A novel immune checkpoint siglec-15 antibody inhibits LUAD by modulating mφ polarization in TME

The expression characteristic and prognostic role of Siglec-15 in lung adenocarcinoma

Sialic acid-binding immunoglobulin-like lectin-15 (Siglec-15) has been identified as an immune suppressor and a promising candidate for immunotherapy of cancer management. However, the association between Siglec-15 expression and clinicopathological features of lung adenocarcinoma (LUAD), especially the prognostic role, is not fully elucidated. In this present study, a serial of bioinformatics analyses in both tissue and cell levels were conducted to provide an overview of Siglec-15 expression. Real-time quantitative PCR (qPCR) test, western blotting assay, and immunohistochemistry (IHC) analyses were conducted to evaluate the expression of Siglec-15 in LUAD. Survival analysis and Kaplan-Meier curve were employed to describe the prognostic parameters of LUAD. The results of bioinformatics analyses demonstrated the up-regulation of Siglec-15 expression in LUAD. The data of qPCR, western blotting, and IHC analyses further proved that the expression of Siglec-15 in LUAD tissues was significantly increased than that in noncancerous tissues. Moreover, the expression level of Siglec-15 protein in LUAD was substantially associated with TNM stage. LUAD cases with up-regulated Siglec-15 expression, positive N status, and advance TNM stage suffered a critical unfavorable prognosis. In conclusion, Siglec-15 could be identified as a novel prognostic biomarker in LUAD and targeting Siglec-15 may provide a promising strategy for LUAD immunotherapy.

Identifying the tumor-associated macrophage of lung adenocarcinoma reveals immune landscape through omics data integration

The tumor-associated macrophages (TAM) play a crucial role in lung adenocarcinoma (LUAD), which can cause the proliferation, migration and invasion of tumor cells. In particular, TAMs mainly regulate changes in the tumor microenvironment thereby contributing to tumorigenesis and progression. Recently, an increasing number of studies are using single-cell RNA (Sc-RNA) sequencing to investigate changes in the composition and transcriptomics of the tumor microenvironment. We obtained Sc-RNA sequencing data of LUAD from GEO database and transcriptome data with clinical information of LUAD patients from TCGA database. A group of important genes in the state transition of TAMs was identified by analyzing TAMs at the single-cell level, while 5 TAM-related prognostic genes were obtained by omics data integration, and a prognostic model was constructed. GOBP analysis revealed that TAM-related genes were mainly enriched in tumor-promoting and immunosuppression-related pathways. After ROC analysis, it was found that the AUC of the prognosis model reached 0.751, with well predictive effectiveness. The 5 unique genes, HLA-DMB, HMGN3, ID3, PEBP1, and TUBA1B, was finally identified through synthesized analysis. The transcriptional characteristics of 5 genes were determined through GEPIA2 database and RT-qPCR. The increased expression of TUBA1B in advanced LUAD may serve as a prognostic indicator, while low expression of PEBP1 in LUAD may have the potential to become a therapeutic target.

Identification of the novel biomarkers involved in the mitochondrial metabolism-related reactive oxygen species and their role in lung cancer T-cell exhaustion and immunotherapy

Purpose

To study the role of mitochondrial metabolism and obtain novel biomarkers in immunotherapy for non-small cell lung cancer (NSCLC).

Methods

We collected the 188 genes involved in mitochondrial metabolism(MMGs) from the MSIGDB project and then quantified the activity of mitochondrial metabolism. All the NSCLC patients were divided into C1 and C2 clusters based on the 26 prognosis-related MMGs. The differences in biology, differential immune microenvironment, chronic hypoxia and prognosis between C1 and C2 patients were also analyzed. In addition, we validated the results of bioinformatics analysis in lung cancer tissues and cell lines.

Results

Patients in the C2 cluster had a higher level of mitochondrial metabolism. Patients in the C2 cluster responded better to immunotherapy and had a lower level of T-cell exclusion. The markers of T-cell failure were upregulated in the C1 patients. Hypoxia can lead to a high percentage of C1 patients. ADH1C might be involved in mitochondrial metabolism and immunotherapy response, which can be affected by hypoxia, making it an underlying biomarker. The expression levels of ADH1C in BEAS-2B, H1299, A549 and H460 cells were detected, revealing that ADH1C is upregulated in lung cancer cells. We observed that patients with low ADH1C expression had a longer survival time. The enzyme activities of HK, PK, LDH and SDH were significantly reduced in H1299 and H460 cells with ADH1C knockdown, along with more ROS. Furthermore, the expression levels of PD-L1 and HHLA2 in tumor tissues were analyzed, which found that ADH1C was significantly positively correlated with the expression of PD-L1 and HHLA2.

Conclusions

In summary, our study comprehensively explored the molecules involved in mitochondrial metabolism and their role in immunotherapy and T lymphocyte failure.

Targeting myeloid cells for cancer immunotherapy: Siglec-7/9/10/15 and their ligands

Advances in immunotherapy have revolutionized cancer treatment, yet many patients do not show clinical responses. While most immunotherapies target T cells, myeloid cells are the most abundant cell type in solid tumors and are key orchestrators of the immunosuppressive tumor microenvironment (TME), hampering effective T cell responses. Therefore, unraveling the immune suppressive pathways within myeloid cells could unveil new avenues for cancer immunotherapy. Over the past decade, Siglec receptors and their ligand, sialic acids, have emerged as a novel immune checkpoint on myeloid cells. In this review, we highlight key findings on how sialic acids modify immunity in the TME through engagement of Siglec-7/9/10/15 expressed on myeloid cells, and how the sialic acid-Siglec axis can be targeted for future cancer immunotherapies.

Exploration of the prognostic prediction value of the PANoptosis-based risk score and its correlation with tumor immunity in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a common cancer with high mortality worldwide. PANoptosis is a novel inflammatory programmed cell death modality with the characteristics of pyroptosis, apoptosis and necroptosis. It is necessary to explore PANoptosis-related genes in LUAD patients and offer evidence for prognosis prediction and therapeutic strategies. Single-cell RNA sequencing data and RNA expression profiles of LUAD patients from The Cancer Genome Atlas and Gene Expression Omnibus databases are used to screen PANoptosis-related differential genes for the construction of a risk model. Fifteen PANoptosis-related markers with prognostic value were identified by Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis. Kaplan-Meier analysis and receiver operating characteristic curve analysis further demonstrated the significant predictive capability. Immune infiltration, Single Nucleotide Variants (SNV) mutations, and clinical drug susceptibility were analyzed. In conclusion, a risk model of 15 PANoptosis-related genes has significant value in prognostic prediction for LUAD and has potential to direct clinical therapeutic strategies during the treatment.

The Potential of Siglecs and Sialic Acids as Biomarkers and Therapeutic Targets in Tumor Immunotherapy

The dysregulation of sialic acid is closely associated with oncogenesis and tumor progression. Most tumor cells exhibit sialic acid upregulation. Sialic acid-binding immunoglobulin-like lectins (Siglecs) are receptors that recognize sialic acid and are expressed in various immune cells. The activity of Siglecs in the tumor microenvironment promotes immune escape, mirroring the mechanisms of the well-characterized PD-1/PD-L1 pathway in cancer. Cancer cells utilize sialic acid-linked glycans to evade immune surveillance. As Siglecs exhibit similar mechanisms as the established immune checkpoint inhibitors (ICIs), they are potential therapeutic targets for different forms of cancer, especially ICI-resistant malignancies. Additionally, the upregulation of sialic acid serves as a potential tumor biomarker. This review examines the feasibility of using sialic acid and Siglecs for early malignant tumor detection and discusses the potential of targeting Siglec-sialic acid interaction as a novel cancer therapeutic strategy.

Machine learning reveals PANoptosis as a potential reporter and prognostic revealer of tumour microenvironment in lung adenocarcinoma

Lung adenocarcinoma (LUAD), a prominent lung cancer subtype, has an underexplored relationship with PANoptosis, a recently discovered mode of tumour cell death. This study incorporated iron death, copper death, scorch death, necrotizing apoptosis and bisulfide death into a pan-death gene set (PANoptosis) and conducted single-cell analysis of scRNA-seq data from 11 LUAD samples. Differentially expressed genes were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. Univariate COX regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen PANoptosis key genes for constructing an LUAD risk model. The model's prognostic performance was evaluated using survival curves, risk scores and validation in the Gene Expression Omnibus database. The study also explored the correlation between risk scores, tumour biological function, immunotherapy, drug sensitivity and immune infiltration. The SMS gene in the PANoptosis model was silenced in two LUAD cell lines for cellular validation. Single-cell analysis revealed eight major cell types and several PANoptosis genes significantly associated with LUAD survival. The risk model demonstrated strong prognostic performance and association with immune infiltration, suggesting PANoptosis involvement in LUAD tumour immunity. Cellular validation further supported these findings. The PANoptosis key risk genes are believed to be closely related to the tumour microenvironment and immune regulation of LUAD, potentially providing valuable insights for early diagnosis and clinical treatment, and broader applications in other tumours and complex diseases.

Cancer cell-intrinsic PD-1: Its role in malignant progression and immunotherapy

Programmed cell death protein-1 (PD-1), also called CD279, is coded by the PDCD1 gene and is constitutively expressed on the surface of immune cells. As a receptor and immune checkpoint, PD-1 can bind to programmed death ligand-1/programmed death ligand-2 (PD-L1/PD-L2) in tumor cells, leading to tumor immune evasion. Anti-PD-1 and anti-PD-L1 are important components in tumor immune therapy. PD-1 is also expressed as an intrinsic variant (iPD-1) in cancer cells where it plays important roles in malignant progression as proposed by recent studies. However, iPD-1 has received much less attention compared to PD-1 expressed on immune cells although there is an unmet medical need for fully elucidating the mechanisms of actions to achieve the best response in tumor immunotherapy. iPD-1 suppresses tumorigenesis in non-small cell lung cancer (NSCLC) and colon cancer, whereas it promotes tumorigenesis in melanoma, hepatocellular carcinoma (HCC), pancreatic ductal adenocarcinoma (PDAC), thyroid cancer (TC), glioblastoma (GBM), and triple-negative breast cancer (TNBC). In this review, we focus on the role of iPD-1 in tumorigenesis and development and its molecular mechanisms. We also deeply discuss nivolumab-based combined therapy in common tumor therapy. iPD-1 may explain the different therapeutic effects of anti-PD-1 treatment and provide critical information for use in combined anti-tumor approaches.

Sialylation: A Cloak for Tumors to Trick the Immune System in the Microenvironment

The tumor microenvironment (TME), where the tumor cells incite the surrounding normal cells to create an immune suppressive environment, reduces the effectiveness of immune responses during cancer development. Sialylation, a type of glycosylation that occurs on cell surface proteins, lipids, and glycoRNAs, is known to accumulate in tumors and acts as a "cloak" to help tumor cells evade immunological surveillance. In the last few years, the role of sialylation in tumor proliferation and metastasis has become increasingly evident. With the advent of single-cell and spatial sequencing technologies, more research is being conducted to understand the effects of sialylation on immunity regulation. This review provides updated insights into recent research on the function of sialylation in tumor biology and summarizes the latest developments in sialylation-targeted tumor therapeutics, including antibody-mediated and metabolic-based sialylation inhibition, as well as interference with sialic acid-Siglec interaction.

The artificial intelligence and machine learning in lung cancer immunotherapy

Since the past decades, more lung cancer patients have been experiencing lasting benefits from immunotherapy. It is imperative to accurately and intelligently select appropriate patients for immunotherapy or predict the immunotherapy efficacy. In recent years, machine learning (ML)-based artificial intelligence (AI) was developed in the area of medical-industrial convergence. AI can help model and predict medical information. A growing number of studies have combined radiology, pathology, genomics, proteomics data in order to predict the expression levels of programmed death-ligand 1 (PD-L1), tumor mutation burden (TMB) and tumor microenvironment (TME) in cancer patients or predict the likelihood of immunotherapy benefits and side effects. Finally, with the advancement of AI and ML, it is believed that "digital biopsy" can replace the traditional single assessment method to benefit more cancer patients and help clinical decision-making in the future. In this review, the applications of AI in PD-L1/TMB prediction, TME prediction and lung cancer immunotherapy are discussed.