A Cancer Associated Fibroblasts-Related Six-Gene Panel for Anti-PD-1 Therapy in Melanoma Driven by Weighted Correlation Network Analysis and Supervised Machine Learning

Laser-enzyme dual responsive liposomes to regulate autophagy in synergy with phototherapy for melanoma treatment

Phototherapy can cause autophagy while killing tumour cells, leading to tumour recurrence and metastasis. Here, we constructed a laser and enzyme dual responsive nanodrug delivery system Tf-Te@CTSL-HCQ (TT@CH) to precisely regulate autophagy in synergy with phototherapy to inhibit the proliferation and metastasis of melanoma. Firstly, transferrin (Tf) was used as a nanoreactor to synthesise phototherapy agent Tf-Te by the biological template mineralisation method. Then, the thermosensitive liposome modified with FAP-α-responsive peptide (CAP) was used as a carrier to encapsulate autophagy inhibitor hydroxychloroquine (HCQ) and Tf-Te, to obtain an intelligent TT@CH delivery system. Once arriving at the tumour site, TT@CH can be cleaved by FAP-α overexpressed on cancer-associated fibroblasts (CAFs), and release Tf-Te and HCQ. Then Tf-Te can target melanoma cells and exert PTT/PDT anti-tumour effect. What's more, hyperpyrexia induced by PTT can further promote drugs release from TT@CH. Meanwhile, HCQ simultaneously inhibited autophagy of CAFs and melanoma cells, and down-regulated IL-6 and HMGB1 secretion, thus effectively inhibiting melanoma metastasis. Pharmacodynamic results exhibited the best anti-tumour effect of TT@CH with the highest tumour inhibition rate of 91.3%. Meanwhile, lung metastatic nodules of TT@CH treated mice reduced by 124.33 compared with that of mice in control group. Overall, TT@CH provided an effective therapy strategy for melanoma.

Immune infiltration-related genes regulate the progression of AML by invading the bone marrow microenvironment

In this study, we try to find the pathogenic role of immune-related genes in the bone marrow microenvironment of AML. Through WGCNA, seven modules were obtained, among which the turquoise module containing 1793 genes was highly correlated with the immune infiltration score. By unsupervised clustering, the turquoise module was divided into two clusters: the intersection of clinically significant genes in the TCGA and DEGs to obtain 178 genes for mutation analysis, followed by obtaining 17 genes with high mutation frequency. Subsequently, these 17 genes were subjected to LASSO regression analysis to construct a riskscore model of 8 hub genes. The TIMER database, ImmuCellAI portal website, and ssGSEA elucidate that the hub genes and risk scores are closely related to immune cell infiltration into the bone marrow microenvironment. In addition, we also validated the relative expression levels of hub genes using the TCGA database and GSE114868, and additional expression levels of hub genes in AML cell lines in vitro. Therefore, we constructed an immune infiltration-related gene model that identify 8 hub genes with good risk stratification and predictive prognosis for AML.

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

Background

The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy.

Methods

Relevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0.

Results

A total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629-0.828) for PFS in the training set, 0.760 (95%CI: 0.728-0.792) and 0.819 (95%CI: 0.757-0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721-0.771) and 0.700 (95%CI: 0.677-0.724) for OS in the training and validation sets, respectively.

Conclusion

Machine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

Informing immunotherapy with multi-omics driven machine learning

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

CPXM1 correlates to poor prognosis and immune cell infiltration in gastric cancer

Background

Gastric cancer (GC) is the fourth most common cause of cancer-related death and the fifth most frequent malignant cancer, especially advanced GC. Carboxypeptidase X member 1 (CPXM1) is an epigenetic factor involved in many physiological processes, including osteoclast differentiation and adipogenesis. Several studies have shown the association of CPXM1 with multiple tumors; however, the mechanism of CPXM1 involvement in the progression of GC is yet to be characterized.

Method

CPXM1 expression data were obtained from the Tumor Immune Estimation Resource. The Cancer Genome Atlas and the Gene Expression Omnibus databases were used to obtain patient-matched clinicopathological information, and the Kaplan-Meier plot database was utilized for the prognosis analysis of GC patients. The Catalog of Somatic Mutations in Cancer and cBioportal databases were adopted to study CPXM1 mutations in tumors. Next, we utilized the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis for mechanism research. Furthermore, we performed tumor microenvironment and immune infiltration analysis based on CPXM1. Finally, we predicted sensitivity to several targeted drugs in GC patients based on CPXM1.CPXM1 is upregulated in GC and is correlated with poor prognosis, gender, and tumor stage in GC patients. Gene enrichment analysis suggested that CPXM1 may regulate the occurrence and progression of GC via the PI3K-AKT and TGF-β pathway. Moreover, CPXM1 expression results in an increase in the proportion of immune and stromal cells. Additionally, the proportion of plasma cells was inversely related to the expression of CPXM1, whereas macrophage M2 expression was proportionate to CPXM1 expression. Finally, six small-molecule drugs that showed notable variations in IC50 between two groups were screened.

Conclusion

These results suggested that CPXM1 regulates the progression of GC and may represent a novel target for the detection and treatment of GC.

The macrophage-associated prognostic gene ANXA5 promotes immunotherapy resistance in gastric cancer through angiogenesis

Gastric cancer (GC) remains a predominant form of malignant tumor globally, necessitating innovative non-surgical therapeutic approaches. This investigation aimed to delineate the expression landscape of macrophage-associated genes in GC and to evaluate their prognostic significance and influence on immunotherapeutic responsiveness. Utilizing the CellMarker2.0 database, we identified 69 immune cell markers with prognostic relevance in GC, including 12 macrophage-specific genes. A Weighted Gene Co-Expression Network Analysis (WGCNA) isolated 3,181 genes correlated with these macrophage markers. The Cancer Genome Atlas (TCGA-STAD) dataset was employed as the training set, while data from the GSE62254 served as the validation cohort. 13 genes were shortlisted through LASSO-Cox regression to formulate a prognostic model. Multivariable Cox regression substantiated that the calculated risk score serves as an imperative independent predictor of overall survival (OS). Distinct macrophage infiltration profiles, pathway associations, treatment susceptibilities, and drug sensitivities were observed between high- and low-risk groups. The preliminary validation of ANXA5 in predicting the survival rates of GC patients at 1 year, 3 years, and 5 years, as well as its expression levels were higher and role in promoting tumor angiogenesis in GC through immunohistochemistry and angiogenesis experiments. In summary, macrophage-related genes were potentially a novel crosstalk mechanism between macrophages and endothelial cells in the tumor microenvironment, and the interplay between inflammation and angiogenesis might have also offered new therapeutic targets, providing a new avenue for personalized treatment interventions.

DNA Methylation Aberrations in Dimethylarsinic Acid-Induced Bladder Carcinogenesis

Arsenic is a known human urinary bladder carcinogen. While arsenic is known to cause aberrant DNA methylation, the mechanism of arsenic-triggered bladder carcinogenesis is not fully understood. The goal of this study was to identify aberrant DNA methylation in rat bladder urothelial carcinoma (UC) induced by dimethylarsinic acid (DMAV), a major organic metabolite of arsenic. We performed genome-wide DNA methylation and microarray gene expression analyses of DMAV-induced rat UCs and the urothelium of rats treated for 4 weeks with DMAV. We identified 40 genes that were both hypermethylated and downregulated in DMAV-induced rat UCs. Notably, four genes (CPXM1, OPCML, TBX20, and KCND3) also showed reduced expression in the bladder urothelium after 4 weeks of exposure to DMAV. We also found that CPXM1 is aberrantly methylated and downregulated in human bladder cancers and human bladder cancer cells. Genes with aberrant DNA methylation and downregulated expression in DMAV-exposed bladder urothelium and in DMAV-induced UCs in rats, suggest that these alterations occurred in the early stages of arsenic-induced bladder carcinogenesis. Further study to evaluate the functions of these genes will advance our understanding of the role of aberrant DNA methylation in arsenic bladder carcinogenesis, and will also facilitate the identification of new therapeutic targets for arsenic-related bladder cancers.

Biomarker-Driven Personalization of Neoadjuvant Immunotherapy in Melanoma

The introduction of immunotherapy has ushered in a new era of anticancer therapy for many cancer types including melanoma. Given the increasing development of novel compounds and combinations and the investigation in earlier disease stages, the need grows for biomarker-based treatment personalization. Stage III melanoma is one of the front-runners in the neoadjuvant immunotherapy field, facilitating quick biomarker identification by its immunogenic capacity, homogeneous patient population, and reliable efficacy readout. In this review, we discuss potential biomarkers for response prediction to neoadjuvant immunotherapy, and how the neoadjuvant melanoma platform could pave the way for biomarker identification in other tumor types.

SIGNIFICANCE

In accordance with the increasing rate of therapy development, the need for biomarker-driven personalized treatments grows. The current landscape of neoadjuvant treatment and biomarker development in stage III melanoma can function as a poster child for these personalized treatments in other tumors, assisting in the development of new biomarker-based neoadjuvant trials. This will contribute to personalized benefit-risk predictions to identify the most beneficial treatment for each patient.

Synaptopodin-2: a potential tumor suppressor

Initially identified as an actin-binding protein containing a PSD95-DLG-ZO1 Domain (PZD domain), Synaptopodin 2 (SYNPO2) has long been considered a structural protein ubiquitously expressed in muscular tissues. However, emerging evidence suggests that SYNPO2 performs diverse functions in cancers in addition to its role in microfilament assembly. In most cancers, high SYNPO2 expression is positively correlated with a good prognosis, suggesting its role as a novel tumor suppressor. Abnormal SYNPO2 expression affects autophagy generation, particularly mitophagy induced by low oxidation or viral infection, as well as chaperone-mediated autophagy triggered by microfilament damage. Mechanically, SYNPO2 regulates tumor growth, metastasis, and invasion via activating the PI3K/AKT/mTOR signal and Hippo signaling pathways. Moreover, the subcellular localization, promoter methylation and single nucleotide polymorphism (SNP) of SYNPO2 have been associated with cancer progression and clinical outcomes, highlighting its potential as a prognostic or diagnostic target for this patient population. This review focuses on the role of SYNPO2 in cancer, including its generation, epigenetic modification, subcellular localization, and biological function.

Moringa oleifera leaf polysaccharides exert anti-lung cancer effects upon targeting TLR4 to reverse the tumor-associated macrophage phenotype and promote T-cell infiltration

Tumor-associated macrophages (TAMs) participate in tumorigenesis, growth, invasion as well as metastasis by facilitating an immunosuppressive tumor microenvironment. Reversing the pro-tumoral M2 phenotype of TAMs has become a hot spot in advancing cancer immunotherapy. In the current study, the content of Moringa oleifera leaf polysaccharides (MOLP) was determined and characterized, along with the anti-cancer mechanism of MOLP studied in a Lewis lung cancer (LLC) tumor-bearing mouse model and bone marrow-derived macrophages. The monosaccharide composition and gel permeation chromatography analyses show that MOLP are mainly composed of galactose, glucose, and arabinose, with approximately 17.35 kDa average molecular weight (Mw). In vivo studies demonstrate that MOLP convert TAMs from the immunosuppressive M2 phenotype to the antitumor M1 phenotype, thus inducing CXCL9 and CXCL10 expression and increasing T-cell infiltration in the tumor. Furthermore, macrophage depletion and T cell suppression demonstrated that the tumor suppressive effect of MOLP was reliant on reprogramming macrophage polarization and T cell infiltration. In vitro studies revealed that MOLP could induce the phenotypic switch from M2 macrophages to M1 by targeting TLR4. The current study highlights that MOLP are promising anticancer plant-derived polysaccharides with potential in modulating the immune microenvironment and have a bright application prospect in the immunotherapy of lung cancer.

Single-cell RNA sequencing reveals dysregulated fibroblast subclusters in prurigo nodularis

Prurigo nodularis (PN) is an intensely pruritic, chronic inflammatory skin disease that disproportionately affects black patients. However, the pathogenesis of PN is poorly understood. We performed single-cell transcriptomic profiling, ligand receptor analysis and cell trajectory analysis of 28,695 lesional and non-lesional PN skin cells to uncover disease-identifying cell compositions and genetic characteristics. We uncovered a dysregulated role for fibroblasts (FBs) and myofibroblasts as a key pathogenic element in PN, which were significantly increased in PN lesional skin. We defined seven unique subclusters of FBs in PN skin and observed a shift of PN lesional FBs towards a cancer-associated fibroblast (CAF)-like phenotype, with WNT5A+ CAFs increased in the skin of PN patients and similarly so in squamous cell carcinoma (SCC). A multicenter PN cohort study subsequently revealed an increased risk of SCC as well as additional CAF-associated malignancies in PN patients, including breast and colorectal cancers. Systemic fibroproliferative diseases were also upregulated in PN patients, including renal sclerosis and idiopathic pulmonary fibrosis. Ligand receptor analyses demonstrated increased FB1-derived WNT5A and periostin interactions with neuronal receptors MCAM and ITGAV, suggesting a fibroblast-neuronal axis in PN. Type I IFN responses in immune cells and increased angiogenesis/permeability in endothelial cells were also observed. As compared to atopic dermatitis (AD) and psoriasis (PSO) patients, increased mesenchymal dysregulation is unique to PN with an intermediate Th2/Th17 phenotype between atopic dermatitis and psoriasis. These findings identify a pathogenic role for CAFs in PN, including a novel targetable WNT5A+ fibroblast subpopulation and CAF-associated malignancies in PN patients.

Analysis of immunotherapeutic response-related signatures in esophageal squamous-cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is one of the most common and lethal malignant diseases. Immunotherapy has been widely studied and has exhibited potential in ESCC treatment. However, there are only a portion of ESCC patients have benefited from immunotherapy. We herein identified immunotherapeutic response-related signatures (IRRS) and evaluated their performance in ESCC prognosis and immunotherapeutic responsiveness.

Methods

We constructed an IRRS using the gene expression data of 274 ESCC patients based on y -30significantly differentially expressed genes, which were compared responders and non-responders from various patient cohorts treated with immunotherapy. Survival analysis was performed in both the GSE53625 and TCGA-ESCC cohorts. We also explored the differences in the tumor microenvironment between the high-IRRS and low-IRRS score groups using single-cell data as a reference. Three immunotherapy cohorts were used to verify the value of the IRRS in predicting immunotherapy response.

Results

Twelve immunotherapy-related genes were selected to construct a signature score and were validated as independent prognostic predictors for patients with ESCC. Patients with high IRRS scores exhibited an immunosuppressive phenotype. Therefore, patients with low IRRS scores may benefit from immunotherapy.

Conclusions

IRRS score is a biomarker for immunotherapy response and prognosis of ESCC.