's independent predictive effectiveness was proven. CIBERSORT examined the comparative infiltration ratios of 22 distinct types of immune cells. RT-qPCR and immunohistochemistry implemented expression validation for critical NLRs-related prognostic genes in clinical samples.

Results

The prognostic signature, including 7 genes, was obtained by the LASSO-Cox algorithm. In TCGA and validation cohorts, SKCM patients with higher risk scores had remarkably poorer overall survival. The independent predictive role of this signature was confirmed by multivariate Cox analysis. Additionally, a graphic nomogram demonstrated that the risk score of the NLRs signature has high predictive accuracy. SKCM patients in the low-risk group revealed a distinct immune microenvironment characterized by the significantly activated inflammatory response, interferon-α/γ response, and complement pathways. Indeed, several anti-tumor immune cell types were significantly accumulated in the low-risk group, including M1 macrophage, CD8 T cell, and activated NK cell. It is worth noting that our NLRs prognostic signature could serve as one of the promising biomarkers for predicting response rates to immune checkpoint blockade (ICB) therapy. Furthermore, the results of expression validation (RT-qPCR and IHC) were consistent with the previous analysis.

Conclusion

A promising NLRs signature with excellent predictive efficacy for SKCM was developed.