Identification of immune-related biomarkers associated with tumorigenesis and prognosis in cutaneous melanoma patients

Expression and functional analyses of TERF2 in esophageal carcinoma

Background

Esophageal cancer (ESCA) is a prevalent malignancy with a high incidence of morbidity and mortality, particularly in Asia. Telomeric Repeat-binding Factor 2 (TERF2) is a crucial component of the telomere-binding protein complex that maintains telomere stability. Aberrant TERF2 expression has been implicated in tumorigenesis, however, its specific role in ESCA remains largely unexplored.

Methods

The expression levels of TERF2 were assessed in esophageal squamous cell carcinoma (ESCC) samples using RT-PCR, IHC, and Western blotting (WB). Serum tumor marker concentrations were determined via electrochemiluminescence immunoassay (ECLIA) and chemiluminescent microparticle immunoassay (CMIA). Bioinformatics analyses were employed to elucidate TERF2's function in EC. The impact of TERF2 on ESCC cell proliferation was evaluated through cell counting kit-8 (CCK8) assays and flow cytometry.

Results

TERF2 protein and mRNA expression were elevated in ESCC tissues and correlated with age, sex, cancer stage, tumor grade, lymph node metastasis (LNM), and tumor histology. Univariate Cox regression analysis indicated TERF2 was an independent prognostic factor for overall survival (OS). TERF2 mRNA levels were associated with serum levels of carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), and tissue polypeptide antigen (TPA) in patients with ESCC. Immune infiltration and chemokine profiles were linked to TERF2 expression in ESCA. TERF2 is involved in regulating ESCC cell proliferation may through the DDR/P53 signaling way.

Conclusions

TERF2 is overexpressed in ESCA and contributes to ESCC cell proliferation may via DDR/TP53 signaling pathway. These results suggest that TERF2 may serve as a potential target for developing treatments and diagnostic biomarker for ESCA.

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.

Direct and Abscopal Antitumor Responses Elicited by AlPcNE-Mediated Photodynamic Therapy in a Murine Melanoma Model

Melanoma, the most aggressive form of skin cancer, presents a major clinical challenge due to its tendency to metastasize and recalcitrance to traditional therapies. Despite advances in surgery, chemotherapy, and radiotherapy, the outlook for advanced melanoma remains bleak, reinforcing the urgent need for more effective treatments. Photodynamic therapy (PDT) has emerged as a promising alternative, leading to targeted tumor destruction with minimal harm to surrounding tissues. In this study, the direct and abscopal antitumor effects of PDT in a bilateral murine melanoma model were evaluated. Although only one of the two tumors was treated, effects were observed in both. Our findings revealed significant changes in systemic inflammation and alterations in CD4+ and CD8+ T cell populations in treated groups, as evidenced by blood analyses and flow cytometry. High-throughput RNA sequencing (RNA-Seq) further unveiled shifts in gene expression profiles in both treated and untreated tumors. This research sheds light on the novel antitumor and abscopal effects of nanoemulsion of aluminum chloride phthalocyanine (AlPcNE)-mediated PDT in melanoma, highlighting the potential of different PDT protocols to modulate immune responses and to achieve more effective and targeted cancer treatments.

The causal relationship between immune cells mediating FIT3L, CCL4, OSM, and skin-derived deteriorated tumors

OBJECTIVE

Observational studies have identified a dual effect of circulating inflammatory proteins and immune cells on cancer progression. However, the specific mechanisms of action have not been clarified in the exacerbation of cutaneous-origin tumors. Therefore, this study aims to investigate whether the causal relationship between circulating inflammatory factors and basal cell carcinoma (BCC), cutaneous malignant melanoma (SKCM), and cutaneous squamous cell carcinoma (cSCC) is regulated by immune cells.

METHODS

This study employed the Two-Sample Mendelian Randomization (TSMR) approach to investigate the causal relationships between 91 circulating inflammatory factors and three prevalent types of skin cancer from a genetic perspective. Bayesian Weighted Mendelian Randomization (BWMR) was also used to validate correlation and reverse MR to assess inverse relationships. Subsequent sensitivity analyses were conducted to limit the impact of heterogeneity and pleiotropy. Finally, the two-step Mendelian Randomization (two-step MR) method was utilized to ascertain the mediating effects of specific immune cell traits in the causal pathways linking circulating inflammatory factors with BCC, SKCM, and cSCC.

RESULTS

The Inverse Variance Weighted (IVW) method and the Bayesian Weighted Algorithm collectively identified nine inflammatory factors causally associated with BCC, SKCM, and cSCC. The results from Cochran's Q test, mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and MR-Egger intercept were not statistically significant (p < 0.05). Additionally, the proportions mediated by CD4+ CD8dim T cell %leukocyte, CD4-CD8-Natural Killer T %T cell, and CD20 on IgD-CD38-B cell for FIt3L, CCL4, and OSM were 9.26%, 8.96%, and 10.16%, respectively.

CONCLUSION

Immune cell levels potentially play a role in the modulation process between circulating inflammatory proteins and cutaneous-origin exacerbated tumors. This finding offers a new perspective for the in-depth exploration of cutaneous malignancies.

Quantifying spatial CXCL9 distribution with image analysis predicts improved prognosis of triple-negative breast cancer

Background: The C-X-C motif chemokine ligand 9 (CXCL9) plays a pivotal role in tumor immunity by recruiting and activating immune cells. However, the relationship between CXCL9 expression and prognosis in triple-negative breast cancer (TNBC) is unclear. Methods: We investigated CXCL9 mRNA expression, clinicopathological features, and prognosis in TNBC patients. We also used computational image analysis to quantify and assess the distribution of CXCL9 protein in the tumor core (TC) and invasive margin (IM). Results: CXCL9 mRNA expression was significantly higher in TNBC tumors compared to normal tissue (p < 0.001) and was associated with smaller tumors (p = 0.022) and earlier stages (p = 0.033). High CXCL9 mRNA expression was correlated with improved overall survival (OS) in three independent cohorts (all p < 0.05). In a separate analysis, low CXCL9 protein expression was associated with increased lymph node metastasis (p = 0.018 and p = 0.036). High CXCL9 protein expression in the TC, IM, or both was associated with prolonged OS (all p < 0.001). Conclusion: High CXCL9 expression, at both the mRNA and protein levels, is associated with improved prognosis in TNBC patients. CXCL9 expression in the TC and/or IM may be an independent prognostic factor.

Chemokines and Cytokines in Immunotherapy of Melanoma and Other Tumors: From Biomarkers to Therapeutic Targets

Chemokines and cytokines represent an emerging field of immunotherapy research. They are responsible for the crosstalk and chemoattraction of immune cells and tumor cells. For instance, CXCL9/10/11 chemoattract effector CD8+ T cells to the tumor microenvironment, making an argument for their promising role as biomarkers for a favorable outcome. The cytokine Interleukin-15 (IL-15) can promote the chemokine expression of CXCR3 ligands but also XCL1, contributing to an important DC-T cell interaction. Recruited cytotoxic T cells can be clonally expanded by IL-2. Delivering or inducing these chemokines and cytokines can result in tumor shrinkage and might synergize with immune checkpoint inhibition. In addition, blocking specific chemokine and cytokine receptors such as CCR2, CCR4 or Il-6R can reduce the recruitment of tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs) or regulatory T cells (Tregs). Efforts to target these chemokines and cytokines have the potential to personalize cancer immunotherapy further and address patients that are not yet responsive because of immune cell exclusion. Targeting cytokines such as IL-6 and IL-15 is currently being evaluated in clinical trials in combination with immune checkpoint-blocking antibodies for the treatment of metastatic melanoma. The improved overall survival of melanoma patients might outweigh potential risks such as autoimmunity. However, off-target toxicity needs to be elucidated.

Decitabine suppresses MDSC-induced immunosuppression through dual functional mechanism and inhibits melanoma metastasis

Myeloid-derived suppressor cells (MDSCs) play a crucial role in promoting melanoma metastasis. Reprogramming MDSCs into mature M1 macrophages has emerged as a strategy to inhibit metastasis. Decitabine (Dec) is known to eradicate MDSCs and suppress tumor growth. In this study, we provide evidence that Dec not only reduces the MDSC population by inducing apoptosis, arresting cell cycle, and impairing recruitment, but also suppresses their immunosuppressive function by downregulating related genes and facilitating differentiation into M1 macrophages. Transcriptomic analysis of Dec-treated MDSCs revealed a marked downregulation of immunosuppressive genes including S100a9, S100a8, Vegf, Cxcr2, and Nos2. Meanwhile, M1 macrophage-associated genes involved in immune activation were upregulated, such as Ddx58, Isg15, Tap1, Ccl5, Cxcl9, and Cxcl10. Further bioinformatic analysis indicated that Dec promotes MDSC-to-M1 macrophage differentiation and activates innate immune pathways including NOD-like signaling to enhance anti-tumor immunity. Time-course studies implied that Dec upregulates myeloid transcription factor Irf7 to initiate MDSC differentiation and orchestrate the anti-tumor immune response. Collectively, our study unveils a novel dual-functional mechanism of Dec as both a cytotoxic agent reducing MDSCs and an inducer of their differentiation into M1 macrophages, thereby alleviating immunosuppression. This highlights Dec's potential for clinical melanoma metastasis suppression.

Insight into the role of IRF7 in skin and connective tissue diseases

Interferons (IFNs) are signalling proteins primarily involved in initiating innate immune responses against pathogens and promoting the maturation of immune cells. Interferon Regulatory Factor 7 (IRF7) plays a pivotal role in the IFNs signalling pathway. The activation process of IRF7 is incited by exogenous or abnormal nucleic acids, which is followed by the identification via pattern recognition receptors (PRRs) and the ensuing signalling cascades. Upon activation, IRF7 modulates the expression of both IFNs and inflammatory gene regulation. As a multifunctional transcription factor, IRF7 is mainly expressed in immune cells, yet its presence is also detected in keratinocytes, fibroblasts, and various dermal cell types. In these cells, IRF7 is critical for skin immunity, inflammation, and fibrosis. IRF7 dysregulation may lead to autoimmune and inflammatory skin conditions, including systemic scleroderma (SSc), systemic lupus erythematosus (SLE), Atopic dermatitis (AD) and Psoriasis. This comprehensive review aims to extensively elucidate the role of IRF7 and its signalling pathways in immune cells and keratinocytes, highlighting its significance in skin-related and connective tissue diseases.

Adipose stem cells control obesity-induced T cell infiltration into adipose tissue

T cell infiltration into white adipose tissue (WAT) drives obesity-induced adipose inflammation, but the mechanisms of obesity-induced T cell infiltration into WAT remain unclear. Our single-cell RNA sequencing reveals a significant impact of adipose stem cells (ASCs) on T cells. Transplanting ASCs from obese mice into WAT enhances T cell accumulation. C-C motif chemokine ligand 5 (CCL5) is upregulated in ASCs as early as 4 weeks of high-fat diet feeding, coinciding with the onset of T cell infiltration into WAT during obesity. ASCs and bone marrow transplantation experiments demonstrate that CCL5 from ASCs plays a crucial role in T cell accumulation during obesity. The production of CCL5 in ASCs is induced by tumor necrosis factor alpha via the nuclear factor κB pathway. Overall, our findings underscore the pivotal role of ASCs in regulating T cell accumulation in WAT during the early phases of obesity, emphasizing their importance in modulating adaptive immunity in obesity-induced adipose inflammation.

Identification of m6A-related lncRNAs-based signature for predicting the prognosis of patients with skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is one of the fastest developing malignancies with strong aggressive ability and no proper curative treatments. Numerous studies illustrated the importance of N6-methyladenosine (m6A) RNA modification to tumorigenesis. The aim of this study was to identify novel prognostic signature by using m6A-related lncRNAs, thus to improve the survival for SKCM patients and guide SKCM therapy.

METHODS

We downloaded the Presentational Matrix data from The Cancer Genome Atlas (TCGA) and analyzed all the expressed lncRNAs among 468 SKCM samples. Pearson correlation analysis was performed to assess the correlations between lncRNAs and 29 m6A-related genes. Least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression analysis were performed to construct m6A-related lncRNAs prognostic signature (m6A-LPS). The accuracy and prognostic value of this signature were validated by using receiver operating characteristic (ROC) curves, Kaplan-Meier (K-M) survival analysis, univariate COX or multivariate COX analyses. After calculating risk scores, patients were divided into low- and high-risk subgroups by the median value of risk scores.

RESULTS

A total of 2973 lncRNAs were found expressed among SKCM tissues. Prognostic analysis showed that 98 lncRNAs had a significant effect on the survival of SKCM patients. The m6A-LPS was validated using K-M and ROC analysis and the predictive accuracy of the risk score was also high according to the AUC of the ROC curve in training and testing sets. A nomogram based on tumor stage, gender and risk score that had a strong ability to forecast the 1-, 2-, 3-, 5-year OS of SKCM patients confirmed by calibrations. Enrichment analysis indicated that malignancy-associated biological processes and pathways were more common in the high-risk subgroup.

CONCLUSION

Collectively, m6A-related lncRNAs exert as potential biomarkers for prognostic stratification of SKCM patients and may assist clinicians achieving individualized treatment for SKCM.

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.

Fatty Acid Metabolism-Related lncRNAs are Potential Biomarkers for Predicting Prognoses and Immune Responses in Patients with Skin Cutaneous Melanoma

Introduction

Skin cutaneous melanoma is becoming more dangerous since it has a poor prognosis and is resistant to treatment. Previous research has shown that lncRNAs and fatty acid metabolism are essential for numerous biological activities. There are no studies on the relationship between fatty acid metabolism-Related lncRNAs and skin cutaneous melanoma.

Methods and Results

In order to better understand the prognosis and survival of SKCM patients, we investigated the significance of lncRNAs related to fatty acid metabolism. In this work, we looked at the fatty acid metabolism genes and lncRNAs expression patterns. On the basis of lncRNAs associated with fatty acid metabolism, a nomogram and a prognosis prediction model were created. Based on the profile of lncRNAs associated with fatty acid metabolism, functional and pharmacological sensitivity investigations were also carried out. We also looked at the connection between immunotherapy and the immune response. The findings demonstrated that a risk score model based on 11 essential lncRNAs for fatty acid metabolism may discriminate between the clinical condition of SKCM and more accurately predict prognosis and survival. We conducted quantitative reverse transcription polymerase-chain reaction (RT-PCR) to verify the model.

Conclusion

These important lncRNAs further showed a strong association with the tumor immune system, and these important lncRNAs also showed a connection between SKCM and chemotherapeutic treatment sensitivity. Our research strives to provide fresh viewpoints and innovative approaches to the treatment and administration of SKCM.

Tumor-Infiltrating iNKT Cells Activated through c-Kit/Sca-1 Are Induced by Pentoxifylline, Norcantharidin, and Their Mixtures for Killing Murine Melanoma Cells

The involvement of NK and other cytotoxic cells is considered the first defense line against cancer. However, a significant lack of information prevails on the possible roles played by factors considered characteristic of primitive cells, such as c-kit and Sca-1, in activating these cells, particularly in melanoma models subjected to treatments with substances under investigation, such as the case of norcantharidin. In this study, B16F1 murine melanoma cells were used to induce tumors in DBA/2 mice, estimating the proportions of NK and iNKT cells; the presence of activation (CD107a+) and primitive/activation (c-kit+/Lya6A+) markers and some tumor parameters, such as the presence of mitotic bodies, nuclear factor area, NK and iNKT cell infiltration in the tumor, infiltrated tumor area, and infiltrating lymphocyte count at 10x and 40x in specimens treated with pentoxifylline, norcantharidin, and the combination of both drugs. Possible correlations were estimated with Pearson's correlation analysis. It should be noted that, despite having demonstrated multiple correlations, immaturity/activation markers were related to these cells' activation. At the tumor site, iNKT cells are the ones that exert the cytotoxic potential on tumor cells, but they are confined to specific sites in the tumor. Due to the higher number of interactions of natural killer cells with tumor cells, it is concluded that the most effective treatment was PTX at 60 mg/kg + NCTD at 0.75 mg/kg.

The Role of NCS1 in Immunotherapy and Prognosis of Human Cancer

The Neural Calcium Sensor1 (NCS1) is a crucial protein that binds to Ca2+ and is believed to play a role in regulating tumor invasion and cell proliferation. However, the role of NCS1 in immune infiltration and cancer prognosis is still unknown. Our study aimed to explore the expression profile, immune infiltration pattern, prognostic value, biological function, and potential compounds targeting NCS1 using public databases. High expression of NCS1 was detected by immune histochemical staining in LIHC (Liver hepatocellular carcinoma), BRCA (Breast invasive carcinoma), KIRC (Kidney renal clear cell carcinoma), and SKCM (Skin Cutaneous Melanoma). The expression of NCS1 in cancer was determined by TCGA (The Cancer Genome Atlas Program), GTEx (The Genotype-Tissue Expression), the Kaplan-Meier plotter, GEO (Gene Expression Omnibus), GEPIA2.0 (Gene Expression Profiling Interactive Analysis 2.0), HPA (The Human Protein Atlas), UALCAN, TIMER2.0, TISIDB, Metascape, Drugbank, chEMBL, and ICSDB databases. NCS1 has genomic mutations as well as aberrant DNA methylation in multiple cancers compared to normal tissues. Also, NCS1 was significantly different in the immune microenvironment, tumor mutational burden (TMB), microsatellite instability (MSI), and immune infiltrate-associated cells in different cancers, which could be used for the typing of immune and molecular subtypes of cancer and the presence of immune checkpoint resistance in several cancers. Univariate regression analysis, multivariate regression analysis, and gene enrichment analysis to construct prognostic models revealed that NCS1 is involved in immune regulation and can be used as a prognostic biomarker for SKCM, LIHC, BRCA, COAD, and KIRC. These results provide clues from a bioinformatic perspective and highlight the importance of NCS1 in a variety of cancers.

Analysis of differential gene immune infiltration and clinical characteristics of skin cutaneous melanoma based on systems biology and drug repositioning methods to identify drug candidates for skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) has a low early detection rate and a high mortality rate. There are many problems such as side effects and drug resistance in existing therapeutic drugs. Current studies have confirmed that SKCM pathogenesis-related genes promote the invasion and metastasis of cutaneous melanoma, but their roles in the tumor microenvironment (TME) remain unclear. Network pharmacology provides new opportunities for drug repurposing and repositioning, and is a fast, safe, and inexpensive drug discovery method to find new drugs for the treatment of SKCM. In this study, based on 3 databases (KEGG, OMIM, and Genotype) to obtain SKCM-related genes, and TCGA SKCM dataset, SKCM differential genes in GSE3189 and GSE46517 were intersected to identify SKCM pathogenesis-related differential genes, and the differential genes were immune infiltration and analysis, For survival analysis, a prognostic nomogram risk model was constructed based on the results of multivariate Cox regression analysis for risk stratification and prognosis prediction, then focused on the differential expression of ZC3H12A and its effect on TME. Finally, the protein interaction network method was used to quantify the similarity between 684 drug targets and skin melanoma, and to screen out drugs similar to skin melanoma. Based on 3 databases of KEGG, OMIM, and Genotype, 294 SKCM-related genes and 18 SKCM pathogenesis-related differential genes were obtained, and 18 SKCM pathogenesis-related differential genes were significantly correlated with TME. The constructed prognostic nomogram risk model predicted performance better and provided valuable information for immunotherapy. Multivariate Cox regression analysis and K-M analysis showed that ZC3H12A was a differentially expressed gene affecting the prognosis of SKCM and promoted the infiltration of anti-tumor immune cells CD8 + T cells, B cells, and DC cells. Based on the analysis of the protein interaction network method, 43 drugs were found to have high potential in the treatment of SKCM, and the literature search of these 43 drugs was carried out, and 21 drugs were found to have experimental verification for the treatment of SKCM. Taken together, the differential genes associated with the pathogenesis of SKCM have important roles in the tumor immune microenvironment, clinicopathological features, and prognosis, especially ZC3H12A has a potential role in identifying early SKCM patients. At the same time, it provides a new strategy for the drug development of SKCM and provides a basis for the reuse of SKCM drugs.

Deciphering Treg cell roles in esophageal squamous cell carcinoma: a comprehensive prognostic and immunotherapeutic analysis

Background: Esophageal squamous cell carcinoma (ESCC) is a prevalent and aggressive form of cancer that poses significant challenges in terms of prognosis and treatment. Regulatory T cells (Treg cells) have gained attention due to their influential role in immune modulation within the tumor microenvironment (TME). Understanding the intricate interactions between Treg cells and the tumor microenvironment is essential for unraveling the mechanisms underlying ESCC progression and for developing effective prognostic models and immunotherapeutic strategies. Methods: A combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq analysis was utilized to explore the role of Treg cells within the TME of ESCC. The accuracy and applicability of the prognostic model were assessed through multi-dimensional evaluations, encompassing an examination of the model's performance across various dimensions, such as the mutation landscape, clinical relevance, enrichment analysis, and potential implications for immunotherapy strategies. Results: The pivotal role of the macrophage migration inhibitory factor (MIF) signaling pathway within the ESCC TME was investigated, with a focus on its impact on Treg cells and other subpopulations. Through comprehensive integration of bulk sequencing data, a Treg-associated signature (TAS) was constructed, revealing that ESCC patients with elevated TAS (referred to as high-TAS individuals) experienced significantly improved prognoses. Heightened immune infiltration and increased expression of immune checkpoint markers were observed in high-TAS specimens. The model's validity was established through the IMvigor210 dataset, demonstrating its robustness in predicting prognosis and responsiveness to immunotherapy. Heightened therapeutic benefits were observed in immune-based interventions for high-TAS ESCC patients. Noteworthy differences in pathway enrichment patterns emerged between high and low-TAS cohorts, highlighting potential avenues for therapeutic exploration. Furthermore, the clinical relevance of key model genes was substantiated by analyzing clinical samples from ten paired tumor and adjacent tissues, revealing differential expression levels. Conclusion: The study established a TAS that enables accurate prediction of patient prognosis and responsiveness to immunotherapy. This achievement holds significant implications for the clinical management of ESCC, offering valuable insights for informed therapeutic interventions.

Revealing the role of regulatory T cells in the tumor microenvironment of lung adenocarcinoma: a novel prognostic and immunotherapeutic signature

Background

Regulatory T cells (Tregs), are a key class of cell types in the immune system. In the tumor microenvironment (TME), the presence of Tregs has important implications for immune response and tumor development. Relatively little is known about the role of Tregs in lung adenocarcinoma (LUAD).

Methods

Tregs were identified using but single-cell RNA sequencing (scRNA-seq) analysis and interactions between Tregs and other cells in the TME were investigated. Next, we used multiple bulk RNA-seq datasets to construct risk models based on marker genes of Tregs and explored differences in prognosis, mutational landscape, immune cell infiltration and immunotherapy between high- and low-risk groups, and finally, qRT-PCR and cell function experiments were performed to validate the model genes.

Results

The cellchat analysis showed that MIF-(CD74+CXCR4) pairs play a key role in the interaction of Tregs with other cell subpopulations, and the Tregs-associated signatures (TRAS) could well classify multiple LUAD cohorts into high- and low-risk groups. Immunotherapy may offer greater potential benefits to the low-risk group, as indicated by their superior survival, increased infiltration of immune cells, and heightened expression of immune checkpoints. Finally, the experiment verified that the model genes LTB and PTTG1 were relatively highly expressed in cancer tissues, while PTPRC was relatively highly expressed in paracancerous tissues. Colony Formation assay confirmed that knockdown of PTTG1 reduced the proliferation ability of LUAD cells.

Conclusion

TRAS were constructed using scRNA-seq and bulk RNA-seq to distinguish patient risk subgroups, which may provide assistance in the clinical management of LUAD patients.

Exploring association of melanoma-specific Bcl-xL with tumor immune microenvironment

BACKGROUND

Macrophages take center stage in the tumor microenvironment, a niche composed of extracellular matrix and a heterogeneous group of cells, including immune ones. They can evolve during tumor progression and acquire Tumor-Associated Macrophage (TAMs) phenotype. The release of cytokines by tumor and stromal cells, influence the secretion of cytokines by TAMs, which can guarantee tumor progression and influence the response to therapy. Among all factors able to recruit and polarize macrophages, we focused our attention on Bcl-xL, a multifaceted member of the Bcl-2 family, whose expression is deregulated in melanoma. It acts not only as a canonical pro-survival and anti-apoptotic protein, but also as a promoter of tumor progression.

METHODS

Human melanoma cells silencing or overexpressing Bcl-xL protein, THP-1 monocytic cells and monocyte-derived macrophages were used in this study. Protein array and specific neutralizing antibodies were used to analyze cytokines and chemokines secreted by melanoma cells. qRT-PCR, ELISA and Western Blot analyses were used to evaluate macrophage polarization markers and protein expression levels. Transwell chambers were used to evaluate migration of THP-1 and monocyte-derived macrophages. Mouse and zebrafish models were used to evaluate the ability of melanoma cells to recruit and polarize macrophages in vivo.

RESULTS

We demonstrated that melanoma cells overexpressing Bcl-xL recruit macrophages at the tumor site and induce a M2 phenotype. In addition, we identified that interleukin-8 and interleukin-1β cytokines are involved in macrophage polarization, and the chemokine CCL5/RANTES in the macrophages recruitment at the tumor site. We also found that all these Bcl-xL-induced factors are regulated in a NF-kB dependent manner in human and zebrafish melanoma models.

CONCLUSIONS

Our findings confirmed the pro-tumoral function of Bcl-xL in melanoma through its effects on macrophage phenotype.

Immunologically active ferumoxytol-poly(I : C) nanomaterials inhibit metastatic melanoma by regulating myeloid-derived suppressor cell differentiation

Nanomaterials have been identified as a potential therapeutic option for targeting myeloid-derived suppressor cells (MDSCs), which are known to play a crucial role in tumor metastasis and treatment resistance. Here, we report a unique immunologically active nanomaterial composed of ferumoxytol and poly(I : C) (FP-NPs) and investigate its immunoregulatory activities on MDSCs in metastatic melanoma. In vivo assays demonstrated that FP-NPs had the ability to significantly impede the progression of metastatic melanoma and decrease the MDSC population in the lungs, spleen, and bone marrow of mice. Both in vivo and in vitro experiments revealed that FP-NPs reduced the number of granulocytic MDSCs and promoted the differentiation of monocytic MDSCs into anti-tumor M1 macrophages. Transcriptome sequencing indicated that FP-NPs significantly altered the expression of several genes involved in immunity. Analysis of Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and quantitative real-time PCR revealed that FP-NPs significantly increased the expression of the myeloid cell differentiation-related gene interferon regulatory factor 7 and activated interferon beta-related signaling pathways, which stimulated the differentiation of MDSCs into M1 macrophages. These findings suggest that FP-NPs, a unique nanomaterial with immunological properties, can induce MDSCs to differentiate into M1 macrophages, potentially offering new treatment prospects for metastatic melanoma in the future.

Diagnostic accuracy of using multiple cytokines to predict aldosterone-producing adenoma

Here, we aimed to study the important cytokines in plasma to identify the aldosterone-producing adenoma (APA). 19 unilateral primary aldosteronism (UPA) patients and 19 healthy people were divided into UPA group and Control group, and the serum of bilateral adrenal veins and inferior vena cava collected by adrenal blood sampling (AVS) in UPA patients and the serum from the healthy subjects were all used to detect multiple cytokines by Luminex immunoassays. Additionally, The UPA patients subjected to laparoscopic adrenalectomy were divided into different groups by pathological results for further study. According our results, IP-10, CXCL9 and RANTES were significantly higher in UPA group compared with control group, and the combination of the three cytokines have significant predictive power for predicting UPA, while the correlational analyses demonstrated that IP-10 and CXCL9 were positively correlated with BP and HR, while EGF was positively correlated with HDL. Additionally, IL-1b was suggested to be the most potential diagnostic biomarker to discriminate the APA and unilateral adrenal hyperplasia (UAH). The present findings might suggest a possibility of IP-10, CXCL9 and RANTES served as a sign to help UPA diagnosis and finally used to assist the diagnosis of APA, while IL-1b was suggested to be the most potential diagnostic biomarker to identify the APA from the UAH patients.

GENECI: A novel evolutionary machine learning consensus-based approach for the inference of gene regulatory networks

Gene regulatory networks define the interactions between DNA products and other substances in cells. Increasing knowledge of these networks improves the level of detail with which the processes that trigger different diseases are described and fosters the development of new therapeutic targets. These networks are usually represented by graphs, and the primary sources for their correct construction are usually time series from differential expression data. The inference of networks from this data type has been approached differently in the literature. Mostly, computational learning techniques have been implemented, which have finally shown some specialization in specific datasets. For this reason, the need arises to create new and more robust strategies for reaching a consensus based on previous results to gain a particular capacity for generalization. This paper presents GENECI (GEne NEtwork Consensus Inference), an evolutionary machine learning approach that acts as an organizer for constructing ensembles to process the results of the main inference techniques reported in the literature and to optimize the consensus network derived from them, according to their confidence levels and topological characteristics. After its design, the proposal was confronted with datasets collected from academic benchmarks (DREAM challenges and IRMA network) to quantify its accuracy. Subsequently, it was applied to a real-world biological network of melanoma patients whose results could be contrasted with medical research collected in the literature. Finally, it has been proved that its ability to optimize the consensus of several networks leads to outstanding robustness and accuracy, gaining a certain generalization capacity after facing the inference of multiple datasets. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a python package available at PyPI: https://pypi.org/project/geneci/.

siRNAs to Knock-down Antiviral Chemokine-related Genes in FRhK-4 Cells

The objective of this study was to generate small interfering RNA (siRNA) to knockdown antiviral chemokine-related genes in fetal rhesus monkey kidney (FRhK-4) cells. We generated siRNA duplexes to suppress antiviral chemokines like CXCL10 and CCL4 in FRhK-4 cells by downregulating interferon regulatory factor (IRF) 3 and IRF7. Three siRNA duplexes (si-F-IRF3-1, si-F-IRF3-2, and si-F-IRF3-3) targeting IRF3, and one siRNA duplex (si-F-IRF7) targeting IRF7 were generated. A nontarget siRNA duplex was used as the negative control. The nontarget or target siRNA duplexes (si-F-IRF3-1, si-F-IRF3-2, si-F-IRF3-3, and si-F-IRF7) were transfected into FRhK-4 cells using transfection reagents, and they were then incubated at 37°C for 6 h with 5% CO₂. After 6 h, the medium was removed, and fresh medium was added to each cell, and they were then incubated at 37°C for 48 h with 5% CO₂. The transfected FRhK-4 cells were infected with hepatitis A virus (HAV) HM-175/18f (viral titer: 10⁵ PFU/mL) and incubated at 37°C for 3 h with 5% CO₂ for HAV propagation. The expression levels of chemokines, including CXCL10 and CCL4, under the regulation of IRF3 and IRF7 in the transfected FRhK-4 cells were measured using quantitative real-time polymerase chain reaction after 3 h of HAV infection. The results indicated that CXCL10 and CCL4 expression levels were decreased in FRhK-4 cells transfected with si-F-IRF3-1, si-F-IRF3-3, or si-F-IRF7 (p < 0.05) compared to those in the negative control. These results indicate that si-F-IRF3-1 and si-F-IRF3-3, and si-F-IRF7 successfully knocked down IRF3 and IRF7 in FRhK-4 cells, respectively and suppressed antiviral chemokines. These siRNAs could be used to suppress antiviral chemokines in FRhK-4 cells.

TREM2 as a Potential Immune-Related Biomarker of Prognosis in Patients with Skin Cutaneous Melanoma Microenvironment

Background

The skin cutaneous melanoma (SKCM) is a devastating form of skin cancer triggered by genetic and environmental factors, and the incidence of SKCM has rapidly increased in recent years. Immune infiltration of the tumor microenvironment is positively associated with overall survival in many tumors. Triggering receptor expressed on myeloid cells 2 (TREM2) is a transmembrane receptor of the immunoglobulin superfamily and a crucial signaling hub for multiple pathological pathways that mediate immunity. Although numerous evidences suggest a crucial role for TREM2 in tumorigenesis of some tumors, no systematic SKCM analysis of TREM2 is available. Mehods. The relationship between TREM2 expression and diagnostic and prognostic value of SKCM patients via using The Cancer Genome Atlas (TCGA) data. The expression level of TREM2 and clinical characteristic correlation in SKCM patients were assessed by the Wilcoxon rank sum test. The cox regression methods, Kaplan-Meier (KM), and log-rank test were used to assess the impact of TREM2 expression on the overall survival (OS). Furthermore, the Gene Set Enrichment Analysis (GSEA) and TIMER were performed to evaluate the enrichment pathways and potential functions and quantify the immune cell infiltration level for TREM2 expression.

Results

The TREM2 in SKCM sample expression levels was significantly higher than in normal tissues. Moreover, this expression level of TREM2 was also associated with the BMI of SKCM patients. KM overall survival analysis and OS curve displayed that a high-level TREM2 expression was significantly correlated with a better SKCM prognosis of patients as compared with a low level of TREM2 expression. The GSEA analysis also revealed that TREM2 was associated with immune functions, such as neutrophil activation.

Conclusion

TREM2 played a crucial role in SKCM, which might be a prognostic biomarker and correlated with immune infifiltrates in SKCM patients.

The expression pattern of pyroptosis-related genes predicts the prognosis and drug response of melanoma

Cutaneous melanoma (CM, hereafter referred to as melanoma) is a highly malignant tumor that typically undergoes early metastasis. Pyroptosis, as a special programmed cell death process that releases inflammatory factors and has been widely studied in tumors, but its role in melanoma has not been fully elucidated. In this study, we examined the relationship between pyroptosis and the prognosis of melanoma through bioinformatic analysis of RNA-sequencing data. Our results demonstrated that pyroptosis is a protective factor associated with melanoma prognosis. A higher pyroptosis score was associated with a more favorable overall survival. We used weighted gene co-expression networks analysis (WGCNA) to establish an effective prognosis model based on 12 pyroptosis-related genes. We then validated it in two independent cohorts. Furthermore, a nomogram combining clinicopathological characteristics and a pyroptosis-related gene signature (PGS) score was designed to effectively evaluate the prognosis of melanoma. Additionally, we analyzed the potential roles of pyroptosis in the tumor immune microenvironment and drug response. Interestingly, we found that the elevated infiltration of multiple immune cells, such as CD4⁺ T cells, CD8⁺ T cells, dendritic cells, and M1 macrophages, may be associated with the occurrence of pyroptosis. Pyroptosis was also related to a better response of melanoma to interferon-α, paclitaxel, cisplatin and imatinib. Through Spearman correlation analysis of the 12 pyroptosis-related genes and 135 chemotherapeutic agents in the Genomics of Drug Sensitivity in Cancer database, we identified solute carrier family 31 member 2 (SLC31A2) and collagen type 4 alpha 5 chain (COL4A5) as being associated with resistance to most of these drugs. In conclusion, this PGS is an effective and novelty prognostic indicator in melanoma, and also has an association with the melanoma immune microenvironment and melanoma treatment decision-making.

Exploring the Correlation of Abnormal CXCL9 Expression with Immune Infiltration in Glioma and Patient Prognosis Based on TCGA and GTEx Databases

In this study, we intend to identify key immune-related genes (IRGs) in gliomas using the TCGA and GTEx databases. Following collection of the RNA-seq data of lower-grade glioma (LGG) and glioblastoma (GBM) patients from the TCGA and GTEx databases, the differentially expressed IRGs (DE-IRGs) were screened. The ESTIMATE algorithm was utilized to evaluate StromalScore and ImmuneScore of LGG and GBM samples and a multifactorial Cox risk model was constructed to identify the related risk genes. The core IRGs of LGG and GBM were screened through a PPI network, followed by exploration of their correlation with glioma prognosis. The relationship between IRGs and immune cells in LGG and GBM was detected. In vitro assays were performed to detect the effect of CXCL9 on glioma cell development. We screened 403 and 492 DE-IRGs in LGG and GBM. StromalScore and ImmuneScore were related to overall survival in LGG, but not in GBM. CXCL9 was identified as a core gene in LGG and GBM and shared association with the prognosis of gliomas. Furthermore, a correlation was found between CXCL9 and immune infiltration of LGG and GBM. Glioma cell proliferation and invasion could be inhibited by silencing of CXCL9. Overall, CXCL9 is correlated to the prognosis of glioma patients and may accelerate glioma development via immune regulation.

Data-driven analysis to identify prognostic immune-related biomarkers in BRAF mutated cutaneous melanoma microenvironment

Skin cutaneous melanoma is one of the deadly diseases, and more than 50% of the patients have BRAF gene mutations. Evidence suggests that oncogenic BRAF modulates the immune system’s ability to recognize SKCM cells. Due to the complexity of the tumor microenvironment (TME) and a lack of a rational mechanistic basis, it is urgent to investigate the immune infiltration and identify prognostic biomarkers in BRAF mutated SKCM patients. Multiple methods including ESTIMATE algorithm, differential gene analysis, prognostic analysis and immune infiltration analysis were performed to investigate the tumor microenvironment. Based on the patient’s immune score and stromal score, immune-related genes DEGs were identified. Functional analysis revealed that these genes were mainly enriched in biological processes such as immune response, defense response and positive regulation of immune system. Furthermore, we analyzed the immune infiltrating cell components of BRAF mutated patients and revealed 4 hub genes associated with overall survival time. Several cells (Monocyte, Macrophage and Gamma delta cells) have been found to be significantly decreased in immune-high BRAF mutated SKCM group. While CD4+T, CD8+T, CD4 naïve, Tr1, Th2 and many T cell subsets were significantly increased in immune-high group. These immune cells and genes were closely related to each other. This study revealed that the dysregulation of immune function and immune cells may contribute to the poor outcomes of BRAF mutated patients. It is of great significance to our further understanding of the TME and immune dysfunction in BRAF mutated SKCM.

A CD8+ T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma

Background

Skin cutaneous melanoma (SKCM) is the most frequently encountered tumor of the skin. Immunotherapy has opened a new horizon in melanoma treatment. We aimed to construct a CD8⁺ T cell-associated immune gene prognostic model (CDIGPM) for SKCM and unravel the immunologic features and the benefits of immunotherapy in CDIGPM-defined SKCM groups.

Method

Single-cell SKCM transcriptomes were utilized in conjunction with immune genes for the screening of CD8⁺ T cell-associated immune genes (CDIGs) for succeeding assessment. Thereafter, through protein-protein interaction (PPI) networks analysis, univariate COX analysis, and multivariate Cox analysis, six genes (MX1, RSAD2, IRF2, GBP2, IFITM1, and OAS2) were identified to construct a CDIGPM. We detected cell proliferation of SKCM cells transfected with IRF2 siRNA. Then, we analyzed the immunologic features and the benefits of immunotherapy in CDIGPM-defined groups.

Results

The overall survival (OS) was much better in low-CDIGPM group versus high CDIGPM group in TCGA dataset and GSE65904 dataset. On the whole, the results unfolded that a low CDIGPM showed relevance to immune response-correlated pathways, high expressions of CTLA4 and PD-L1, a high infiltration rate of CD8⁺ T cells, and more benefits from immunotherapy.

Conclusion

CDIGPM is an good model to predict the prognosis, the potential immune escape from immunotherapy for SKCM, and define immunologic and molecular features.

The role of cuproptosis-related gene in the classification and prognosis of melanoma

Background

Melanoma, as one of the most aggressive and malignant cancers, ranks first in the lethality rate of skin cancers. Cuproptosis has been shown to paly a role in tumorigenesis, However, the role of cuproptosis in melanoma metastasis are not clear. Studying the correlation beteen the molecular subtypes of cuproptosis-related genes (CRGs) and metastasis of melanoma may provide some guidance for the prognosis of melanoma.

Methods

We collected 1085 melanoma samples in The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus(GEO) databases, constructed CRGs molecular subtypes and gene subtypes according to clinical characteristics, and investigated the role of CRGs in melanoma metastasis. We randomly divide the samples into train set and validation set according to the ratio of 1:1. A prognostic model was constructed using data from the train set and then validated on the validation set. We performed tumor microenvironment analysis and drug sensitivity analyses for high and low risk groups based on the outcome of the prognostic model risk score. Finally, we established a metastatic model of melanoma.

Results

According to the expression levels of 12 cuproptosis-related genes, we obtained three subtypes of A₁, B₁, and C₁. Among them, C₁ subtype had the best survival outcome. Based on the differentially expressed genes shared by A₁, B₁, and C₁ genotypes, we obtained the results of three gene subtypes of A₂, B₂, and C₂. Among them, the B₂ group had the best survival outcome. Then, we constructed a prognostic model consisting of 6 key variable genes, which could more accurately predict the 1-, 3-, and 5-year overall survival rates of melanoma patients. Besides, 98 drugs were screened out. Finally, we explored the role of cuproptosis-related genes in melanoma metastasis and established a metastasis model using seven key genes.

Conclusions

In conclusion, CRGs play a role in the metastasis and prognosis of melanoma, and also provide new insights into the underlying pathogenesis of melanoma.

The Comprehensive Analysis of Hub Gene ARRB2 in Prostate Cancer

Methods

The differential expressed genes (DEGs) were screened from the gene expression profile GSE30994 related to PRAD and then analyzed by protein-protein interaction (PPI) to screen the hub gene. Subsequently, the relation between hub gene and pan cancers, PRAD prognosis, and immunotherapy was analyzed. Besides, the effects of hub gene on the growth and metastasis of PRAD cell lines and inflammatory factors (IFs) were detected by functional experiments.

Results

276 upregulated and 1,861 downregulated DEGs were analyzed from GSE30994 gene expression profiles. Through enrichment analysis, it was found that upregulated DEGs were significantly enriched in nitric oxide-mediated signal transduction, insulin signaling pathway, etc. Through PPI networks, ARRB2 was determined as the hub gene that was highly expressed in pan cancers, including PRAD, and contributed to poor prognosis of PRAD patients. Immunoassay showed that ARRB2 was associated with B cells, NK cells, endothelial cells, etc. and also connected with tumor-infiltrating lymphocytes (TILs). Next, the signature model analysis revealed that ARRB2 had a clinical value in predicting PRAD prognosis. In functional experiments, ARRB2 was highly expressed in PRAD cell lines, promoted PRAD cell growth and metastasis, and positively associated with IFs.

Conclusion

ARRB2 has a good prognostic ability in PRAD, and it could be a potential target of PRAD immunotherapy, which offers new directions for PRAD research.

Application of Deep Learning on the Prognosis of Cutaneous Melanoma Based on Full Scan Pathology Images

Introduction

The purpose of this study is to use deep learning and machine learning to learn and classify patients with cutaneous melanoma with different prognoses and to explore the application value of deep learning in the prognosis of cutaneous melanoma patients.

Methods

In deep learning, VGG-19 is selected as the network architecture and learning model for learning and classification. In machine learning, deep features are extracted through the VGG-19 network architecture, and the support vector machine (SVM) model is selected for learning and classification. Compare and explore the application value of deep learning and machine learning in predicting the prognosis of patients with cutaneous melanoma.

Result

According to receiver operating characteristic (ROC) curves and area under the curve (AUC), the average accuracy of deep learning is higher than that of machine learning, and even the lowest accuracy is better than that of machine learning.

Conclusion

As the number of learning increases, the accuracy of machine learning and deep learning will increase, but in the same number of cutaneous melanoma patient pathology maps, the accuracy of deep learning will be higher. This study provides new ideas and theories for computational pathology in predicting the prognosis of patients with cutaneous melanoma.

A Ferroptosis-Related Gene Signature for Predicting Survival and Immunotherapy Effect in Renal Cancer

Background

Most recently, no efficient prognostic indictor is present for kidney cancer. Thus, we aimed to build and validate a new prognostic gene signature for renal cancer patients using the Cancer Genomic Atlas (TCGA).

Methods

A “time-dependent receiver operating characteristic (tROC)” curve was generated, and a log-rank test was performed to assess the performance of the biomarker in training and validation. A “ferroptosis-related gene signature” was developed. In different training and validations sets, tROC and log-rank test were used to validate the biomarker's performance.

Results

In the training set with a P value less than 0.01 and the validation set, the “gene signature” was significantly correlated with survival. Eventually, it was found that the ferroptosis-related gene signature was directly correlated with immune score and the score of tumor mutation, suggesting its role in predicting response to immunotherapy.

Conclusion

We developed and validated a “ferroptosis-related gene signature” that can be sued for patients with kidney cancer. It can also assist in facilitating the plan for treatment and risk stratification.

Expression of chemokine CXCL8/9/10/11/13 and its prognostic significance in head and neck cancer

BACKGROUND

Head and neck cancer (HNC) is a very popular cancer, with many primary sites and pathological types, at the top of the list of tumors. Chemokines are a class of small molecular basic proteins, whose N-terminal cysteine residues can be divided into four subunits by location and number, which significantly enhances the expression level in all kinds of cancers. However, in HNC, especially in head and neck squamous cell carcinoma, the chemokine CXCL8/9/10/11/13 has not been clearly explored for its diagnosis and prognosis.

METHODS

The ONCOMINE database was used to analyze the expression of chemokine family in various cancers. After CXCL8/9/10/11/13 was screened out, the expression of CXCL 8/9/11/13 in patients with HNC/normal people were analyzed by UALCAN database. The expression and pathological stages of CXCL 8/9/10/13 in HNC tissues were analyzed by the GEPIA database, and the relationship between its mRNA expression and the overall survival (OS) time of patients with HNC was analyzed by Kaplan-Meier plotter database. In addition, 171 co-expressed genes significantly related to CXCL8/9/10/11/13 mutation were screened by online tool cBioPortal, and the protein interaction network of these genes was constructed by STRING database. Finally, the potential functions of CXCL8/9/10/11/13 and its 171 co-expressed genes were explored by the enrichment and analysis function of David database.

RESULTS

Transcriptional expression of chemokine 8/9/10/11/13 was significantly increased in patients with HNC. Clinical stage of patients with HNC was significantly correlated with overexpression of CXCL9/10/11. In addition, the chemokine CXCL8/9/10/13 was significantly correlated with over-survival of patients with HNC, so it could be distinguished between short-term and long-term survival of patients with HNC. In conclusion, CXCL8/9/10/11/13 closely connected with the expression and prognosis of HNC.

CONCLUSION

In this study, our results suggest that chemokine CXCL8/9/10/11/13 may play a critical role in the development of HNC, and, according to relevant data, it may affect the survival and prognosis of patients with HNC.

Prognostic value of metabolic genes in lung adenocarcinoma via integrative analyses

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common malignant lung tumor. Metabolic pathway reprogramming is an important hallmark of physiologic changes in cancers. However, the mechanisms through which these metabolic genes and pathways function in LUAD as well as their prognostic values have not been fully established.

METHODS

Four publicly available datasets from GEO and TCGA were used to identify differentially expressed genes (DEGs) in LUAD, which were then subjected to GO and KEGG pathway enrichment analysis. Associations between metabolic gene expressions with overall survival, tumor stage, TP53 mutation status, and infiltrated immune cells were investigated. Protein-protein interactions were evaluated using GeneMANIA and Metascape.

RESULTS

By integrating four public datasets, 247 DEGs were identified in LUAD. These DEGs were significantly enriched in regulation of chromosome segregation, centromeric region, and histone kinase activity GO terms, as well as in cell cycle, p53 signaling pathway, metabolic pathways, and other KEGG pathways. Elevated expressions of ten metabolic genes in LUAD were significantly associated with poor survival outcomes. These metabolic genes were highly expressed in more advanced tumor stage and TP53 mutated patients. Moreover, expression levels were significantly correlated with tumor-infiltrating immune cells. PPI interaction analysis revealed that the top 20 genes interacting with each metabolic gene were significantly enriched in DNA replication, response to radiation, and central carbon metabolism in cancer.

CONCLUSION

This study elucidates on molecular changes in metabolic genes in LUAD, which may inform the development of genetically oriented diagnostic approaches and effective treatment options.

The Overexpression of SLC25A13 Predicts Poor Prognosis and Is Correlated with Immune Cell Infiltration in Patients with Skin Cutaneous Melanoma

Purpose

Skin cutaneous melanoma (SKCM) is one of the most malignant and aggressive cancers with poor prognosis due to its rapid progression towards metastasis. Thus, finding clinically relevant biomarkers for early diagnosis, prognosis, and therapy prediction is essential. This study focused on the identification of SLC25A13 as a novel biomarker for SKCM and is aimed at investigating the biological functions of solute carrier family 25 member 13 (SLC25A13) in the development of SKCM.

Methods

GEPIA was used to analyze the diagnostic and prognostic values of SLC25A13 in SKCM using the TCGA dataset. PrognoScan was used to validate the prognostic value of SLC25A13 and its coexpressed genes in SKCM. TISIDB was established to reveal the relationship between the expression of SLC25A13 and immune infiltration in SKCM. The protein expression of SLC25A13 in SKCM was evaluated by the Human Protein Atlas. The signaling pathways and biological functions of SLC25A13 in SKCM were analyzed by LinkOmics. Metascape was applied to analyze the functional enrichment analysis of SLC25A13. Protein-protein interaction analysis of SLC25A13 was performed by GeneMANIA.

Results

The mRNA and protein levels of SLC25A13 in the SKCM were much higher than those in the normal tissue. Furthermore, the overexpression of SLC25A13 predicts worse outcomes of SKCM patients. Moreover, the SLC25A13 expression was negatively correlated with the immune infiltration level of SKCM. The overexpression of SLC25A13 coexpressed genes, such as ACLY and AFG3L2, and SCL25A13 interacting genes also predicted the unfavorable prognosis of SKCM patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of SLC25A13 coexpressed genes showed that these genes are enriched in ATPase activity, cell cycle, mTOR, and VEGFA-VEGFR2 signaling pathways, which were relevant to tumor development and angiogenesis. Gene set enrichment analysis (GSEA) demonstrated that the SLC25A13 expression was related to infiltrating immune cells in SKCM.

Conclusion

Our findings revealed that SLC25A13 might be a potential prognostic and therapeutic biomarker for SKCM.

Identification of significant genes with a poor prognosis in skin cutaneous malignant melanoma based on a bioinformatics analysis

Background

Skin cutaneous malignant melanoma (SKCM) is a deadly mutated malignancy that arises from melanocytes in the basal layer of the skin. This study sought to identify effective treatment targets that could serve as prospective therapeutic targets to improve patient outcomes.

Methods

The GSE83583, GSE111766, and GSE104849 data sets from the GPL10558 platform in the Gene Expression Omnibus (GEO) were used in this study. The candidate genes were identified using the GEO2R tool and a Venn diagram. The Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Gene and Genome (KEGG) preliminary analyses of the differentially expressed genes (DEGs) were conducted using the Database for Annotation, Visualization and Integrated Discovery, and R software. The protein-protein interaction (PPI) network was examined using Cytoscape software. The survminer package was used to examine the overall survival of patients with the identified genes. The Human Protein Atlas (HPA) was used to verify the protein levels of significant genes with poor prognosis. The highly expressed genes in the melanoma tissues were visualized using the ggplot2 package.

Results

In total, 160 DEGs from 124 melanoma tissues and 9 normal melanocyte tissues were examined in this study. Cytoscape displayed 19 central nodes from the 160 DEGs. The re-analysis showed that the cytochrome P450 family 1 subfamily B member 1 (CYP1B1) and protein kinase C beta (PRKCB) were significantly enriched in the micro ribonucleic acids (RNAs) in cancer.

Conclusions

CYP1B1 and PRKCB were overexpressed in and correlated with the poor prognosis of SKCM. Our findings might help explore the prognosis and diagnostic markers of SKCM.

CXCL9-modified CAR T cells improve immune cell infiltration and antitumor efficacy

Chimeric antigen receptor (CAR) T cells remain unsatisfactory in treating solid tumors. The frequency of tumor-infiltrating T cells is closely related to the good prognosis of patients. Augmenting T cell accumulation in the tumor microenvironment is essential for tumor clearance. To overcome insufficient immune cell infiltration, innovative CAR designs need to be developed immediately. CXCL9 plays a pivotal role in regulating T cell migration and inhibiting tumor angiogenesis. Therefore, we engineered CAR T cells expressing CXCL9 (CART-CXCL9). The addition of CXCL9 enhanced cytokine secretion and cytotoxicity of CAR T cells and endowed CAR T cells with the ability to recruit activated T cells and antiangiogenic effect. In tumor-bearing mice, CART-CXCL9 cells attracted more T cell trafficking to the tumor site and inhibited angiogenesis than conventional CAR T cells. Additionally, CART-CXCL9 cell therapy slowed tumor growth and prolonged mouse survival, displaying superior antitumor activity. Briefly, modifying CAR T cells to express CXCL9 could effectively improve CAR T cell efficacy against solid tumors.

Construction and Validation of a Ferroptosis-Related Prognostic Signature for Melanoma Based on Single-Cell RNA Sequencing

Melanoma, the deadliest type of skin cancer, is on the rise globally. The generally poor prognosis makes melanoma still an enormous public health problem. Ferroptosis is a newly emerging form of iron-dependent regulated cell death, which has been implicated in the development and treatment of several tumors. However, whether there is a connection between ferroptosis-related genes and the prognosis of melanoma patients remains an enigma. In the present study, we identified a ferroptosis-related genes signature to predict the prognosis of melanoma patients by analyzing single-cell RNA-sequencing data from the Gene Expression Omnibus (GEO). Single-cell trajectory analysis was performed to explore malignant differentiation. CellChat was used to investigate intercellular communications in melanoma. Collectively, a novel four-gene signature (CP, MAP1LC3A, transferrin, and TP53) was constructed for prognosis prediction. COX proportional hazards regression analysis showed that the established ferroptosis-associated risk model was an independent prognostic predictor for melanoma patients (HR = 2.3293; 95%CI 1.1528–4.706) (p < 0.018). Patients with low-risk scores had significantly better overall survival (OS) than those with high-risk scores in The Cancer Genome Atlas, GSE59455, and GSE22153 dataset (p = 0.0015, p = 0.031, p = 0.077). Furthermore, the gene expression level of the four genes were verified in multistrain melanoma cell lines and normal human epidermal melanocytes (NHEM). The protein expression level of the four genes in clinical samples were further verified in the Human Protein Atlas (HPA) databases. Taken together, our study identified the prognostic significance of the ferroptosis-related genes in melanoma and developed a novel four-gene prognostic signature, which may shed light on the prognostic assessment and clinical decision making for melanoma patients.

An Integrated Pan-Cancer Analysis of 33 Human Cancers Reveals the Potential Clinical Implications and Immunotherapeutic Value of C-X-C Motif Chemokine Ligand 13

Background

C-X-C Motif Chemokine Ligand 13 (CXCL13) plays a crucial part in the pathogenesis of numerous lymphoproliferative disorders, inflammatory responses, and autoimmune diseases. CXCL13 also influence tumor development and prognosis, and be a potential target for cancer treatment. However, CXCL13 expression-based panoramic picture in pan-cancer remain unclear. This study focused on elucidating different expression levels, prognostic significance, immune-related characteristics, epigenetic variations, and immunotherapeutic value of CXCL13.

Methods

Based on different databases such as TCGA, GTEX, CCLE and HPA, we studied the expression of CXCL13 in different tissues at different levels. Moreover, we analyzed the correlation between CXCL13 expression and clinicopathological characteristics, prognosis, Mismatch Repair Genes (MMRs), Microsatellite Instability (MSI), tumor mutation burden (TMB), immune cells infiltration, immune-related genes, and the role in tumor immunotherapy. And the expression of CXCL13 in digestive tract cancers and the correlation between CXCL13 and immune genes were further analyzed by histological verification.

Results

CXCL13 was highly expressed in various tumor tissues and was also closely related to prognosis. CXCL13 expression levels were closely related to MSI, TMB and immune cells infiltration, including CD8+ T cells, macrophages, follicular helper T cells and B cells. CXCL13 expression levels were related to immune checkpoint genes and the efficacy of immunotherapy.

Conclusion

CXCL13 might be a useful biomarker for determining the diagnosis and prognosis of human cancers but also a biomarker for evaluating the efficacy of immunotherapy.

New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation

Skin cutaneous melanoma (SKCM) is the most aggressive and fatal type of skin cancer. Its highly heterogeneous features make personalized treatments difficult, so there is an urgent need to identify markers for early diagnosis and therapy. Detailed profiles are useful for assessing malignancy potential and treatment in various cancers. In this study, we constructed a co-expression module using expression data for cutaneous melanoma. A weighted gene co-expression network analysis was used to discover a co-expression gene module for the pathogenesis of this disease, followed by a comprehensive bioinformatics analysis of selected hub genes. A connectivity map (CMap) was used to predict drugs for the treatment of SKCM based on hub genes, and immunohistochemical (IHC) staining was performed to validate the protein levels. After discovering a co-expression gene module for the pathogenesis of this disease, we combined GWAS validation and DEG analysis to identify 10 hub genes in the most relevant module. Survival curves indicated that eight hub genes were significantly and negatively associated with overall survival. A total of eight hub genes were positively correlated with SKCM tumor purity, and 10 hub genes were negatively correlated with the infiltration level of CD4+ T cells and B cells. Methylation levels of seven hub genes in stage 2 SKCM were significantly lower than those in stage 3. We also analyzed the isomer expression levels of 10 hub genes to explore the therapeutic target value of 10 hub genes in terms of alternative splicing (AS). All 10 hub genes had mutations in skin tissue. Furthermore, CMap analysis identified cefamandole, ursolic acid, podophyllotoxin, and Gly-His-Lys as four targeted therapy drugs that may be effective treatments for SKCM. Finally, IHC staining results showed that all 10 molecules were highly expressed in melanoma specimens compared to normal samples. These findings provide new insights into SKCM pathogenesis based on multi-omics profiles of key prognostic biomarkers and drug targets. GPR143 and SLC45A2 may serve as drug targets for immunotherapy and prognostic biomarkers for SKCM. This study identified four drugs with significant potential in treating SKCM patients.

EZH2 Inhibitor Enhances the STING Agonist‒Induced Antitumor Immunity in Melanoma

STING agonists are a new class of drugs for cancer immunotherapy that activate both innate and adaptive antitumor immunity. Recently, multiple clinical trials of STING agonists have been conducted in hematological malignancies and solid tumors. However, STING is commonly suppressed in melanoma through mechanisms that remain unclear. We found that STING expression was epigenetically suppressed by H3K27me3 in melanoma, and EZH2 inhibitor could induce an H3K27 shift from trimethylation to acetylation, resulting in increased expression of STING. Furthermore, a combination of STING agonist and EZH2 inhibitor upregulated major histocompatibility complex class I expression and chemokine production. Whole-transcriptome analysis showed that IFN-1‒related genes were significantly upregulated in the combination treatment group. In addition, the combination treatment synergistically reduced tumor growth and increased CD8⁺ T-cell infiltration in a poorly immunogenic melanoma mouse model B16-F10. These results showed, to our knowledge, a previously unreported mechanism underlying the epigenetic regulation of STING expression in melanoma; a combination of STING agonists and EZH2 inhibitors can boost the antitumor immune response and would be a promising treatment option for patients with melanoma who are refractory to current immunotherapies.

Identification of a Five-Gene Prognostic Signature Related to B Cells Infiltration in Pancreatic Adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is an extremely malignant cancer. Immunotherapy is a promising avenue to increase the survival time of patients with PAAD.

Methods

RNA sequencing and clinical data for PAAD were downloaded from the TCGA database. The ssGSEA method and weighted gene co-expression network analysis were used to calculate the relative abundance of tumor-infiltrating immune cells and identify the related modules. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were used to construct a prognostic model. MCPcounter and EPIC were also used to assess immune cell components using gene expression profiles.

Results

The B cells closely related module was identified, and five genes, including ARID5A, CLEC2B, MICAL1, MZB1, and RAPGEF1, were ultimately selected to establish a prognostic signature to calculate the risk scores of PAAD patients. Kaplan–Meier curves showed worse survival in the high-risk patients (p < 0.05), and the area under the receiver operating characteristic (ROC) curves of risk score for 1-year and 3-year survival were 0.78 and 0.80, respectively, based on the training set. Similar results were verified using the validated and combined sets. Interestingly, the low-risk group presented significantly elevated immune and stromal scores, proportion of B cells, and associations between these five genes and B cells were identified using multiple methods including ssGSEA, MCPcounter, and EPIC.

Conclusion

This is the first attempt to study a B cells-related prognostic signature, which is instrumental in the exploration of novel prognostic biomarkers in PAAD.

Identification of CXCL13 as an Immune-Related Biomarker Associated with Tumorigenesis and Prognosis in Cutaneous Melanoma Patients

BACKGROUND Melanoma is one of the most lethal tumors and its treatment is still challenging. It is urgent to detect novel therapy targets in melanoma. MATERIAL AND METHODS The GEO dataset was used to obtain a list of DEGS (differentially-expressed genes). Integrative bioinformatics analyses, including HPRD database, TCGA data, and TIMER, were performed to determine the role of CXCL13 in SKCM (skin cutaneous melanoma) progression and the immune environment. Furthermore, Pearson correlation coefficient analysis was used to measure correlations between CXCL13 and its co-expressed genes. Survival analysis, GO, and KEGG enrichment analysis were performed to investigate the role of CXCL13 in SKCM. RESULTS A total of 41 DEGs were identified in 3 GEO datasets, and 4 out of 41 DEGs are hub genes. Among the 4 hub genes, CXCL13 is involved in the most KEGG terms. CXCL13 is co-expressed with well-known immune checkpoint blockade targets, and it was associated with better overall survival. In addition, CXCL13 levels in infiltrating immune cells (neutrophil and myeloid dendritic cells) affect prognosis and survival in SKCM. Functional enrichment analysis clarified that CXCL13-co-expressed top 30 genes were associated with immune signaling pathways. Network analysis identified CXCL13 as a hub gene that interacts with CXCR5 to participate in immune-related biological process. CONCLUSIONS This study found that CXCL13 is associated with SKCM tumorigenesis and prognosis and immune infiltrations. Our result suggests that CXCL13 has great potential in development of novel immunotherapy targets in melanoma.

The Research on the Treatment of Metastatic Skin Cutaneous Melanoma by Huanglian Jiedu Decoction Based on the Analysis of Immune Infiltration Analysis

Objective

To explore the potential mechanism of Huanglian Jiedu Decoction (HJD) treatment and prevention of metastatic Cutaneous Melanoma (CM) occurrence and metastasis based on network pharmacological methods and immune infiltration analysis.

Methods

The GEO database was used to obtain metastatic CM disease targets, the TCMSP database and the HERB database were used to obtain HJD action targets, core genes were screened by protein interaction network, and the potential mechanism of HJD in the treatment of metastatic CM was explored by enrichment analysis, prognostic analysis and immune infiltration analysis.

Results

HJD treatment of metastatic CM involved 60 targets, enrichment analysis showed that HJD treatment of metastatic CM involved Chemokine signaling pathway, NF-kappa B signaling pathway, and Fluid shear stress and atherosclerosis, etc. Prognostic analysis revealed that HJD had a certain ability to improve the prognosis of metastatic CM patients. Immune infiltration analysis showed that HJD could inhibit the immune cell infiltration of metastatic CM patients by acting on related targets.

Conclusions

Our study identified the potential mechanism of HJD in the treatment of metastatic CM through network pharmacology, and revealed the mechanism of HJD in the prevention of Skin Cutaneous Melanoma metastasis through immune infiltration analysis and prognostic analysis.

Immune Monitoring in Melanoma and Urothelial Cancer Patients Treated with Anti-PD-1 Immunotherapy and SBRT Discloses Tumor Specific Immune Signatures

Simple Summary

Currently available biomarkers for response to checkpoint inhibitors are incomplete and predominantly focus on tumor tissue analysis e.g., tumor mutational burden, programmed cell death-ligand 1 (PD-L1) expression. Biomarkers in peripheral blood would allow a more dynamic monitoring and could offer a way for sequential adaptation of treatment strategy. We conducted an in-depth analysis of baseline and on-treatment systemic immune features in a cohort of stage III/IV melanoma and stage IV urothelial cancer (UC) patients treated with anti-programmed cell death-1 (anti-PD-1) therapy combined with stereotactic body radiotherapy (SBRT) in a similar regimen/schedule. Baseline immunity was clearly different between these two cohorts, indicating a less active immune landscape in UC patients. This study also detected signatures of proliferation in the CD8⁺ T-cell compartment pre-treatment and early after anti-PD-1 initiation that were positively correlated with clinical outcome in both tumor types. In addition our data support the biological relevance of PD-1/PD-L1 expression on circulating immune cell subsets, especially in melanoma.

Abstract

(1) Background: Blockade of the PD-1/PD-L1 pathway has revolutionized the oncology field in the last decade. However, the proportion of patients experiencing a durable response is still limited. In the current study, we performed an extensive immune monitoring in patients with stage III/IV melanoma and stage IV UC who received anti-PD-1 immunotherapy with SBRT. (2) Methods: In total 145 blood samples from 38 patients, collected at fixed time points before and during treatment, were phenotyped via high-parameter flow cytometry, luminex assay and UPLC-MS/MS. (3) Results: Baseline systemic immunity in melanoma and UC patients was different with a more prominent myeloid compartment and a higher neutrophil to lymphocyte ratio in UC. Proliferation (Ki67⁺) of CD8⁺ T-cells and of the PD-1⁺/PD-L1⁺ CD8⁺ subset at baseline correlated with progression free survival in melanoma. In contrast a higher frequency of PD-1/PD-L1 expressing non-proliferating (Ki67⁻) CD8⁺ and CD4⁺ T-cells before treatment was associated with worse outcome in melanoma. In UC, the expansion of Ki67⁺ CD8⁺ T-cells and of the PD-L1⁺ subset relative to tumor burden correlated with clinical outcome. (4) Conclusion: This study reveals a clearly different immune landscape in melanoma and UC at baseline, which may impact immunotherapy response. Signatures of proliferation in the CD8⁺ T-cell compartment prior to and early after anti-PD-1 initiation were positively correlated with clinical outcome in both cohorts. PD-1/PD-L1 expression on circulating immune cell subsets seems of clinical relevance in the melanoma cohort.

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Objectives

Osteosarcoma was the most popular primary malignant tumor in children and adolescent, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past 35 years. This study aims to explore the role of lipid metabolism in the development and diagnosis of osteosarcoma.

Methods

Clinical information and corresponding RNA data of osteosarcoma patients were downloaded from TRGET and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the tumor immune microenvironment (TIME) and immune status of the identified subgroups. Functional analyses including GO, KEGG, GSVA and GSEA analyses were conducted to elucidate the underlying mechanisms. Prognostic risk model was constructed using LASSO algorithm and multivariate Cox regression analysis.

Results

Two molecular subgroups with significantly different survival were identified. Better prognosis was associated with high immune score, low tumor purity, high abundance of immune infiltrating cells and relatively high immune status. GO and KEGG analyses revealed that the DEGs between the two subgroups were mainly enriched in immune- and bone remodeling-associated pathways. GSVA and GSEA analyses indicated that, lipid catabolism downregulation and lipid hydroxylation upregulation may impede the bone remodeling and development of immune system. Risk model based on lipid metabolism related genes (LMRGs) showed potent potential for survival prediction in osteosarcoma. Nomogram integrating risk model and clinical characteristics could predict the prognosis of osteosarcoma patients accurately.

Conclusion

Expression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately.

A Nomogram Combining a Four-Gene Biomarker and Clinical Factors for Predicting Survival of Melanoma

Background

Currently there is no effective prognostic indicator for melanoma, the deadliest skin cancer. Thus, we aimed to develop and validate a nomogram predictive model for predicting survival of melanoma.

Methods

Four hundred forty-nine melanoma cases with RNA sequencing (RNA-seq) data from TCGA were randomly divided into the training set I (n = 224) and validation set I (n = 225), 210 melanoma cases with RNA-seq data from Lund cohort of Lund University (available in GSE65904) were used as an external test set. The prognostic gene biomarker was developed and validated based on the above three sets. The developed gene biomarker combined with clinical characteristics was used as variables to develop and validate a nomogram predictive model based on 379 patients with complete clinical data from TCGA (Among 470 cases, 91 cases with missing clinical data were excluded from the study), which were randomly divided into the training set II (n = 189) and validation set II (n = 190). Area under the curve (AUC), concordance index (C-index), calibration curve, and Kaplan-Meier estimate were used to assess predictive performance of the nomogram model.

Results

Four genes, i.e., CLEC7A, CLEC10A, HAPLN3, and HCP5 comprise an immune-related prognostic biomarker. The predictive performance of the biomarker was validated using tROC and log-rank test in the training set I (n = 224, 5-year AUC of 0.683), validation set I (n = 225, 5-year AUC of 0.644), and test set I (n = 210, 5-year AUC of 0.645). The biomarker was also significantly associated with improved survival in the training set (P < 0.01), validation set (P < 0.05), and test set (P < 0.001), respectively. In addition, a nomogram combing the four-gene biomarker and six clinical factors for predicting survival in melanoma was developed in the training set II (n = 189), and validated in the validation set II (n = 190), with a concordance index of 0.736 ± 0.041 and an AUC of 0.832 ± 0.071.

Conclusion

We developed and validated a nomogram predictive model combining a four-gene biomarker and six clinical factors for melanoma patients, which could facilitate risk stratification and treatment planning.

Transcriptome Characterization of Short Distance Transport Stress in Beef Cattle Blood

The transportation is a crucial phase in beef cattle industry, and the annual losses caused by beef cattle transport stress are substantial. Several studies have described the effect of long distance transportation stress on animal health, such as disorder in nervous, endocrine, immune, and metabolic system. However, molecular mechanisms underlying short distance transportation stress is still poorly understood. Present study aims to investigate the effect of short distance transportation by measuring the hematological indices and transcriptomic analysis. In this study, a total 10 Qinchuan cattle were used to compare the molecular characteristics of blood before and after transportation. We have found that a stress-related marker "white blood cell count (WBC)" increased significantly after transportation. The decrease in triglyceride (TG), cholestenone (CHO), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) showed that energy expenditure was increased after transportation, but not enough to activate fatty decomposition. Intriguingly, the decrease of malondialdehyde (MDA) showed that cattle were more resilience to oxidative stress. The RNA-seq showed that 1,092 differentially expressed genes (DEGs) were found (329 up-regulated and 763 down-regulated) between group before and group after. The GO and KEGG enrichment showed that the metabolic pathway and B cell function related pathways were enriched. Furthermore, median absolute deviation (MAD) top 5,000 genes were used to construct a co-expression network by weighted correlation network analysis (WGCNA), and 11 independent modules were identified. Combing with protein-protein interaction (PPI) analysis, the verification of quantitative real-time PCR (qPCR) and the correlation of B cell function, structural maintenance of chromosomes 3 (SMC3), jun proto-oncogene (JUN), and C-X-C motif chemokine ligand 10 (CXCL10) were suggested as potential molecular markers in identification of short distance transportation. Collectively, the blood RNA-seq analysis and WGCNA indicated that the disorder of B cell differentiation, proliferation, survival, and apoptosis were the potential molecular mechanism in short distance transportation stress. In conclusion, our results provide the novel insight about potential biomarkers for short distance transportation stress, which may serve as for diagnosing and preventing this condition in beef industry.

Identification of Immune-Related Prognostic Biomarkers Associated with HPV-Positive Head and Neck Squamous Cell Carcinoma

Background

As a type of malignant tumor, head and neck squamous cell carcinoma (HNSCC) seriously threatens human health. This study is aimed at constructing a new, reliable prognostic model.

Method

The gene expression profile data of HNSCC patients were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases. The immune-related differentially expressed genes (IRDEGs) related to HNSCC were identified. We then used Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis to explore IRDEGs related to the HNSCC prognosis and to construct and validate a risk scoring model and used ESTIMATE to evaluate tumor immune infiltration in HNSCC patients. Finally, we validated IGSF5 expression and function in HNSCC cells.

Results

A total of 1,195 IRDEGs were found from the GSE65858 dataset. Thirty-one of the 1,195 IRDEGs were associated with the prognosis of HNSCC. Nine key IRDEGs were further selected using the LASSO method, and a risk scoring model was established for predicting the survival of HNSCC patients. According to the risk scoring model, the prognosis of patients in the high-risk group was worse than that of the low-risk group; the high-risk group had significantly higher immune scores than the low-risk group; and between the high- and low-risk samples, there were significant differences in the proportion of 10 types of cells, including naive cells, plasma cells, and resting CD4⁺ memory T cells. IGSF5 has low expression in HNSCC, and overexpression of IGSF5 significantly impaired HNSCC cell proliferation.

Conclusion

This prognostic risk assessment model can help systematically evaluate the survival prognosis of HNSCC patients and provides a new research direction for the improvement of the survival prognosis of HNSCC patients in clinical practice.