cPLA2 blockade attenuates S100A7-mediated breast tumorigenicity by inhibiting the immunosuppressive tumor microenvironment

Liquid-liquid phase separation-related genes associated with prognosis, tumor microenvironment characteristics, and tumor cell features in bladder cancer

OBJECTIVE

This study aimed to explore the Liquid-liquid phase separation (LLPS)-related genes associated with the prognosis of bladder cancer (BCa) and assess the potential application of LLPS-related prognostic signature for predicting prognosis in BCa patients.

METHODS

Clinical information and transcriptome data of BCa patients were extracted from the Cancer Genome Atlas-BLCA (TCGA-BLCA) database and the GSE13507 database. Furthermore, 108 BCa patients who received treatment at our institution were subjected to a retrospective analysis. The least absolute shrinkage and selection operator (LASSO) analysis was performed to develop an LLPS-related prognostic signature for BCa. The CCK8, wound healing and Transwell assays were performed.

RESULTS

Based on 62 differentially expressed LLPS-related genes (DELRGs), three DELRGs were screened by LASSO analysis including kallikrein-related peptidase 5 (KLK5), monoacylglycerol O-acyltransferase 2 (MOGAT2) and S100 calcium-binding protein A7 (S100A7). Based on three DELRGs, a novel LLPS-related prognostic signature was constructed for individualized prognosis assessment. Kaplan-Meier curve analyses showed that LLPS-related prognostic signature was significantly correlated with overall survival (OS) of BCa. ROC analyses demonstrated the LLPS-related prognostic signature performed well in predicting the prognosis of BCa patients in the training group (the area under the curve (AUC) = 0.733), which was externally verified in the validation cohort 1 (AUC = 0.794) and validation cohort 2 (AUC = 0.766). Further experiments demonstrated that inhibiting KLK5 could affect the proliferation, migration, and invasion of BCa cells.

CONCLUSIONS

In this study, a novel LLPS-related prognostic signature was successfully developed and validated, demonstrating strong performance in predicting the prognosis of BCa patients.

CENPF interaction with PLA2G4A promotes glioma growth by modulating mTORC1 and NF-κB pathways

BACKGROUND

Glioma is the most common primary malignant tumor of the central nervous system, and due to the limited effectiveness of traditional single-target therapies, there is an urgent need for new therapeutic targets. Centromere protein F (CENPF) belongs to the centromere protein family and is mainly involved in the regulation of the cell cycle. CENPF has recently been found to play a key role in tumorigenesis and tumor progression, but its role in gliomas has not been well studied.

METHODS

The expression level and clinical information of CENPF were obtained by analyzing the TCGA, CGGA and GEO databases. Immunohistochemistry and western blot analysis were used to quantitatively detect the expression of CENPF in glioma tissues and cell lines. Gene set enrichment analysis (GSEA) of TCGA and GSE16011 datasets was used to explore the molecular mechanism of the CENPF. CENPF-interacting proteins were detected by molecular docking and co-immunoprecipitation (Co-IP). After silencing CENPF, CCK-8 assay, Transwell assay and flow cytometry were used to detect changes in cell proliferation, invasion, cell cycle and apoptosis, and Western blot was used to detect changes in signaling pathway protein levels.

RESULTS

Bioinformatics analysis showed that CENPF was generally highly expressed in gliomas and was associated with poor prognosis. This result was confirmed in glioma samples from our hospital. Multivariate Cox regression analysis showed that CENPF was an independent prognostic marker for gliomas. Western blot analysis in vitro showed that CENPF was overexpressed in the U251 and LN229 cell lines; therefore, these two cell lines were selected for subsequent experiments. GSEA analysis showed that CENPF was mainly involved in the G2/M phase-mediated cell cycle and P53 signaling pathway. Flow cytometry analysis confirmed that silencing CENPF induced G2/M phase arrest and increased apoptosis in glioma cells. Subsequent experiments confirmed that CENPF influences the epithelial-mesenchymal transition (EMT) process through the mTORC1 signaling pathway. Molecular docking and Co-IP assay revealed that CENPF exerts its effects by interacting with PLA2G4A promoting the downstream signaling pathway. Finally, we found that silencing CENPF combined with a PLA2G4A inhibitor (AACOCF3) induced glioma cell apoptosis and exhibited anti-glioma effects.

CONCLUSIONS

This study found that CENPF plays a key role in promoting tumorigenesis through its interaction with PLA2G4A. This study provides a theoretical foundation for advancing multi-targeted therapies in glioma and for developing strategies to overcome tumor drug resistance.

In Triple-Negative Breast Cancer: Correlation Among Metabolic Syndrome, S100A7/cPLA2 Expression and the Efficacy of Neoadjuvant Chemotherapy

BACKGROUND

Triple-negative breast cancer (TNBC) has a poor prognosis. Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is a prognostic factor. This study aimed to find predictors of efficacy.

METHODS

A total of 266 TNBC patients treated with NAC were included. The relationship between MetS, S100A7/cPLA2 expression and clinicopathological features was investigated. The effect on pCR, clinical response, and disease-free survival (DFS) was observed. A cell co-culture model was established by the researchers to further assess the function of S100A7.

RESULTS

Correlation analysis revealed a strong association between the expressions of S100A7 and cPLA2, with both significantly higher in the MetS group compared to the non-MetS group. Logistic regression analysis indicated that MetS and S100A7/cPLA2 expressions were linked to pCR and clinical response. S100A7/cPLA2 served as an independent predictor of pCR, while cPLA2 was an independent predictor of clinical response. Survival analysis demonstrated that MetS and S100A7/cPLA2 were associated with an increased risk of disease progression. MP grading and clinical efficacy were independent predictors of DFS, with MetS and S100A7/cPLA2 expressions correlating with shortened DFS. In the co-culture model, S100A7 inhibited the NF-κB pathway, enhancing TNBC cell proliferation and invasion in the presence of macrophages, and promoting M2 macrophage polarization.

CONCLUSION

S100A7/cPLA2 expression predicts a low pCR rate in TNBC patients undergoing NAC and may serve as a potential mechanistic biomarker linking MetS with altered NAC efficacy in TNBC, warranting further investigation and intervention.

Turning cold into hot: emerging strategies to fire up the tumor microenvironment

The tumor microenvironment (TME) is a complex, highly structured, and dynamic ecosystem that plays a pivotal role in the progression of both primary and metastatic tumors. Precise assessment of the dynamic spatiotemporal features of the TME is crucial for understanding cancer evolution and designing effective therapeutic strategies. Cancer is increasingly recognized as a systemic disease, influenced not only by the TME, but also by a multitude of systemic factors, including whole-body metabolism, gut microbiome, endocrine signaling, and circadian rhythm. In this review, we summarize the intrinsic, extrinsic, and systemic factors contributing to the formation of 'cold' tumors within the framework of the cancer-immunity cycle. Correspondingly, we discuss potential strategies for converting 'cold' tumors into 'hot' ones to enhance therapeutic efficacy.

Lipid metabolic remodeling delays senescence of T cells to potentiate their immunity against solid tumors

BACKGROUND

Tumor cells can drive the senescence of effector T cells by unbalancing their lipid metabolism, thereby limiting adoptive T cell therapy and contributing to tumor immune evasion. Our objective is to provide a feasible strategy for enhancing T cell treatment efficacy against solid tumors.

METHODS

In this study, liposomal arachidonyl trifluoromethyl ketone (ATK) was anchored onto the adoptive T cell surface via bioorthogonal reactions, aiming to specifically inhibit the group IVA cytosolic phospholipase A2α (cPLA2α), a key enzyme facilitating phospholipid metabolism and senescent state of T cells.

RESULTS

The surface engineering exerted rare side effects on the activation and migration of T cells, but local and sustained extravasation of ATK downregulated cPLA2α expression, reprogrammed lipid metabolism, and inhibited lipid droplet accumulation. This endows T cells with delayed senescence and declined apoptosis to maintain their tumor-killing potency. Systemic administration of surface-engineered T cells resulted in superior infiltration in solid tumors and improved antitumor efficacy by enhancing the secretion of cytotoxic molecules, thereby prolonging the survival of mice bearing colorectal carcinoma and melanoma xenografts.

CONCLUSIONS

Lipid-metabolically remodeled T cells with delayed senescence increase efficacy in tumor microenvironment, highlighting a novel strategy for solid tumor immunotherapy.

RAGE and its ligands in breast cancer progression and metastasis

Introduction

Breast cancer is the most common form of cancer diagnosed worldwide and the leading cause of death in women globally, according to Globocan 2020. Hence, investigating novel pathways implicated in cancer progression and metastasis could lead to the development of targeted therapies and new treatment strategies in breast cancer. Recent studies reported an interplay between the receptor for advanced glycation end products (RAGE) and its ligands, S100 protein group, advanced glycation end products (AGEs) and high-mobility group box 1 protein (HMGB1) and breast cancer growth and metastasis.

Materials and methods

We used articles available in the NCBI website database PubMed to write this scoping review. The search words used were 'RAGE receptor' AND/OR 'breast cancer, RAGE ligands, glycation end products'. A total of 90 articles were included. We conducted a meta-analysis to assess the relationship between the RAGE rs1800624 polymorphism and breast cancer risk using fixed-effect or random-effect models to calculate odds ratios (ORs) and their corresponding 95% confidence intervals (95% CIs).

Results

RAGE upon activation by its ligands enhances downstream signaling pathways, contributing to breast cancer cells migration, growth, angiogenesis, metastasis, and drug resistance. In addition, studies have shown that RAGE and its ligands influence the way breast cancer cells interact with immune cells present in the tumor microenvironment (macrophages, fibroblasts), thus regulating it to promote tumor growth and metastasis.

Conclusion

Breast cancers with a high expression of RAGE are associated with poor prognosis. Targeting RAGE and its ligands impairs cell invasion and metastasis, showing promising potential for further research as potential prognostic biomarkers or targeted onco-therapeutics.

Divergent paths of mammary gland involution: unveiling the cellular dynamics in abruptly and gradually involuted mouse models

BACKGROUND

Epidemiological studies associate an increase in breast cancer risk, particularly triple-negative breast cancer (TNBC), with lack of breastfeeding. This is more prevalent in African American women, with significantly lower rate of breastfeeding compared to Caucasian women. Prolonged breastfeeding leads to gradual involution (GI), whereas short-term or lack of breastfeeding leads to abrupt involution (AI) of the breast. Our previous study utilizing a murine model demonstrated precancerous changes, specifically hyperplasia, a non-obligate precursor of breast cancer in the mammary glands of AI mice. Here we investigated mechanisms during early events of AI that prompts precancerous changes in mouse mammary glands.

METHODS

Uniparous FVB/N mice were randomized to AI and GI on postpartum day 7 when all pups were removed from AI dams. GI dams were allowed to nurse the pups till day 31. Cell death kinetics and gene expression were assessed by TUNEL assay and qPCR respectively. Immune cell changes were investigated by flow cytometry, cytokine array and multiplex immunofluorescence. 3D-organoid cultures were used for in vitro assay of luminal progenitor cells.

RESULTS

AI results in rapid cell death, DNA repair response, and immunosuppressive myeloid cells infiltration, leading to a chronically inflamed microenvironment. GI elicits a more controlled immune response and extended cell death. At the peak of cell death, AI glands harbored more immunosuppressive myeloid-derived suppressor cells (MDSCs) and CD206 + M2-like macrophages, known to promote oncogenic events, compared to GI glands. AI glands exhibit an enrichment of CCL9-producing MDSCs and CD206 + M2-like macrophages that promote expansion of ELF5 + /ERα- luminal cells, both in vitro and in vivo. Multiplex imaging of AI glands demonstrated an increase in ELF5 + /WNT5a + luminal cells alongside a reduction in the ELF5 + /ERα + population when involution appeared histologically complete. A significantly higher number of CD206 + cells in post involution AI gland attests to a chronically inflamed state induced by AI.

CONCLUSIONS

Our findings reveal significant disparities between AI and GI gland dynamics at the early phase of involution. CCL9, secreted by immune cells at the peak of cell death promotes expansion of Elf5 + /ERα- luminal progenitor cells, the putative precursors of TNBC connecting early events of AI with increased breast cancer risk.

Elucidating the role of S100A10 in CD8+ T cell exhaustion and HCC immune escape via the cPLA2 and 5-LOX axis

Hepatocellular carcinoma (HCC) is a common malignant tumor with a complex immune evasion mechanism posing a challenge to treatment. The role of the S100A10 gene in various cancers has garnered significant attention. This study aims to elucidate the impact of S100A10 on CD8+ T cell exhaustion via the cPLA2 and 5-LOX axis, thereby elucidating its role in immune evasion in HCC. By analyzing the HCC-related data from the GEO and TCGA databases, we identified differentially expressed genes associated with lipid metabolism and developed a prognostic risk model. Subsequently, through RNA-seq and PPI analyses, we determined vital lipid metabolism genes and downstream factors S100A10, ACOT7, and SMS, which were significantly correlated with CD8+ T cell infiltration. Given the most significant expression differences, we selected S100A10 for further investigation. Both in vitro and in vivo experiments were conducted, including co-culture experiments of CD8+ T cells with MHCC97-L cells, Co-IP experiments, and validation in an HCC mouse model. S100A10 was significantly overexpressed in HCC tissues and potentially regulates CD8+ T cell exhaustion and lipid metabolism reprogramming through the cPLA2 and 5-LOX axis. Silencing S100A10 could inhibit CD8+ T cell exhaustion, further suppressing immune evasion in HCC. S100A10 may activate the cPLA2 and 5-LOX axis, initiating lipid metabolism reprogramming and upregulating LTB4 levels, thus promoting CD8+ T cell exhaustion in HCC tissues, facilitating immune evasion by HCC cells, ultimately impacting the growth and migration of HCC cells. This research highlights the critical role of S100A10 via the cPLA2 and 5-LOX axis in immune evasion in HCC, providing new theoretical foundations and potential targets for diagnosing and treating HCC.

HECTD2/TNFAIP1 Axis Regulating the p38/JNK Pathway to Promote an Inflammatory Response in Renal Cell Carcinoma Cells

BACKGROUND/AIM

The underlying processes of renal cell carcinoma (RCC), one of the deadliest malignancies of the urinary system, are still poorly understood. HECT domain E3 ubiquitin protein ligase 2 (HECTD2) is an E3 ubiquitin ligase implicated in the pulmonary inflammatory response. This study investigated the impact of HECTD2 on regulating inflammation in RCC cells and its potential mechanisms.

MATERIALS AND METHODS

HECTD2 expression in RCC tissues was examined. Immunoprecipitation and western blot (WB) analysis confirmed that HECTD2 up-regulated euchromatic histone lysine methyltransferase 2 (EHMT2) protein degradation. ChIP experiments validated tumor necrosis factor α Inducing protein 1 (TNFAIP1) as a direct target of EHMT2. qRT-PCR determined HECTD2 and TNFAIP1 expression in RCC cells. Cell viability was assayed via CCK-8. ELISA was employed to measure the expression of IL-6, TNF-α, IL-8, and IL-1β. WB analysis was conducted to test p38/JNK pathway-related protein (p38, p-p38, JNK, and p-JNK) expression.

RESULTS

HECTD2 and TNFAIP1 were significantly up-regulated in RCC patient tissues and cells. Subsequent investigations revealed that HECTD2 promoted an inflammatory response in RCC cells. Additionally, HECTD2 up-regulated TNFAIP1 expression, and high TNFAIP1 expression could reverse the repressive impact of low HECTD2 expression on the inflammatory response in RCC cells. Rescue experiments demonstrated that the addition of p38/JNK pathway inhibitors attenuated the impact of TNFAIP1 overexpression on the RCC inflammatory response.

CONCLUSION

Our findings establish a new mechanism by which HECTD2 exerts a pro-inflammatory role in RCC cells and present a prospective method for an anti-inflammatory intervention targeting the HECTD2/TNFAIP1 axis in malignancies.

Correlation between S100A7 and immune characteristics, methylation, tumor stemness and tumor heterogeneity in pan-cancer and its role in chemotherapy resistance in breast cancer

OBJECTIVE

To explore the relationships between S100A7 and the immune characteristics, tumor heterogeneity, and tumor stemness pan-cancer as well as the effect of S100A7 on chemotherapy sensitivity in breast cancer.

METHODS

TCGA-BRCA and TCGA-PANCANCER RNA-seq data and clinical follow-up survival data were collected from the University of California Santa Cruz database. Survival analyses were performed to explore the relationship between S100A7 expression and pan-cancer prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and Gene Set Enrichment Analysis (GSEA) were used to identify the potential pathways related to the differentially expressed genes in breast cancer. Spearman's and Wilcoxon's tests were used to investigate the relationships between S100A7 expression and immune characteristics, methylation, tumor heterogeneity, and tumor stemness. The potential functions of S100A7 and its influence on chemotherapy sensitivity in breast cancer were elucidated using reverse transcription-quantitative PCR, Cell Counting Kit-8 (CCK-8) assay, Transwell assay, and wound healing assay.

RESULTS

S100A7 was highly expressed in most types of tumors and was associated with poor prognosis. S100A7 was closely associated with immunomodulators, immune checkpoint and immune cell infiltration. Further, S100A7 was related to tumor mutational burden, tumor heterogeneity, methylation and tumor stemness in breast cancer. High S100A7 expression was associated with the invasiveness, migration, proliferation and chemotherapy resistance of breast cancer cells in vitro experiments.

CONCLUSION

High S100A7 expression was related with poor prognosis and chemotherapy resistance in breast cancer, making it a potential immune and chemotherapy resistance biomarker.

Humanized mouse models: A valuable platform for preclinical evaluation of human cancer

Animal models are routinely employed to assess the treatments for human cancer. However, due to significant differences in genetic backgrounds, traditional animal models are unable to meet bioresearch needs. To overcome this restriction, researchers have generated and optimized immunodeficient mice, and then engrafted human genes, cells, tissues, or organs in mice so that the responses in the model mice could provide a more reliable reference for treatments. As a bridge connecting clinical application and basic research, humanized mice are increasingly used in the preclinical evaluation of cancer treatments, particularly after gene interleukin 2 receptor gamma mutant mice were generated. Human cancer models established in humanized mice support exploration of the mechanism of cancer occurrence and provide an efficient platform for drug screening. However, it is undeniable that the further application of humanized mice still faces multiple challenges. This review summarizes the construction approaches for humanized mice and their existing limitations. We also report the latest applications of humanized mice in preclinical evaluation for the treatment of cancer and point out directions for future optimization of these models.

Clinical and Immunologic Features of Germline Pathogenic Variant-Positive Patients with Melanoma

PURPOSE

Malignant melanoma represents the most lethal skin cancer with germline predispositions thought to comprise 10% to 15% of all melanoma cases. No studies to date examine the immunologic features that may differentiate survival differences between germline pathogenic variant (gPV)-positive patients with melanoma from gPV-negative patients with melanoma.

EXPERIMENTAL DESIGN

Adult patients with melanoma and clinical characteristics suggesting hereditary predisposition to cancer were prospectively recruited to undergo germline testing and flow cytometric analysis of peripheral immune suppressor cells.

RESULTS

In this cohort, gPV-positive patients (n = 72) had a significantly improved melanoma-specific survival (MSS) compared with gPV-negative patients (n = 411; HRadj, 0.32; 95% CI, 0.13-0.82; P = 0.01). These survival improvements among gPV-positive patients were most apparent among cutaneous melanoma subtypes (HRadj, 0.12; 95% CI, 0.016-0.86; P = 0.03) and numerically improved in later-stage (IIB-IV) patients (HRadj, 0.34; 95% CI, 0.10-1.11; P = 0.06). Further, gPV-positive patients had a significantly lower level of total circulating PMN-MDSC compared with gPV-negative patients (P = 0.01), which was most apparent in those diagnosed with later stages (IIB-IV) of melanoma (P = 0.009). Finally, a significant upregulation of inflammatory transcriptome signatures in later-stage gPV-positive patients (n = 21) was observed in comparison with gPV-negative patients (n = 173) in the cutaneous melanoma cohort (SKCM) of The Cancer Genome Atlas (TCGA).

CONCLUSIONS

gPV-positive patients with melanoma exhibit improved MSS in addition to reduced peripheral PMN-MDSC and an enhanced inflammatory microenvironment.

Single-cell RNA sequencing reveals the heterogeneity of epithelial cell and fibroblast cells from non- to metastatic lymph node OTSCC

Lymph node metastasis (LNM) is one of the common features of oral tongue squamous cell carcinoma (OTSCC). LNM is also taken as a sign of advanced OTSCC and poor survival rate. Recently, single-cell RNA sequencing has been applied in investigating the heterogeneity of tumor microenvironment and discovering the potential biomarkers for helping the diagnosis and prognosticating. Pathogenesis of LNM in OTSCC remains unknown. Specifically, cancer-associated fibroblasts (CAFs) and epithelial tumor cells could foster the progression of tumors. Thus, in this study, we aimed to comprehensively analyze the roles of subpopulations of CAFs and epithelial tumor cells in lymph node metastatic OTSCC using the integration of OTSCC single-cell RNA sequencing datasets. Four distinct subtypes of CAFs, namely vascular CAFs, myofibroblast CAFs, inflammatory CAFs, and growth arrest CAFs were successfully discovered in LNM tumor and confirmed the roles of GAS and PTN pathways in the progression of tumor metastasis. In addition, NKAIN2+ epithelial cells and FN1+ epithelial cells specifically exhibited an upregulation of PTN, NRG, MIF, and SPP1 signaling pathways in the metastatic OTSCC. In doing so, we put forth some potential biomarkers that could be utilized for the purpose of diagnosing and prognosticating OTSCC during its metastatic phase and tried to confirm by immunofluorescence assays.

Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment

Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable.

S100P as a potential biomarker for immunosuppressive microenvironment in pancreatic cancer: a bioinformatics analysis and in vitro study

BACKGROUND

Immunosuppression is a significant factor contributing to the poor prognosis of cancer. S100P, a member of the S100 protein family, has been implicated in various cancers. However, its role in the tumor microenvironment (TME) of pancreatic cancer remains unclear. This study aimed to investigate the potential impact of S100P on TME characteristics in patients with pancreatic cancer.

METHODS

Multiple data (including microarray, RNA-Seq, and scRNA-Seq) were obtained from public databases. The expression pattern of S100P was comprehensively evaluated in RNA-Seq data and validated in four different microarray datasets. Prognostic value was assessed through Kaplan-Meier plotter and Cox regression analyses. Immune infiltration levels were determined using the ESTIMATE and ssGSEA algorithms and validated at the single-cell level. Spearman correlation test was used to examine the correlation between S100P expression and immune checkpoint genes, and tumor mutation burden (TMB). DNA methylation analysis was performed to investigate the change in mRNA expression. Reverse transcription PCR (RT-PCR) and immunohistochemical (IHC) were utilized to validate the expression using five cell lines and 60 pancreatic cancer tissues.

RESULTS

This study found that S100P was differentially expressed in pancreatic cancer and was associated with poor prognosis (P < 0.05). Notably, S100P exhibited a significant negative-correlation with immune cell infiltration, particularly CD8 + T cells. Furthermore, a close association between S100P and immunotherapy was observed, as it strongly correlated with TMB and the expression levels of TIGIT, HAVCR2, CTLA4, and BTLA (P < 0.05). Intriguingly, higher S100P expression demonstrated a negative correlation with methylation levels (cg14323984, cg27027375, cg14900031, cg14140379, cg25083732, cg07210669, cg26233331, and cg22266967), which were associated with CD8 + T cells. In vitro RT-PCR validated upregulated S100P expression across all five pancreatic cancer cell lines, and IHC confirmed high S100P levels in pancreatic cancer tissues (P < 0.05).

CONCLUSION

These findings suggest that S100P could serve as a promising biomarker for immunosuppressive microenvironment, which may provide a novel therapeutic way for pancreatic cancer.

Comprehensive pan-cancer analysis reveals NUSAP1 is a novel predictive biomarker for prognosis and immunotherapy response

Nucleolar and spindle-associated protein 1 (NUSAP1) is a microtubule-associated protein that plays a crucial role in mitosis. Despite initial reports suggesting a potential involvement of NUSAP1 in tumor progression and malignant cell regulation, there has been no systematic analysis of its role in the tumor immune microenvironment, nor its predictive value for prognosis and immunotherapy response across different cancer types. In this study, we analyze NUSAP1 mRNA and protein expression levels in various human normal and tumor tissues, using data from TCGA, GTEx, CPTAC, HPA databases, and clinical samples. Our findings reveal that NUSAP1 is highly expressed in multiple tumor tissues across most cancer types and is primarily expressed in malignant and immune cells, according to single-cell sequencing data from the TISCH database. Prognostic analysis based on curated survival data from the TCGA database indicates that NUSAP1 expression levels can predict clinical outcomes for 26 cancer types. Furthermore, Gene Set Enrichment Analysis (GSEA) suggests that NUSAP1 promotes cell proliferation, tumor cell invasion, and regulation of anti-tumor response. Analysis of immune score, immune cell infiltration, and anti-cancer immunity cycle using ESTIMATE, TIMER, and TIP databases show that high NUSAP1 levels are associated with low CD4+T and NKT cell infiltration but high Th2 and MDSC infiltration, inversely correlated with antigen-presenting molecules and positively correlated with a variety of immune negative regulatory molecules. Notably, patients with melanoma, lung, and kidney cancer with high NUSAP1 expression levels have shorter survival times and lower immunotherapy response rates. Using Cmap analysis, we identify Entinostat and AACOCF3 as potential inhibitors of NUSAP1-mediated pro-oncogenic effects. In vitro and in vivo experiments further confirm that NUSAP1 knockdown significantly reduces the proliferation ability of A549 and MCF-7 cells. Overall, our study highlights the potential of NUSAP1 expression as a novel biomarker for predicting prognosis and immuno-therapeutic efficacy across different human cancers and suggests its potential for developing novel antitumor drugs or improving immunotherapy.

Implications of receptor for advanced glycation end products for progression from obesity to diabetes and from diabetes to cancer

Obesity and type 2 diabetes mellitus (T2DM) are chronic pathologies with a high incidence worldwide. They share some pathological mechanisms, including hyperinsulinemia, the production and release of hormones, and hyperglycemia. The above, over time, affects other systems of the human body by causing tissue hypoxia, low-grade inflammation, and oxidative stress, which lay the pathophysiological groundwork for cancer. The leading causes of death globally are T2DM and cancer. Other main alterations of this pathological triad include the accumulation of advanced glycation end products and the release of endogenous alarmins due to cell death (i.e., damage-associated molecular patterns) such as the intracellular proteins high-mobility group box protein 1 and protein S100 that bind to the receptor for advanced glycation products (RAGE) - a multiligand receptor involved in inflammatory and metabolic and neoplastic processes. This review analyzes the latest advanced reports on the role of RAGE in the development of obesity, T2DM, and cancer, with an aim to understand the intracellular signaling mechanisms linked with cancer initiation. This review also explores inflammation, oxidative stress, hypoxia, cellular senescence, RAGE ligands, tumor microenvironment changes, and the “cancer hallmarks” of the leading tumors associated with T2DM. The assimilation of this information could aid in the development of diagnostic and therapeutic approaches to lower the morbidity and mortality associated with these diseases.

The Ephrin tyrosine kinase a3 (EphA3) is a novel mediator of RAGE-prompted motility of breast cancer cells

BACKGROUND

The receptor for advanced glycation-end products (RAGE) and its ligands have been implicated in obesity and associated inflammatory processes as well as in metabolic alterations like diabetes. In addition, RAGE-mediated signaling has been reported to contribute to the metastatic progression of breast cancer (BC), although mechanistic insights are still required. Here, we provide novel findings regarding the transcriptomic landscape and the molecular events through which RAGE may prompt aggressive features in estrogen receptor (ER)-positive BC.

METHODS

MCF7 and T47D BC cells stably overexpressing human RAGE were used as a model system to evaluate important changes like cell protrusions, migration, invasion and colony formation both in vitro through scanning electron microscopy, clonogenic, migration and invasion assays and in vivo through zebrafish xenografts experiments. The whole transcriptome of RAGE-overexpressing BC cells was screened by high-throughput RNA sequencing. Thereafter, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses allowed the prediction of potential functions of differentially expressed genes (DEGs). Flow cytometry, real time-PCR, chromatin immunoprecipitation, immunofluorescence and western blot assays were performed to investigate the molecular network involved in the regulation of a novel RAGE target gene namely EphA3. The clinical significance of EphA3 was explored in the TCGA cohort of patients through the survivALL package, whereas the pro-migratory role of EphA3 signaling was ascertained in both BC cells and cancer-associated fibroblasts (CAFs). Statistical analysis was performed by t-tests.

RESULTS

RNA-seq findings and GSEA analysis revealed that RAGE overexpression leads to a motility-related gene signature in ER-positive BC cells. Accordingly, we found that RAGE-overexpressing BC cells exhibit long filopodia-like membrane protrusions as well as an enhanced dissemination potential, as determined by the diverse experimental assays. Mechanistically, we established for the first time that EphA3 signaling may act as a physical mediator of BC cells and CAFs motility through both homotypic and heterotypic interactions.

CONCLUSIONS

Our data demonstrate that RAGE up-regulation leads to migratory ability in ER-positive BC cells. Noteworthy, our findings suggest that EphA3 may be considered as a novel RAGE target gene facilitating BC invasion and scattering from the primary tumor mass. Overall, the current results may provide useful insights for more comprehensive therapeutic approaches in BC, particularly in obese and diabetic patients that are characterized by high RAGE levels.

Spatial Proteomics for the Molecular Characterization of Breast Cancer

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

Comprehensive analysis of prognosis of cuproptosis-related oxidative stress genes in multiple myeloma

Introduction: Multiple myeloma (MM) is a highly heterogeneous hematologic malignancy. The patients’ survival outcomes vary widely. Establishing a more accurate prognostic model is necessary to improve prognostic precision and guide clinical therapy.

Methods: We developed an eight-gene model to assess the prognostic outcome of MM patients. Univariate Cox analysis, Least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses were used to identify the significant genes and construct the model. Other independent databases were used to validate the model.

Results: The results showed that the overall survival of patients in the high-risk group was signifificantly shorter compared with that of those in the low-risk group. The eight-gene model demonstrated high accuracy and reliability in predicting the prognosis of MM patients.

Discussion: Our study provides a novel prognostic model for MM patients based on cuproptosis and oxidative stress. The eight-gene model can provide valid predictions for prognosis and guide personalized clinical treatment. Further studies are needed to validate the clinical utility of the model and explore potential therapeutic targets.

Cervical cancer immune infiltration microenvironment identification, construction of immune scores, assisting patient prognosis and immunotherapy

Background

The immune microenvironment is of great significance in cervical cancer. However, there is still a lack of systematic research on the immune infiltration environment of cervical cancer.

Methods

We obtained cervical cancer transcriptome data and clinical information from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, evaluated the immune microenvironment of cervical cancer, determined immune subsets, constructed an immune cell infiltration scoring system, screened key immune-related genes, and performed single-cell data analysis and cell function analysis of key genes.

Results

We combined the TCGA and GEO data sets and obtained three different immune cell populations. We obtained two gene clusters, extracted 119 differential genes, and established an immune cell infiltration (ICI) scoring system. Finally, three key genes, IL1B, CST7, and ITGA5, were identified, and single-cell sequencing data were mined to distribute these key genes in different cell types. By up-regulating CST7 and down-regulating IL1B and ITGA5, cervical cancer cells’ proliferation ability and invasion ability were successfully reduced.

Conclusion

We conducted a comprehensive assessment of the state of the tumor immune microenvironment in cervical cancer, constructed the ICI scoring system, and identified the ICI scoring system as a potential indicator of susceptibility to immunotherapy for cervical cancer, identifying key genes suggesting that IL1B, CST7, and ITGA5 play an essential role in cervical cancer.

Identification of Immune-Related Subtypes and Construction of a Novel Prognostic Model for Bladder Urothelial Cancer

The purpose of this study was to explore the relationship between bladder urothelial cancer (BLCA) and immunity, to screen prognosis-related immune genes (PIGs), and to construct an immune-related prognosis model (IRPM). We processed the relevant data of The Cancer Genome Atlas (TCGA-BLCA) and GSE13507 using R software and Perl. We divided BLCA into high-immunity and low-immunity subtypes. There were significant differences in the two subtypes. In addition, we identified 13 PIGs of BLCA by jointly analyzing the gene expression data and survival information of GSE13507 and TCGA-BLCA, and constructed IRPM through nine of them. The low-risk group had better survival outcome than the high-risk group. We also constructed a nomogram based on clinicopathological information and risk scores of the patients. Moreover, the prognosis of BLCA patients was significantly impacted by the expression of almost every gene used to calculate the risk score. The result of real-time fluorescence quantitative polymerase chain reaction revealed that all the genes used to calculate the risk score were differentially expressed between BLCA and adjacent normal tissues, except PDGFRA. Our research provided potential targets for the treatment of BLCA and a reference for judging the prognosis of BLCA.

Machine Learning Assistants Construct Oxidative Stress-Related Gene Signature and Discover Potential Therapy Targets for Acute Myeloid Leukemia

Background

Oxidative stress (OS) is associated with the development of acute myeloid leukemia (AML). However, there is lack of relevant research to confirm that OS-related genes can guide patients in risk stratification and predict their survival probability.

Method

First, we Data from three public databases, respectively. Then, we use batch univariate Cox regression and machine learning to select important characteristic genes; next, we build the model and use receiver operating characteristic curve (ROC) to evaluate the accuracy. Moreover, GSEAs were performed to discover the molecular mechanism and conduct nomogram visualization. In addition, the relative importance value was used to identify the hub gene, and GSE9476 was to validate hub gene difference expression. Finally, we use symptom mapping to predict the candidate herbs, targeting the hub gene, and put these candidate herbs into Traditional Chinese Medicine Systems Pharmacology (TCMSP) to identify the main small molecular ingredients and then docking hub proteins with this small molecular.

Results

A total of 313 candidate oxidative stress-related genes could affect patients' outcomes and machine learning to select six potential genes to construct a gene signature model to predict the overall survival (OS) of AML patients. Patients in a high group will obtain a short survival time when compared with the low-risk group (HR = 3.97, 95% CI: 2.48-6.36; p < 0.001). ROC results demonstrate the model has better prediction efficiency with AUC 0.873. GSEA suggests that this gene is enriched in several important signaling pathways. Nomogram is constructed and is robust. PLA2G4A is a hub gene of signature and associated with prognosis, and Nobiletin could target PLA2G4A for therapy AML.

Conclusion

We use two different machine learning methods to build six oxidative stress-related gene signatures that could assist clinical decisions and identify PLA2G4A as a potential biomarker for AML. Nobiletin, targeting PLA2G4, may provide a third pathway for therapy AML.

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Simple Summary

Cancer is the leading cause of death worldwide, and the overall aging of the population results in an increased risk of a cancer diagnosis during a person’s lifetime. Diagnosis and treatment at an early stage will typically increase the chances of survival. Tumors can develop therapy resistance, and it is difficult to predict how individual patients will respond to therapy. Most studies that aim to resolve this problem have focused on studying the composition and characteristics of dissociated tumors, while ignoring the role of cell localization and interactions within the tumor microenvironment. In the past decade, technological innovations have enabled multiplex imaging analyses of intact tumors to study localization and interaction parameters, which can be used as biomarkers, or can be correlated with treatment responses and clinical outcomes.

Abstract

The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide an overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications.

Focal Adhesion Kinase (FAK)-Hippo/YAP transduction signaling mediates the stimulatory effects exerted by S100A8/A9-RAGE system in triple-negative breast cancer (TNBC)

BACKGROUND

Understanding the intricate signaling network involved in triple-negative breast cancer (TNBC) represents a challenge for developing novel therapeutic approaches. Here, we aim to provide novel mechanistic insights on the function of the S100A8/A9-RAGE system in TNBC.

METHODS

TNM plot analyzer, Kaplan-Meier plotter, Meta-analysis, GEPIA2 and GOBO publicly available datasets were used to evaluate the clinical significance of S100A8/A9 and expression levels of S100A8/A9, RAGE and Filamin family members in breast cancer (BC) subtypes. METABRIC database and Cox proportional hazard model defined the clinical impact of high RAGE expression in BC patients. Multiple bioinformatics programs identified the main enriched pathways within high RAGE expression BC cohorts. By lentiviral system, TNBC cells were engineered to overexpress RAGE. Western blotting, immunofluorescence, nucleus/cytoplasm fractionation, qRT-PCR, gene silencing and luciferase experiments were performed to identify signal transduction mediators engaged by RAGE upon stimulation with S100A8/A9 in TNBC cells. Proliferation, colony formation and transwell migration assays were carried out to evaluate the growth and migratory capacity of TNBC cells. Statistical analysis was performed by ANOVA and independent t-tests.

RESULTS

We found a remarkable high expression of S100A8 and S100A9 in BC, particularly in HER2-positive and TNBC, with the latter associated to worst clinical outcomes. In addition, high RAGE expression correlated with a poor overall survival in BC. Next, we determined that the S100A8/A9-RAGE system triggers FAK activation by engaging a cytoskeleton mechanosensing complex in TNBC cells. Through bioinformatics analysis, we identified the Hippo pathway as the most enriched in BC patients expressing high RAGE levels. In accordance with these data, we demonstrated the involvement of S100A8/A9-RAGE-FAK signaling in the control of Hippo/YAP activities, and we established the crucial contribution of RAGE-FAK-YAP circuitry in the growth and migratory effects initiated by S100A8/A9 in TNBC cells.

CONCLUSIONS

The present study provides novel mechanistic insights on RAGE actions in TNBC. Moreover, our findings suggest that RAGE-FAK-YAP transduction pathway could be exploited as a druggable system halting the aggressive TNBC subtype.