Integrative lactylation and tumor microenvironment signature as prognostic and therapeutic biomarkers in skin cutaneous melanoma.

BACKGROUND: The incidence of skin cutaneous melanoma (SKCM), one of the most aggressive and lethal skin tumors, is increasing worldwide. However, for advanced SKCM, we still lack an accurate and valid way to predict its prognosis, as well as novel theories to guide the planning of treatment options for SKCM patients. Lactylation (LAC), a novel post-translational modification of histones, has been shown to promote tumor growth and inhibit the antitumor response of the tumor microenvironment (TME) in a variety of ways. We hope that this study will provide new ideas for treatment options for SKCM patients, as well as research on the molecular mechanisms of SKCM pathogenesis and development. METHODS: At the level of the RNA sequencing set (TCGA, GTEx), we used differential expression analysis, LASSO regression analysis, and multifactor Cox regression analysis to screen for prognosis-related genes and calculate the corresponding LAC scores. The content of TME cells in the tumor tissue was calculated using the CIBERSORT algorithm, and the TME score was calculated based on its results. Finally, the LAC-TME classifier was established and further analyzed based on the two scores, including the construction of a prognostic model, analysis of clinicopathological characteristics, and correlation analysis of tumor mutation burden (TMB) and immunotherapy. Based on single-cell RNA sequencing data, this study analyzed the cellular composition in SKCM tissues and explored the role of LAC scores in intercellular communication. To validate the functionality of the pivotal gene CLPB in the model, cellular experiments were ultimately executed. RESULTS: We screened a total of six prognosis-related genes (NDUFA10, NDUFA13, CLPB, RRM2B, HPDL, NARS2) and 7 TME cells with good prognosis. According to Kaplan-Meier survival analysis, we found that the LAClow/TMEhigh group had the highest overall survival (OS) and the LAChigh/TMElow group had the lowest OS (p value < 0.05). In further analysis of immune infiltration, tumor microenvironment (TME), functional enrichment, tumor mutational load and immunotherapy, we found that immunotherapy was more appropriate in the LAClow/TMEhigh group. Moreover, the cellular assays exhibited substantial reductions in proliferation, migration, and invasive potentials of melanoma cells in both A375 and A2058 cell lines upon CLPB knockdown. CONCLUSIONS: The prognostic model using the combined LAC score and TME score was able to predict the prognosis of SKCM patients more consistently, and the LAC-TME classifier was able to significantly differentiate the prognosis of SKCM patients across multiple clinicopathological features. The LAC-TME classifier has an important role in the development of immunotherapy regimens for SKCM patients.

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.

Integrated multi-omics identified the novel intratumor microbiome-derived subtypes and signature to predict the outcome, tumor microenvironment heterogeneity, and immunotherapy response for pancreatic cancer patients.

Background: The extremely malignant tumour known as pancreatic cancer (PC) lacks efficient prognostic markers and treatment strategies. The microbiome is crucial to how cancer develops and responds to treatment. Our study was conducted in order to better understand how PC patients' microbiomes influence their outcome, tumour microenvironment, and responsiveness to immunotherapy. Methods: We integrated transcriptome and microbiome data of PC and used univariable Cox regression and Kaplan-Meier method for screening the prognostic microbes. Then intratumor microbiome-derived subtypes were identified using consensus clustering. We utilized LASSO and Cox regression to build the microbe-related model for predicting the prognosis of PC, and utilized eight algorithms to assess the immune microenvironment feature. The OncoPredict package was utilized to predict drug treatment response. We utilized qRT-PCR to verify gene expression and single-cell analysis to reveal the composition of PC tumour microenvironment. Results: We obtained a total of 26 prognostic genera in PC. And PC samples were divided into two microbiome-related subtypes: Mcluster A and B. Compared with Mcluster A, patients in Mcluster B had a worse prognosis and higher TNM stage and pathological grade. Immune analysis revealed that neutrophils, regulatory T cell, CD8+ T cell, macrophages M1 and M2, cancer associated fibroblasts, myeloid dendritic cell, and activated mast cell had remarkably higher infiltrated levels within the tumour microenvironment of Mcluster B. Patients in Mcluster A were more likely to benefit from CTLA-4 blockers and were highly sensitive to 5-fluorouracil, cisplatin, gemcitabine, irinotecan, oxaliplatin, and epirubicin. Moreover, we built a microbe-derived model to assess the outcome. The ROC curves showed that the microbe-related model has good predictive performance. The expression of LAMA3 and LIPH was markedly increased within pancreatic tumour tissues and was linked to advanced stage and poor prognosis. Single-cell analysis indicated that besides cancer cells, the tumour microenvironment of PC was also rich in monocytes/macrophages, endothelial cells, and fibroblasts. LIPH and LAMA3 exhibited relatively higher expression in cancer cells and neutrophils. Conclusion: The intratumor microbiome-derived subtypes and signature in PC were first established, and our study provided novel perspectives on PC prognostic indicators and treatment options.

Role of single-cell ferroptosis regulation in intercellular communication and skin cutaneous melanoma progression and immunotherapy.

BACKGROUND: The involvement of ferroptosis in the pathogenesis and progression of various cancers has been well established. However, limited studies have investigated the role of ferroptosis-mediated tumor microenvironment (TME) in skin cutaneous melanoma (SKCM). METHODS: By leveraging single-cell RNA sequencing data, the nonnegative matrix factorization (NMF) approach was employed to comprehensively characterize and identify distinct gene signatures within ferroptosis-associated TME cell clusters. Prognostic and treatment response analyses were conducted using both bulk datasets and external cancer cohort to evaluate the clinical implications of TME clusters. RESULTS: This NMF-based analysis successfully delineated fibroblasts, macrophages, T cells, and B cells into multiple clusters, enabling the identification of unique gene expression patterns and the annotation of distinct TME clusters. Furthermore, pseudotime trajectories, enrichment analysis, cellular communication analysis, and gene regulatory network analysis collectively demonstrated significant intercellular communication between key TME cell clusters, thereby influencing tumor cell development through diverse mechanisms. Importantly, our bulk RNA-seq analysis revealed the prognostic significance of ferroptosis-mediated TME cell clusters in SKCM patients. Moreover, our analysis of immune checkpoint blockade highlighted the crucial role of TME cell clusters in tumor immunotherapy, facilitating the discovery of potential immunotherapeutic targets. CONCLUSIONS: In conclusion, this pioneering study employing NMF-based analysis unravels the intricate cellular communication mediated by ferroptosis within the TME and its profound implications for the pathogenesis and progression of SKCM. We provide compelling evidence for the prognostic value of ferroptosis-regulated TME cell clusters in SKCM, as well as their potential as targets for immunotherapy.

Machine learning-based signature of necrosis-associated lncRNAs for prognostic and immunotherapy response prediction in cutaneous melanoma and tumor immune landscape characterization.

Background: Cutaneous melanoma (CM) is one of the malignant tumors with a relative high lethality. Necroptosis is a novel programmed cell death that participates in anti-tumor immunity and tumor prognosis. Necroptosis has been found to play an important role in tumors like CM. However, the necroptosis-associated lncRNAs' potential prognostic value in CM has not been identified. Methods: The RNA sequencing data collected from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Project (GTEx) was utilized to identify differentially expressed genes in CM. By using the univariate Cox regression analysis and machine learning LASSO algorithm, a prognostic risk model had been built depending on 5 necroptosis-associated lncRNAs and was verified by internal validation. The performance of this prognostic model was assessed by the receiver operating characteristic curves. A nomogram was constructed and verified by calibration. Furthermore, we also performed sub-group K-M analysis to explore the 5 lncRNAs' expression in different clinical stages. Function enrichment had been analyzed by GSEA and ssGSEA. In addition, qRT-PCR was performed to verify the five lncRNAs' expression level in CM cell line (A2058 and A375) and normal keratinocyte cell line (HaCaT). Results: We constructed a prognostic model based on five necroptosis-associated lncRNAs (AC245041.1, LINC00665, AC018553.1, LINC01871, and AC107464.3) and divided patients into high-risk group and low-risk group depending on risk scores. A predictive nomogram had been built to be a prognostic indicator to clinical factors. Functional enrichment analysis showed that immune functions had more relationship and immune checkpoints were more activated in low-risk group than that in high-risk group. Thus, the low-risk group would have a more sensitive response to immunotherapy. Conclusion: This risk score signature could be used to divide CM patients into low- and high-risk groups, and facilitate treatment strategy decision making that immunotherapy is more suitable for those in low-risk group, providing a new sight for CM prognostic evaluation.

Identification and validation of neurotrophic factor-related genes signature in HNSCC to predict survival and immune landscapes.

Background: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer worldwide. Its highly aggressive and heterogeneous nature and complex tumor microenvironment result in variable prognosis and immunotherapeutic outcomes for patients with HNSCC. Neurotrophic factor-related genes (NFRGs) play an essential role in the development of malignancies but have rarely been studied in HNSCC. The aim of this study was to develop a reliable prognostic model based on NFRGs for assessing the prognosis and immunotherapy of HNSCC patients and to provide guidance for clinical diagnosis and treatment. Methods: Based on the TCGA-HNSC cohort in the Cancer Genome Atlas (TCGA) database, expression profiles of NFRGs were obtained from 502 HNSCC samples and 44 normal samples, and the expression and prognosis of 2601 NFRGs were analyzed. TGCA-HNSC samples were randomly divided into training and test sets (7:3). GEO database of 97 tumor samples was used as the external validation set. One-way Cox regression analysis and Lasso Cox regression analysis were used to screen for differentially expressed genes significantly associated with prognosis. Based on 18 NFRGs, lasso and multivariate Cox proportional risk regression were used to construct a prognostic risk scoring system. ssGSEA was applied to analyze the immune status of patients in high- and low-risk groups. Results: The 18 NFRGs were considered to be closely associated with HNSCC prognosis and were good predictors of HNSCC. The multifactorial analysis found that the NFRGs signature was an independent prognostic factor for HNSCC, and patients in the low-risk group had higher overall survival (OS) than those in the high-risk group. The nomogram prediction map constructed from clinical characteristics and risk scores had good prognostic power. Patients in the low-risk group had higher levels of immune infiltration and expression of immune checkpoints and were more likely to benefit from immunotherapy. Conclusion: The NFRGs risk score model can well predict the prognosis of HNSCC patients. A nomogram based on this model can help clinicians classify HNSCC patients prognostically and identify specific subgroups of patients who may have better outcomes with immunotherapy and chemotherapy, and carry out personalized treatment for HNSCC patients.

Insulin-like Growth Factor-2 (IGF-2) in Fibrosis.

The insulin family consists of insulin, insulin-like growth factor 1 (IGF-1), insulin-like growth factor 2 (IGF-2), their receptors (IR, IGF-1R and IGF-2R), and their binding proteins. All three ligands are involved in cell proliferation, apoptosis, protein synthesis and metabolism due to their homologous sequences and structural similarities. Insulin-like growth factor 2, a member of the insulin family, plays an important role in embryonic development, metabolic disorders, and tumorigenesis by combining with three receptors with different degrees of affinity. The main pathological feature of various fibrotic diseases is the excessive deposition of extracellular matrix (ECM) after tissue and organ damage, which eventually results in organic dysfunction because scar formation replaces tissue parenchyma. As a mitogenic factor, IGF-2 is overexpressed in many fibrotic diseases. It can promote the proliferation of fibroblasts significantly, as well as the production of ECM in a time- and dose-dependent manner. This review aims to describe the expression changes and fibrosis-promoting effects of IGF-2 in the skin, oral cavity, heart, lung, liver, and kidney fibrotic tissues.

Characterization of coagulation-related gene signature to predict prognosis and tumor immune microenvironment in skin cutaneous melanoma.

Backgroud: Skin cutaneous melanoma (SKCM) is an extremely metastatic form of skin cancer. However, there are few valuable molecular biomarkers, and accurate diagnosis is still a challenge. Hypercoagulable state encourages the infiltration and development of tumor cells and is significantly associated with poor prognosis in cancer patients. However, the use of a coagulation-related gene (CRG) signature for prognosis in SKCM, on the other hand, has yet to be determined. Method: We used data from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases to identify differentially expressed CRGs, then designed a prognostic model by using the LASSO algorithm, univariate and multivariate Cox regression analysis, and constructed a nomogram which was evaluated by calibration curves. Moreover, the Gene Expression Omnibus (GEO), GSE54467 was used as an independent validation. The correlation between risk score and clinicopathological characteristics, tumor microenvironment (TME), and immunotherapy was further analyzed. Results: To develop a prognostic model, seven CRGs in SKCM patients related to overall survival (OS) were selected: ANG, C1QA, CFB, DUSP6, KLKB1, MMP7, and RABIF. According to the Kaplan-Meier survival analysis, an increased OS was observed in the low-risk group than in the high-risk group (P<0.05). Immunotherapy was much more beneficial in the low-risk group, as per immune infiltration, functional enrichment, and immunotherapy analysis. Conclusions: The prognosis of SKCM patients may now be predicted with the use of a CRG prognostic model, thus guiding the development of treatment plans for SKCM patients and promoting OS rates.

A Novel Necroptosis-Related Gene Signature in Skin Cutaneous Melanoma Prognosis and Tumor Microenvironment.

Background: Necroptosis has been identified recently as a newly recognized programmed cell death that has an impact on tumor progression and prognosis, although the necroptosis-related gene (NRGs) potential prognostic value in skin cutaneous melanoma (SKCM) has not been identified. The aim of this study was to construct a prognostic model of SKCM through NRGs in order to help SKCM patients obtain precise clinical treatment strategies. Methods: RNA sequencing data collected from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed and prognostic NRGs in SKCM. Depending on 10 NRGs via the univariate Cox regression analysis usage and LASSO algorithm, the prognostic risk model had been built. It was further validated by the Gene Expression Omnibus (GEO) database. The prognostic model performance had been assessed using receiver operating characteristic (ROC) curves. We evaluated the predictive power of the prognostic model for tumor microenvironment (TME) and immunotherapy response. Results: We constructed a prognostic model based on 10 NRGs (FASLG, TLR3, ZBP1, TNFRSF1B, USP22, PLK1, GATA3, EGFR, TARDBP, and TNFRSF21) and classified patients into two high- and low-risk groups based on risk scores. The risk score was considered a predictive factor in the two risk groups regarding the Cox regression analysis. A predictive nomogram had been built for providing a more beneficial prognostic indicator for the clinic. Functional enrichment analysis showed significant enrichment of immune-related signaling pathways, a higher degree of immune cell infiltration in the low-risk group than in the high-risk group, a negative correlation between risk scores and most immune checkpoint inhibitors (ICIs), anticancer immunity steps, and a more sensitive response to immunotherapy in the low-risk group. Conclusions: This risk score signature could be applied to assess the prognosis and classify low- and high-risk SKCM patients and help make the immunotherapeutic strategy decision.