The EphA family belongs to a large group of membrane receptor tyrosine kinases. Emerging evidence indicates that the EphA family participates in tumour occurrence and progression. Nonetheless, the expression patterns and prognostic values of the nine EphAs in non-small cell lung cancer (NSCLC) have rarely been studied before. In the current study, we comprehensively analysed the expression and prognostic role of EphA family members by different means. The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis databases were used to investigate the expression of EphAs in NSCLC. The cBioPortal database was applied to analyse the prognostic values and genetic mutations of EphAs.We discovered that the expression of EphA10 was significantly higher in NSCLC tissues than in adjacent noncancerous tissues, and survival analyses showed that a higher level of EphA10 predicted poor prognosis. Further exploration into the role of EphA10 by ESTIMATE, CIBERSORT, and ssGSEA analysis found that it was also related to immune infiltration and higher expression of targets of ICI targets. In conclusion, this study revealed that among the EphA family members, EphA10 played an oncogenic role and was a promising biomarker for poor prognosis and better immunotherapy response in NSCLC.

BACKGROUND: M2-like tumor-associated macrophages (M2-like TAMs) play key roles in tumor progression and the immune response. However, the clinical significance and prognostic value of M2-like TAMs-associated regulatory genes in gastric cancer (GC) have not been clarified.
METHODS: Herein, we identified M2-like TAM-related genes by weighted gene coexpression network analysis of TCGA-STAD and GSE84437 cohort. Lasso-Cox regression analyses were then performed to screen for signature genes, and a novel signature was constructed to quantify the risk score for each patient. Tumor mutation burden (TMB), survival outcomes, immune cells, and immune function were analyzed in the risk groups to further reveal the immune status of GC patients. A gene-drug correlation analysis and sensitivity analysis of anticancer drugs were used to identify potential therapeutic agents. Finally, we verified the mRNA expression of signature genes in patient tissues by qRT-PCR, and analyzed the expression distribution of these genes by IHC.
RESULTS: A 4-gene (SERPINE1, MATN3, CD36, and CNTN1) signature was developed and validated, and the risk score was shown to be an independent prognostic factor for GC patients. Further analyses revealed that GC patients in the high-risk group had a worse prognosis than those in the low-risk group, with significant differences in TMB, clinical features, enriched pathways, TIDE score, and tumor microenvironment features. Finally, we used qRT-PCR and IHC analysis to verify mRNA and protein level expression of signature genes.
CONCLUSIONS: These findings highlight the importance of M2-like TAMs, provide a new perspective on individualized immunotherapy for GC patients.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and second leading cause of cancer-related deaths worldwide. The heightened mortality associated with HCC is largely attributed to its propensity for metastasis, which cannot be achieved without remodeling or loss of the basement membrane (BM). Despite advancements in targeted therapies and immunotherapies, resistance and limited efficacy in late-stage HCC underscore the urgent need for better therapeutic options and early diagnostic biomarkers. Our study aimed to address these gaps by investigating and evaluating potential biomarkers to improve survival outcomes and treatment efficacy in patients with HCC.
METHODS: In this study, we collected the transcriptome sequencing, clinical, and mutation data of 424 patients with HCC from The Cancer Genome Atlas (TCGA) and 240 from the International Cancer Genome Consortium (ICGC) databases. We then constructed and validated a prognostic model based on metastasis and basement membrane-related genes (MBRGs) using univariate and multivariate Cox regression analyses. Five immune-related algorithms (CIBERSORT, QUANTISEQ, MCP counter, ssGSEA, and TIMER) were then utilized to examine the immune landscape and activity across high- and low-risk groups. We also analyzed Tumor Mutation Burden (TMB) values, Tumor Immune Dysfunction and Exclusion (TIDE) scores, mutation frequency, and immune checkpoint gene expression to evaluate immune treatment sensitivity. We analyzed integrin subunit alpha 3 (ITGA3) expression in HCC by performing single-cell RNA sequencing (scRNA-seq) analysis using the TISCH 2.0 database. Lastly, wound healing and transwell assays were conducted to elucidate the role of ITGA3 in tumor metastasis.
RESULTS: Patients with HCC were categorized into high- and low-risk groups based on the median values, with higher risk scores indicating worse overall survival. Five immune-related algorithms revealed that the abundance of immune cells, particularly T cells, was greater in the high-risk group than in the low-risk group. The high-risk group also exhibited a higher TMB value, mutation frequency, and immune checkpoint gene expression and a lower tumor TIDE score, suggesting the potential for better immunotherapy outcomes. Additionally, scRNA-seq analysis revealed higher ITGA3 expression in tumor cells compared with normal hepatocytes. Wound healing scratch and transwell cell migration assays revealed that overexpression of the MBRG ITGA3 enhanced migration of HCC HepG2 cells.
CONCLUSIONS: This study established a direct molecular correlation between metastasis and BM, encompassing clinical features, tumor microenvironment, and immune response, thereby offering valuable insights for predicting clinical outcomes and immunotherapy responses in HCC.

Head and neck squamous carcinoma (HNSC) is a prevalent malignant disease, with the majority of patients being diagnosed at an advanced stage. Endoplasmic reticulum stress (ERS) is considered to be a process that promotes tumorigenesis and impacts the tumor microenvironment (TME) in various cancers. The study aims to investigate the predictive value of ERS in HNSC and explore the correlation between ERS-related genes and TME. A series of bioinformatics analyses were carried out based on mRNA and scRNA-seq data from the TCGA and GEO databases. We conducted RT-qPCR and western blot to validate the signature, and performed cell functional experiments to investigate the in vitro biological functions of the gene. We identified 63 ERS-related genes that were associated with outcome and stage in HNSC. A three-gene signature (ATF6, TRIB3, and UBXN6) was developed, which presents predictive value in the prognosis and immunotherapy response of HNSC patients. The high-risk group exhibited a worse prognosis but may benefit from immunotherapy. Furthermore, there was a significant correlation between the signature and immune infiltration. In the high-risk group, fibroblasts were more active in intercellular communication, and more T cells were observed at the end of the sequential phase. The genes in the ERS-related signature were overexpressed in HNSC cells, and the knockdown of TRIB3 significantly inhibited cell proliferation and migration. This study established a novel ERS-related signature that has potential implications for HNSC therapy and the understanding of TME.

BACKGROUND: Bladder cancer (BLCA) is a highly aggressive and heterogeneous disease, posing challenges for diagnosis and treatment. Cancer immunotherapy has recently emerged as a promising option for patients with advanced and drug-resistant cancers. Fibroblasts, a significant component of the tumor microenvironment, play a crucial role in tumor progression, but their precise function in BLCA remains uncertain.
METHODS: Single-cell RNA sequencing (scRNA-seq) data for BLCA were obtained from the Gene Expression Omnibus database. The R package \"Seurat\" was used for processing scRNA-seq data, with uniform manifold approximation and projection (UMAP) for downscaling and cluster identification. The FindAllMarkers function identified marker genes for each cluster. Differentially expressed genes influencing overall survival (OS) of BLCA patients were identified using the limma package. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between high- and low-risk groups were investigated. RT-qPCR and immunohistochemistry validated the expression of prognostic genes.
RESULTS: Fibroblast marker genes identified three molecular subtypes in the testing set. A prognostic signature comprising ten genes stratified BLCA patients into high- and low-score groups. This signature was validated in one internal and two external validation sets. High-score patients exhibited increased immune cell infiltration, elevated chemokine expression, and enhanced immune checkpoint expression but had poorer OS and a reduced response to immunotherapy. Six sensitive anti-tumor drugs were identified for the high-score group. RT-qPCR and immunohistochemistry showed that CERCAM, TM4SF1, FN1, ANXA1, and LOX were highly expressed, while EMP1, HEYL, FBN1, and SLC2A3 were downregulated in BLCA.
CONCLUSIONS: A novel fibroblast marker gene-based signature was established, providing robust predictions of survival and immunotherapeutic response in BLCA patients.

Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples. Using a single center cohort of 189 patients, our objective was to evaluate various fusion methods that used non-invasive computed tomography (CT) and 18 F-FDG positron emission tomography (PET) images as inputs to various deep learning models to automatically predict PD-L1 in non-small cell lung cancer (NSCLC). We compared three different architectures (ResNet, DenseNet, and EfficientNet) and considered different input data (CT only, PET only, PET/CT early fusion, PET/CT late fusion without as well as with partially and fully shared weights to determine the best model performance. Models were assessed utilizing areas under the receiver operating characteristic curves (AUCs) considering their 95% confidence intervals (CI). The fusion of PET and CT images as input yielded better performance for PD-L1 classification. The different data fusion schemes systematically outperformed their individual counterparts when used as input of the various deep models. Furthermore, early fusion consistently outperformed late fusion, probably as a result of its capacity to capture more complicated patterns by merging PET and CT derived content at a lower level. When we looked more closely at the effects of weight sharing in late fusion architectures, we discovered that while it might boost model stability, it did not always result in better results. This suggests that although weight sharing could be beneficial when modality parameters are similar, the anatomical and metabolic information provided by CT and PET scans are too dissimilar to consistently lead to improved PD-L1 status predictions.

[This corrects the article DOI: 10.3389/fimmu.2024.1391524.].

UNASSIGNED: While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy.
UNASSIGNED: Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.
UNASSIGNED: Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort.
UNASSIGNED: Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

This study constructed a comprehensive analysis of cell death modules in eliminating aberrant cells and remodeling tumor microenvironment (TME). Consensus analysis was performed in 490 lung adenocarcinoma (LUAD) patients based on 4 types of cell death prognostic genes. Intersection method divided these LUAD samples into 5 cell death risk (CDR) clusters, and COX regression analysis were used to construct the CDR signature (CDRSig) with risk scores. Significant differences of TME phenotypes, clinical factors, genome variations, radiosensitivity and immunotherapy sensitivity were observed in different CDR clusters. Patients with higher risk scores in the CDRSig tended to be immune-excluded or immune-desert, and those with lower risk scores were more sensitive to radiotherapy and immunotherapy. The results from mouse model showed that intense expression of the high-risk gene PFKP was associated with low CD8+ T cell infiltration upon radiotherapy and anti-PD-L1 treatment. Deficient assays in vitro confirmed that PFKP downregulation enhanced cGAS/STING pathway activation and radiosensitivity in LUAD cells. In conclusion, our studies originally performed a comprehensive cell death analysis, suggesting the importance of CDR patterns in reprogramming TME and providing novel clues for LUAD personalized therapies.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer characterized by metabolic reprogramming. Glutamine metabolism is pivotal in metabolic reprogramming, contributing to the significant heterogeneity observed in ccRCC. Consequently, developing prognostic markers associated with glutamine metabolism could enhance personalized treatment strategies for ccRCC patients. This study obtained RNA sequencing and clinical data from 763 ccRCC cases sourced from multiple databases. Consensus clustering of 74 glutamine metabolism related genes (GMRGs)- profiles stratified the patients into three clusters, each of which exhibited distinct prognosis, tumor microenvironment, and biological characteristics. Then, six genes (SMTNL2, MIOX, TMEM27, SLC16A12, HRH2, and SAA1) were identified by machine-learning algorithms to develop a predictive signature related to glutamine metabolism, termed as GMRScore. The GMRScore showed significant differences in clinical prognosis, expression profile of immune checkpoints, abundance of immune cells, and immunotherapy response of ccRCC patients. Besides, the nomogram incorporating the GMRScore and clinical features showed strong predictive performance in prognosis of ccRCC patients. ALDH18A1, one of the GRMGs, exhibited elevated expression level in ccRCC and was related to markedly poorer prognosis in the integrated cohort, validated by proteomic profiling of 232 ccRCC samples from Fudan University Shanghai Cancer Center (FUSCC). Conducting western blotting, CCK-8, transwell, and flow cytometry assays, we found the knockdown of ALDH18A1 in ccRCC significantly promoted apoptosis and inhibited proliferation, invasion, and epithelial-mesenchymal transition (EMT) in two human ccRCC cell lines (786-O and 769-P). In conclusion, we developed a glutamine metabolism-related prognostic signature in ccRCC, which is tightly linked to the tumor immune microenvironment and immunotherapy response, potentially facilitating precision therapy for ccRCC patients. Additionally, this study revealed the key role of ALDH18A1 in promoting ccRCC progression for the first time.