An M0 macrophage-related prognostic model for hepatocellular carcinoma

Ubiquitin-conjugating enzyme E2S (UBE2S) as a prognostic biomarker and regulator of tumorigenesis in osteosarcoma

Ubiquitin-conjugating enzyme E2S (UBE2S) is a member of ubiquitin conjugating enzymes with unclear association with osteosarcoma (OS). This study aimed to assess UBE2S's predictive value in OS using data from TCGA and GEO databases. Kaplan-Meier survival analysis and ROC curves were used for prognostic evaluation, and a nomogram was developed for prognostic prediction. Potential biological functions, pathways, and correlations with tumor immune microenvironment, immunotherapy response, and drug sensitivity were analyzed. UBE2S overexpression was linked to poor prognosis, and the nomogram effectively predicted OS survival outcomes. UBE2S was found to impact tumorigenesis pathways, immune landscape, and treatment sensitivity in OS. Transcriptome sequencing, RT-qPCR, Western Blotting, and immunohistochemistry confirmed that UBE2S is abnormally overexpressed in OS. Additionally, a series of in vitro experiments showed that UBE2S knockdown reduced OS cell proliferation and migration while promoting apoptosis. In vivo experiments also confirmed that UBE2S knockdown could inhibit OS cell growth. In summary, our research demonstrates that UBE2S is a reliable prognostic factor for OS. Its abnormal overexpression enhances OS proliferation and migration, indicating its significance for future personalized treatment strategies in OS.

Metabolic-associated fatty liver disease (MAFLD) promotes the progression of hepatocellular carcinoma by enhancing KIF20A expression

BACKGROUND

Compared to other HCC, those related to MAFLD exhibit distinct prognostic differences. This article aims to elucidate the impact of MAFLD on HCC prognosis through the lens of KIF20A, thereby providing a theoretical foundation for targeted therapies in MAFLD-related HCC.

METHODS

We employed the Weighted gene co-expression network analysis (WGCNA) method alongside the Mime package to identify key genes associated with MAFLD-related HCC. Subsequently, we utilized OCLR and CytoTRACE algorithms to evaluate the relationship between these genes and HCC stemness. The R package was employed to conduct immunological analyses on both mRNA sequencing and single-cell data. We validated the effects of core genes on HCC through experimental approaches, including cell culture, Transwell assays, Western Blot, and proliferation assays. Finally, we predicted potential therapeutic drugs using the OncoPredict software package.

RESULTS

WGCNA identified the cyan module associated with MAFLD in GSE135251 and the blue module linked to HCC in TCGA. Further analysis identified KIF20A as the core gene in MAFLD-related HCC. Utilizing the OCLR and CytoTRACE algorithms, KIF20A was found to correlate with mRNA stemness index (mRNAsi). Analysis of public databases revealed that KIF20A promotes immune tolerance through the SPP1-CD44 pathway and drives HCC progression via the G2M checkpoint. Experimental results demonstrated that lipotoxic damage in HCC cells and small extracellular vesicles (sEVs) derived from these cells upregulate KIF20A, thereby accelerating HCC progression. Finally, OncoPredict and AutoDock were employed to predict drugs targeting KIF20A.

CONCLUSION

MAFLD-related HCC can elevate KIF20A levels and promote tumor proliferation and migration.

Machine learning-based identification of telomere-related gene signatures for prognosis and immunotherapy response in hepatocellular carcinoma

Telomere in cancers shows a main impact on maintaining chromosomal stability and unlimited proliferative capacity of tumor cells to promote cancer development and progression. So, we targeted to detect telomere-related genes(TRGs) in hepatocellular carcinoma (HCC) to develop a novel predictive maker and response to immunotherapy. We sourced clinical data and gene expression datasets of HCC patients from databases including TCGA and GEO database. The TelNet database was utilized to identify genes associated with telomeres. Genes with altered expression from TCGA and GSE14520 were intersected with TRGs, and Cox regression analysis was conducted to pinpoint genes strongly linked to survival prognosis. The risk model was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression technique. Subsequently, evaluation of the risk model focused on immune cell infiltration, checkpoint genes, drug responsiveness, and immunotherapy outcomes across both high- and low-risk patient groups. We obtained 25 TRGs from the overlapping set of 34 genes using Cox regression analysis. Finally, six TRGs (CDC20, TRIP13, EZH2, AKR1B10, ESR1, and DNAJC6) were identified to formulate the risk score (RS) model, which independently predicted prognosis for HCC. The high-risk group demonstrated worse survival outcomes and showed elevated levels of infiltration by Macrophages M0 and Tregs. Furthermore, a notable correlation was observed between the genes in the risk model and immune checkpoint genes. The RS model, derived from TRGs, has been validated for its predictive value in immunotherapy outcomes. In conclusion, this model not only predicted the prognosis of HCC patients but also their immune responses, providing innovative strategies for cancer therapy.

Integrated transcriptomic and metabolomic analysis reveals the effects of EMMPRIN on nucleotide metabolism and 1C metabolism in AS mouse BMDMs

Background

Extracellular matrix metalloproteinase inducer (EMMPRIN) has been considered as a key promoting factor in atherosclerosis (AS). Some studies have shown that regulating EMMPRIN expression in bone marrow-derived macrophages (BMDMs) of ApoE-/- mice can affect plaque stability, but the mechanism was not clear.

Methods

AS model mice were built from high-fat-feeding ApoE -/- mice, and were divided into siE group and CON group. The BMDMs and aortas from AS mice were harvested following in vivo treatment with either EMMPRIN short interfering (si)RNA (siEMMPRIN) or negative control siRNA. Transcriptomic and metabolomic profiles were analyzed using RNA-sequencing and Liquid chromatography-tandem mass spectrometry (LC-MS/MS), respectively. The efficacy of siEMMPRIN was assessed through real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting (WB). Immunofluorescence staining was employed to measure EMMPRIN expression within aortic atherosclerotic plaques. Cell proliferation was monitored using the Cell Counting Kit-8 (CCK8), while flow cytometry was utilized to analyze the cell cycle. Additionally, seahorse analysis and oil red O staining were conducted to verify glucose and lipid metabolism, respectively.

Results

A total of 3,282 differentially expressed metabolites (DEMs) and 16,138 differentially expressed genes (DEGs) were identified between the CON group and siE group. The nucleotide metabolism and one-carbon (1C) metabolism were identified as major altered pathways at both the transcriptional and metabolic levels. Metabolomic results identified increased levels of glycine, serine, betaine and S-adenosyl-L-methionine (SAM) to S-adenosyl-L-homocysteine (SAH) ratio and decreased levels of dimethylglycine (DMG) and SAH in 1C metabolism, accompanied by the accumulation of nucleotides, nucleosides, and bases in nucleotide metabolism. Transcriptomics results shown that Dnmt, Mthfd2 and Dhfr were downregulated, while Mthfr were upregulated in 1C metabolism. And numerous genes involved in de novo nucleotide synthesis, pentose phosphate pathway (PPP) and dNTP production were significantly inhibited, which may be associated with decreased BMDMs proliferation and cell cycle arrest in the G0/G1 phase in siE group. Multi-omics results also showed changes in glucose and lipid metabolism. Seahorse assay confirmed reduced glycolysis and oxidative phosphorylation (OXPHOS) levels and the Oil Red O staining confirmed the decrease of lipid droplets in siE group.

Conclusion

The integrated metabolomic and transcriptomic analysis suggested that nucleotide metabolism and 1C metabolism may be major metabolic pathways affected by siEMMPRIN in AS mouse BMDMs. Our study contributes to a better understanding of the role of EMMPRIN in AS development.

Novel biomarker in hepatocellular carcinoma: Stearoyl-CoA desaturase 1

BACKGROUND

In recent years, more and more studies have shown that reprogramming lipid metabolism plays an important role in the occurrence and development of hepatocellular carcinoma (HCC). However, there is a lack of systematic exploration of fatty acid (FA) profiles in HCC.

AIMS

This study aims to systematically investigate the FA profile in HCC and assess the diagnostic potential of stearoyl-CoA desaturase 1 (SCD1) as a biomarker for HCC.

METHODS

The FA profile in HCC tissues was detected by gas chromatography mass spectrometry. Abnormal FA metabolism was analyzed by qRT-PCR, Western blot. Immunohistochemical and bioinformatics analysis were used to analyze SCD1 expression and function. Receiver operating characteristic curves were used to analyze the diagnostic efficacy of SCD1, and the relationship between SCD1 and immune infiltration in HCC was analyzed by the biological information method.

RESULTS

FAs were found to accumulate in the HCC samples, and abnormal FA metabolism in HCC related to the upregulation of the expression and activity of SCD1. The combination of SCD1 and alpha-fetoprotein produced a greater area under the receiver operating characteristic curve (0.925, P < 0.001) than SCD1 or alpha-fetoprotein alone. It also showed better sensitivity (77.5 %). Besides, high SCD1 expression was found to be related to immune infiltration in HCC.

CONCLUSION

SCD1 can serve as a reliable biomarker for HCC diagnosis.

Identification of potential diagnostic targets and therapeutic strategies for anoikis-related biomarkers in lung squamous cell carcinoma using machine learning and computational virtual screening

Objective

Lung squamous cell carcinoma (LUSC) is a common subtype of non-small cell lung cancer (NSCLC) characterized by high invasiveness, high metastatic potential, and drug resistance, resulting in poor patient prognosis. Anoikis, a specific form of apoptosis triggered by cell detachment from the extracellular matrix (ECM), plays a crucial role in tumor metastasis. Resistance to anoikis is a key mechanism by which cancer cells acquire metastatic potential. Although several studies have identified biomarkers related to LUSC, the role of anoikis-related genes (ARGs) remains largely unexplored.

Methods

Anoikis-related genes were obtained from the Harmonizome and GeneCards databases, and 222 differentially expressed genes (DEGs) in LUSC were identified via differential expression analysis. Univariate Cox regression analysis identified 74 ARGs significantly associated with survival, and a prognostic model comprising 8 ARGs was developed using LASSO and multivariate Cox regression analyses. The model was internally validated using receiver operating characteristic (ROC) curves and Kaplan-Meier (K-M) survival curves. Differences in immune cell infiltration and gene expression between high- and low-risk groups were analyzed. Virtual drug screening and molecular dynamics simulations were performed to evaluate the therapeutic potential of CSNK2A1, a key gene in the model. Finally, in vitro experiments were conducted to validate the therapeutic effects of the identified drug on LUSC.

Results

The 8-gene prognostic model demonstrated excellent predictive performance and stability. Significant differences in immune cell infiltration and immune microenvironment characteristics were observed between the high- and low-risk groups, suggesting the critical role of ARGs in shaping the immune landscape of LUSC. Virtual drug screening identified Dihydroergotamine as having the highest binding affinity for CSNK2A1. Molecular dynamics simulations confirmed that the CSNK2A1-Dihydroergotamine complex exhibited strong binding stability. Further in vitro experiments demonstrated that Dihydroergotamine significantly inhibited LUSC cell viability, migration, and invasion, and downregulated CSNK2A1 expression.

Conclusion

This study is the first to construct an anoikis-related prognostic model for LUSC, highlighting its role in the tumor immune microenvironment and providing insights into personalized therapy. Dihydroergotamine exhibited significant anti-LUSC activity and holds promise as a potential therapeutic agent. CSNK2A1 emerged as a robust candidate for early diagnosis and a therapeutic target in LUSC.

Reprogramming Macrophage Phenotypes With Photobiomodulation for Improved Inflammation Control in ENT Organ Tissues

Photobiomodulation (PBM), a noninvasive phototherapy that utilizes wavelengths between red and near-infrared light, has emerged as a promising approach for controlling inflammation by modulating macrophage polarization. This review investigates the therapeutic potential of PBM in treating ENT-specific inflammatory conditions, such as chronic rhinosinusitis and otitis media, focusing on its effects on macrophage phenotypes and evidence from preclinical studies. By promoting mitochondrial activity, increasing adenosine triphosphate production, and modulating reactive oxygen species, PBM has been shown to shift macrophages from a pro-inflammatory to an anti-inflammatory phenotype. Studies have demonstrated that PBM enhances tissue repair, reduces inflammatory markers, and promotes wound healing. Moreover, PBM facilitates the polarization of M2 macrophages, a crucial factor in resolving mucosal inflammation in the nasal, pharyngeal, and middle ear cavities, as well as restoring tissue homeostasis. The anti-inflammatory effects of PBM are attributed to its ability to influence several molecular mechanisms involved in inflammation regulation, particularly in ENT organ tissues, where recurrent inflammation can lead to chronic conditions such as otitis media or sinusitis. Furthermore, this review compares PBM to competing methods for reprogramming macrophages and treating inflammation, highlighting its advantages of minimal toxicity, simplicity, and precision in controlling ENT immune responses.

T cell receptor signaling pathway subgroups and construction of a novel prognostic model in osteosarcoma

Background

T cell receptor (TCR) signaling pathway is closely related to tumor progress and immunotherapy. This study aimed to explore the clinical significance, prognosis, immune infiltration and chemotherapy sensitivity of TCR in osteosarcoma (OS).

Material and methods

OS data were obtained from TARGET and GEO database. TCR signaling pathway-related genes (TCRGs) were extracted from Molecular Signatures Database. Unsupervised non-negative matrix factorization clustering analysis was used to identify OS molecular subtypes. Differential expressed TCRGs between molecular subtypes were screened with univariate Cox regression, LASSO regression and multivariate Cox regression. Subsequently, an OS-associated prognostic model was constructed and validated. Nomogram was established and verified. Immune landscape analysis including immune infiltration analysis, ESTIMATE algorithm and immune checkpoints expression levels of molecular subtypes and different risk groups were analyzed. Finally, chemotherapy sensitivity and potential therapeutic agents between different risk groups was identified.

Results

Two TCRGs related subclusters were identified. Two hundred and seventy-two Differential expressed TCRGs were screened between two subclusters. A robust prognostic model were constructed. High and low risk groups were stratified. Low risk group showed higher ESTIMATE, immune and stromal scores, while high risk group exhibited higher tumor purity and the lower expression levels of immune checkpoints. A nomogram comprising metastasis and risk score was successfully built. The sensitivity to chemotherapy agents were different across high and low risk groups.

Conclusions

Our study proposed TCR related molecular subtypes and provided a prognostic model for OS. Our findings may bring a new insight into the immunotherapy for OS patients.

Exploring extrahepatic metastasis of hepatocellular carcinoma based on methylation driver genes and establishing a prognostic model for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC), theseventh most common cancer worldwide, is characterized by a high mortality rate, advanced diagnosis, and susceptibility to extrahepatic metastasis. Numerous studies have shown that DNA methylation is a crucial factor in epigenetic modifications and regulation of carcinogenesis.

METHODS

HCC patient data were sourced from the TCGA dataset as a training set, while GSE116174 was used as an external validation set for verification. Differential methylation and expression analyses were performed on HCC samples with and without extrahepatic metastasis. In the intersecting genes, the relationship between methylation and expression levels of the intersecting genes was analyzed. Genes with a correlation coefficient≥|0.30| and P<0.05 were identified as methylation driver genes. Cox regression analysis was conducted to identify genes associated with HCC prognosis and establish a risk score. Subsequently, a prognostic model was established and validated using Cox regression analysis incorporating the risk score and other clinical factors. Using immunohistochemistry to evaluate the expression of DHX58 and EIF5A2 in HCC tissues with and without extrahepatic metastasis. Immunoinfiltration analysis was performed on the HCC samples using CIBERSORT.

RESULTS

Our research identified eight methylation driver genes for HCC extrahepatic metastasis, of which two genes (DHX58 and EIF5A2) were associated with HCC patient prognosis. And the study further constructed and validated the risk score and prognostic model. Immunoinfiltration analysis showed that M0 macrophage abundance was correlated with the prognosis of HCC patients. Immunohistochemistry revealed differences in DHX58 and EIF5A2 expression between HCC tissues with and without extrahepatic metastasis, consistent with our bioinformatics findings.

TMEM101 expression and its impact on immune cell infiltration and prognosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a cancer caused by inflammation, which affects the immune response and treatment outcomes. Finding new immune-related targets could improve HCC immunotherapy. New research suggests that TMEM family proteins can act as either tumor suppressors or oncogenes, but the role of TMEM101 in HCC development is unclear. This study conducted an analysis of TMEM101 mRNA expression and its correlation with clinical outcomes in HCC patients using RNA sequencing data from various open databases. Additionally, differences in TMEM101 expression in HCC cell lines and HCC tissue microarrays were examined using RT-qPCR, western blotting, and in situ hybridization staining. The findings presented herein offer initial evidence indicating a significant upregulation of TMEM101 mRNA expression in HCC, which is linked to a poorer prognosis. Furthermore, TMEM101 expression was found to be positively associated with the histological grade and clinical stage of HCC patients. Moreover, a notable reduction in promoter methylation of TMEM101 was observed in HCC patients. Cox regression analysis indicated that TMEM101 was an independent prognostic factor for overall survival (OS) in HCC patients. A nomogram incorporating TMEM101 and tumor stage was constructed and assessed. Comparative analysis with four established HCC diagnostic biomarkers (AFP, EFNA3, MDK, and SMYD5) using ROC curve and time-dependent ROC curves demonstrated the diagnostic potential of TMEM101 in HCC. Gene set enrichment analysis (GSEA) revealed a correlation between TMEM101 and the cell cycle, DNA replication, and repair signaling pathways, which were differentially enriched in the TMEM101 high expression phenotype. The findings from CIBERSORT analysis suggest that TMEM101's pro-tumor effect may be due to decreasing the number of anti-tumor immune cells (M1 macrophages and resting memory CD4+ T cells) and promoting M0 macrophage infiltration in the tumor microenvironment (TME). Overall, our study indicates that TMEM101 could serve as a promising diagnostic and prognostic biomarker for HCC.

Development of a Prognostic Risk Model for Esophageal Cancer Based on M0 Macrophage-Related Genes

Background

This study investigates the prognostic value of M0 macrophage-related genes (M0MRGs) in esophageal cancer (ESCA) and identifies novel targets for immunotherapy.

Methods

Differentially expressed genes (DEGs) were screened with ESCA-related expression profile data (GSE5364 and GSE17351) from the GEO database, followed by GO and KEGG pathway enrichment analyses. Then, immune cell infiltration was examined with the CIBERSORT algorithm and multiplex fluorescence-based immunohistochemistry (MP-IHC). ESCA-related gene expression data and relevant clinical information were retrieved from TCGA. M0MRGs were identified with TCGA-ESCA based on Spearman's correlation coefficient. Additionally, LASSO and Cox regression analyses were conducted to further construct an M0MRG-related prognostic model. ATP6V0D2 and MMP12 expression in ESCA was analyzed with tissue microarray. Finally, the half maximal inhibitory concentrations (IC50) of commonly used chemotherapeutics in TCGA-ESCA were calculated with the "oncoPredict" R package.

Conclusion

In summary, ATP6V0D2 and MMP12 were crucial components in a prognostic risk model for ESCA and were associated with poor prognoses, implicating the involvement of elevated M0 macrophages in disease progression and providing potential therapeutic targets and strategies for ESCA.

Development of molecular sensors based on fluorescent proteins for polarized macrophages identification

Macrophages are a type of white blood cells that play key roles in innate immune responses as a part of cellular immunity for host defence and tissue homeostasis. To perform diverse functions, macrophages show high plasticity by transforming to polarized states. They are mainly identified as unpolarized, pro-inflammatory and antiinflammatory states and termed as M0, M1 and M2 macrophages respectively. Discriminating polarized states is important due to strict implication with inflammatory conditions resulting in many diseases as chronic inflammation, neurodegeneration, and cancer etc. Many polarization protein markers have been identified and applied to investigate expression profiles through PCR and other techniques with antibodies. However, they are time and cost consuming and sometimes show insufficient performances. We focused on the mannose receptor (CD206) as representative marker of M2 macrophage recognising terminal mannose. We developed dose dependent mannosylated fluorescent proteins (FPs) by conjugations with mannose derivative for around 20 modifiable sites on FPs surfaces. Maximum modifications did not spoil various features of FPs. We found further sensitive and specific discriminations among M2, M1 and M0 macrophages after treating polarized macrophages with adequately conditioned FPs compared to already established approaches using anti CD206 antibody through flow cytometric analysis. These results might be derived from direct ligand utilizations and increased avidity due to multivalent bindings with abundantly modified multimeric FPs. Our strategy is simple but addresses disadvantages of preceding methods. Moreover, this strategy is applicable to detect other cell surface receptors as FPs can be modified with ligands or recognizable aptamer like molecules.

Multi-Algorithm-Integrated Tertiary Lymphoid Structure Gene Signature for Immune Landscape Characterization and Prognosis in Colorectal Cancer Patients

PURPOSE

Colorectal cancer (CRC) is a common malignancy with a low survival rate as well as a low response rate to immunotherapy. This study aims to develop a risk model based on tertiary lymphoid structure (TLS)-associated gene signatures to enhance predictions of prognosis and immunotherapy response.

METHODS

TLS-associated gene data were obtained from TCGA-CRC and GEO cohorts. A comprehensive analysis using univariate Cox regression identified TLS-associated genes with significant prognostic implications. Subsequently, multiple algorithms were employed to select the most influential genes, and a stepwise Cox regression model was constructed. The model's predictive performance was validated using independent datasets (GSE39582, GSE17536, and GSE38832). To further investigate the immune microenvironment, immune cell infiltration in high-risk (HRG) and low-risk (LRG) groups was assessed using the CIBERSORT and ssGSEA algorithms. Additionally, we evaluated the model's potential to predict immune checkpoint blockade therapy response using data from The Cancer Imaging Archive, the TIDE algorithm, and external immunotherapy cohorts (GSE35640, GSE78200, and PRJEB23709). Immunohistochemistry (IHC) was employed to characterize TLS presence and CCL2 gene expression.

RESULTS

A three-gene (CCL2, PDCD1, and ICOS) TLS-associated model was identified as strongly associated with prognosis and demonstrated predictive power for CRC patient outcomes and immunotherapy efficacy. Notably, patients in the low-risk group (LRG) had a higher overall survival rate as well as a higher re-response rate to immunotherapy compared to the high-risk group (HRG). Finally, IHC results confirmed significantly elevated CCL2 expression in the TLS regions.

CONCLUSIONS

The multi-algorithm-integrated model demonstrated robust performance in predicting patient prognosis and immunotherapy response, offering a novel perspective for assessing immunotherapy efficacy. CCL2 may function as a TLS modulator and holds potential as a therapeutic target in CRC.

Comparative Analysis on Polyphenolic Composition of Different Olive Mill Wastewater and Related Extra Virgin Olive Oil Extracts and Evaluation of Nutraceutical Properties by Cell-Based Studies

This study reports a comparative analysis of the polyphenolic composition and nutraceutical properties of different olive mill wastewater (OMWW) and corresponding extra virgin olive oil (EVOO) extracts. Specifically, four OMWWs and corresponding EVOOs from cultivars Frantoio (A) and Leccino (B) obtained from different crushing seasons (early-stage (A1 and B1) and later-stage (A2 and B2)) were analyzed. Employing HPLC-DAD and LC-MS methods, the primary polyphenol content was identified and quantified. Overall, OMWW extracts showed a greater polyphenolic content compared to corresponding EVOO extracts, with OMWW B1 displaying the highest levels of polyphenols. The antiradical properties of extracts towards radical species (DPPH, ABTS, O2-, and HOCl-) were demonstrated in vitro, revealing a correlation with polyphenolic content. In fact, OMWW B1 and B2 demonstrated the strongest antiradical activity. Exploring nutraceutical properties of OMWWs, the intestinal permeation of the main polyphenols in a co-culture model (Caco-2 and HT29-MTX cell lines) was assessed, with tyrosol achieving a permeation of almost 60%. Furthermore, the involvement in the inflammation process has been evaluated in cell studies on THP1-derived macrophages by immunocytochemistry, demonstrating that OMWW B1 may exert an anti-inflammatory effect by modulating specific phenotype expression on macrophages. In conclusion, this study provides evidence supporting the reuse of OMWWs as a source of polyphenols with nutraceutical properties.

Establish a novel immune-related gene prognostic risk index (IRGPRI) associated with CD8+ cytotoxic T lymphocytes in non-small-cell lung cancer (NSCLC)

Background

The aim of this study is to create an index called IRGPRI (immune-related gene prognostic risk index) that can be utilized for predicting the prognosis and assessing the efficacy of immune checkpoint inhibitors (ICIs) therapy in patients with non-small-cell lung cancer (NSCLC).

Methods

Distinguishing gene expression patterns (DEGs) were detected in CD8+ cytotoxic T lymphocytes (CTLs) compared to other cellular types such as CD4 T cells, B cells, plasma cells, and CD8 Tex using the advanced technology of Single-cell RNA Sequencing (scRNA-seq). The construction of IRGPRI was accomplished by employing LASSO Cox regression analysis. We conducted a comparative analysis on clinical characteristics and molecular features, such as pathway enrichment and gene mutation, among the distinct subgroups of IRGPRI. Furthermore, we explored the correlation between immunological characteristics and IRGPRI subgroups to comprehensively assess the effectiveness of ICIs in NSCLC patients.

Results

A total of 109 genes were identified by intersecting immune-related genes with DEGs obtained from single-cell RNA sequencing data (GSE131907), specifically comparing CTLs to other cell types. From these, we selected 7 prognosis-related genes, namely TRBC1, HLA-DMA, CTSH, RAC1, CTSL, ANXA2, and CEBPB. These genes were used to construct the IRGPRI. The prognosis of patients diagnosed with NSCLC was found to be significantly better in the low-risk group compared to the high-risk group, as demonstrated by Kaplan-Meier (K-M) survival analysis. This observation was further confirmed through the utilization of data from the GEO cohort. The low-risk group demonstrated an increase in pathways linked with immune response, whereas the high-risk group exhibited a higher prevalence of pathways related to cancer. Furthermore, it was noted in the TCGA cohort that there existed a significant rise in the mutation frequency of every gene within the high-risk group as opposed to the low-risk group. Missense variation emerged as the most prevalent form of mutation. According to the analysis of immune cell infiltration and function, the comprehensive findings suggest that the group with a low risk is characterized by an increased presence of plasma cells, CTLs, T cells follicular helper, Tregs, and Dendritic cell resting. Additionally, they exhibit a higher score in terms of immune function for B cells, CD8+ T cells, checkpoint activity, T cell inhibition and stimulation. Moreover, this low-risk group demonstrates greater efficacy when treated with ICIs therapy compared to the high-risk group.

Conclusions

Our research effectively developed and verified a unique IRGPRI, showcasing its association with immune-related characteristics. As a result, the potential of IRGPRI as a valuable biomarker for predicting prognosis and evaluating the effectiveness of ICIs treatment in cancer is evident.

A disulfidptosis-related lncRNA signature for analyzing tumor microenvironment and clinical prognosis in hepatocellular carcinoma

Introduction

Disulfidptosis is a recently identified form of non-apoptotic programmed cell death which distinguishes itself from classical cell death pathways. However, the prognostic implications of disulfidptosis-related long non-coding RNAs (DRLs) and their underlying mechanisms in hepatocellular carcinoma (HCC) remain largely unexplored.

Methods

In this study, we leveraged RNA-sequencing data and clinical information of HCC patients from the TCGA database. Through expression correlation and prognostic correlation analyses, we identified a set of top-performing long non-coding RNAs. Subsequently, a 5-DRLs predictive signature was established by conducting a Lasso regression analysis.

Results

This signature effectively stratified patients into high- and low-risk groups, revealing notable differences in survival outcomes. Further validation through univariate and multivariate Cox regression analyses confirmed that the risk score derived from our signature independently predicted the prognosis of HCC patients. Moreover, we observed significant disparities in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups, shedding light on the potential connection between immune-related mechanisms and disulfidptosis. Notably, the signature also exhibited predictive value in the context of chemotherapeutic drug sensitivity and immunotherapy efficacy for HCC patients. Finally, we performed experimental validation at both cellular and patient levels and successfully induced a disulfidptosis phenotype in HCC cells.

Discussion

In general, this multifaceted approach provides a comprehensive overview of DRLs profiles in HCC, culminating in the establishment of a novel risk signature that holds promise for predicting prognosis and therapy outcomes of HCC patients.

The cuproptosis-related gene UBE2D2 functions as an immunotherapeutic and prognostic biomarker in pan-cancer

BACKGROUND

Cuproptosis, as a unique modality of regulated cell death, requires the involvement of ubiquitin-binding enzyme UBE2D2. However, the prognostic and immunotherapeutic values of UBE2D2 in pan-cancer remain largely unknown.

METHODS

Using UCSC Xena, TIMER, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA) databases, we aimed to explore the differential expression pattern of UBE2D2 across multiple cancer types and to evaluate its association with patient prognosis, clinical features, and genetic variations. The association between UBE2D2 and immunotherapy response was assessed by gene set enrichment analysis, tumor microenvironment, immune gene co-expression and drug half maximal inhibitory concentration (IC50) analysis.

RESULTS

The mRNA and protein levels of UBE2D2 were markedly elevated in most cancer types, and UBE2D2 exhibited prognostic significance in liver hepatocellular carcinoma (LIHC), kidney chromophobe (KICH), uveal melanomas (UVM), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), and kidney renal papillary cell carcinoma (KIRP). UBE2D2 expression was correlated with clinical features, tumor mutation burden, microsatellite instability, and anti-tumor drug resistance in several tumor types. Gene enrichment analysis showed that UBE2D2 was significantly associated with immune-related pathways. The expression level of UBE2D2 was correlated with immune cell infiltration, including CD4 + T cells、Macrophages M2、CD8 + T cells in pan-cancer. PDCD1, CD274 and CTLA4 expression levels were positively correlated with UBE2D2 level in multiple cancers.

CONCLUSIONS

We comprehensively investigated the potential value of UBE2D2 as a prognostic and immunotherapeutic predictor for pan-cancer, providing a novel insight for cancer immunotherapy.

Activated THP-1 Macrophage-Derived Factors Increase the Cytokine, Fractalkine, and EGF Secretions, the Invasion-Related MMP Production, and Antioxidant Activity of HEC-1A Endometrium Cells

Endometrium receptivity is a multifactor-regulated process involving progesterone receptor-regulated signaling, cytokines and chemokines, and additional growth regulatory factors. In the female reproductive system, macrophages have distinct roles in the regulation of receptivity, embryo implantation, immune tolerance, and angiogenesis or oxidative stress. In the present study, we investigated the effects of PMA-activated THP-1 macrophages on the receptivity-related genes, cytokines and chemokines, growth regulators, and oxidative stress-related molecules of HEC-1A endometrium cells. We established a non-contact co-culture in which the culture medium of the PMA-activated macrophages exhibiting the pro-inflammatory phenotype was used for the treatment of the endometrial cells. In the endometrium cells, the expression of the growth-related factors activin and bone morphogenetic protein 2, the growth hormone EGF, and the activation of the downstream signaling molecules pERK1/2 and pAkt were analyzed by ELISA and Western blot. The secretions of cytokines and chemokines, which are involved in the establishment of endometrial receptivity, and the expression of matrix metalloproteinases implicated in invasion were also determined. Based on the results, the PMA-activated THP-1 macrophages exhibiting a pro-inflammatory phenotype may play a role in the regulation of HEC-1A endometrium cells. They alter the secretion of cytokines and chemokines, as well as the protein level of MMPs of HEC-1A cells. Moreover, activated THP-1 macrophages may elevate oxidative stress protection of HEC-1A endometrium cells. All these suggest that pro-inflammatory macrophages have a special role in the regulation of receptivity-related and implantation-related factors of HEC-1A cells.

DNA damage response-related signatures characterize the immune landscape and predict the prognosis of HCC via integrating single-cell and bulk RNA-sequencing

BACKGROUND

The occurrence and progression of hepatocellular carcinoma (HCC) are significantly affected by DNA damage response (DDR). Exploring DDR-related biomarkers can help predict the prognosis and immune characteristics of HCC.

METHODS

First, the single-cell RNA sequencing (scRNA-seq) dataset GSE242889 was processed and performed manual annotation. Then we found the marker genes of DDR-active subgroups based on "AUCell" algorithm. The "Limma" R package was used to identify differentially expressed genes (DEGs) between tumor and normal samples of HCC. The risk prognostic model was constructed by filtering genes using univariate Cox and LASSO regression analyses. Finally, the signatures were analyzed for immune infiltration, gene mutation, and drug sensitivity. Last but not least, KPNA2, which had the largest coefficient in our model was validated by experiments including western blot, MTT, colony formation and γ-H2AX assays.

RESULTS

We constructed a prognostic model based on 5 DDR marker genes including KIF2C, CDC20, KPNA2, UBE2S and ADH1B for HCC. We also proved that the model had an excellent performance in both training and validation cohorts. Patients in the high-risk group had a poorer prognosis, different immune features, gene mutation frequency, immunotherapy response and drug sensitivity compared with the low-risk group. Besides, our experimental results proved that KPNA2 was up-regulated in liver cancer cells than in hepatocytes. More importantly, the knockdown of KPNA2 significantly inhibited cell variability, proliferation and promoted DNA damage.

CONCLUSIONS

We innovatively integrated scRNA-seq and bulk RNA sequencing to construct the DDR-related prognostic model. Our model could effectively predict the prognosis, immune landscape and therapy response of HCC.

FCGR3A F158V alleles frequency differs in multiple myeloma patients from healthy population

FCGR3A presents a single nucleotide polymorphism at location 158 (V/F), which affects its binding to the fragment crystallizable (Fc) of antibodies (Abs). FcγRIIIa-158 V allotype has the highest affinity and is associated with a better clinical response to IgG1 monoclonal Abs (mAb) treatment. We compared the allele frequency of FCGR3A-F158V polymorphism in cohorts of patients with B-cell lymphoproliferative disorders, including multiple myeloma (MM), monoclonal gammopathy of undetermined significance (MGUS), non-Hodgkin lymphoma (NHL), and B-cell chronic leukemia (B-CLL). FCGR3A-158F homozygous were enriched and tended to be in MM and MGUS patients, respectively; but neither in B-CLL nor in NHL patients. We identified a significantly lower concentration of CD8 T-cells and resting memory CD4 T-cells in MM patients bone marrow with the F/F genotype, associated with an increase in the macrophage percentage. In contrast, natural killer cells increased in V/V homozygous patients. This suggests a deregulation of the immune microenvironment in FCGR3A-F/F homozygous patients. However, we did not observe difference in response following treatment combining chemotherapy associated or not with daratumumab, an IgG1 mAb direct against CD38. Our findings suggest that FCGR3A F158V polymorphism can regulate the immune environment and affect the development of tumor plasma cells.

Matrix Metalloproteinase 11 Promotes Migration and Invasion of Colorectal Cancer by Elevating Slug Protein

Purpose: Matrix metalloproteinase-11 (MMP11), which belongs to the stromelysin subgroup, has been reported to play a role in the progression of colorectal cancer (CRC). However, the significance of MMP11 in the tumor microenvironment, immune/stromal cells, and its mechanism in CRC remain unclear. Methods: The impact of MMP11 knockdown using specific short hairpin RNAs (shRNAs) on the metastasis and invasion of colorectal cancer RKO and SW480 cells was investigated using western blot, quantitative real-time polymerase chain reaction (qRT-PCR), transwell assays, and immunohistochemistry. Results: MMP11 mRNA expression was significantly higher in CRC cells than in normal cells, and its expression was stimulated in CCD-18Co fibroblasts. Additionally, MMP11 expression was found to be higher in individuals aged ≤ 65 years, the T4/T3 group, and Stage III/IV patients. Overall survival (OS) and disease-free survival rates were significantly different between the high and low MMP11 groups. Furthermore, the receiver operating characteristic (ROC) curves for MMP11 at 1-, 3-, and 5-years were 0.450, 0.552, and 0.560, respectively. Moreover, MMP11 promoted the migration and invasion of CRC cells by elevating the expression of Slug protein. Most importantly, MMP11 was positively associated with M0-macrophages and negatively associated with M1-macrophages, NK cells activated, NK cells resting, T cells CD4 memory activated, and T cells follicular helper, indicating the remarkable interactions of MMP11 with tumor immunology. Conclusions: MMP11 plays an important role in colorectal cancer development, and its mechanism in CRC needs to be further explored in the future.

Identification of adipocyte infiltration-related gene subtypes for predicting colorectal cancer prognosis and responses of immunotherapy/chemotherapy

Colorectal cancer (CRC) is a prevalent and aggressive malignancy characterized by a complex tumor microenvironment (TME). Given the variations in the level of adipocyte infiltration in TME, the prognosis may differ among CRC patients. Thus, there is an urgent need to establish a reliable method for identifying adipocyte subtypes in CRC in order to elucidate the impact of adipocyte infiltration on CRC treatment and prognosis. Herein, 144 adipocyte-infiltration-related genes (AIRGs) were identified as predictive markers for the immune-associated features and prognosis of CRC patients. Based on the 144 genes, the unsupervised clustering algorithm identified two distinct clusters of CRC patients with variations in molecular and signaling pathways, clinicopathological characteristics and responses to CRC chemotherapy and immunotherapy. Furthermore, an AIRG prognostic signature was constructed and validated in independent datasets. Overall, this study developed a prognostic signature based on AIRGs in CRC, which may contribute to the development of personalized treatment strategies and enhance prognostic prediction for CRC patients.

The Multifaceted Nature of Macrophages in Cardiovascular Disease

As the leading cause of mortality worldwide, cardiovascular disease (CVD) represents a variety of heart diseases and vascular disorders, including atherosclerosis, aneurysm, ischemic injury in the heart and brain, arrythmias, and heart failure. Macrophages, a diverse population of immune cells that can promote or suppress inflammation, have been increasingly recognized as a key regulator in various processes in both healthy and disease states. In healthy conditions, these cells promote the proper clearance of cellular debris, dead and dying cells, and provide a strong innate immune barrier to foreign pathogens. However, macrophages can play a detrimental role in the progression of disease as well, particularly those inflammatory in nature. This review will focus on the current knowledge regarding the role of macrophages in cardiovascular diseases.

Identification of a risk model for prognostic and therapeutic prediction in renal cell carcinoma based on infiltrating M0 cells

The tumor microenvironment (TME) comprises immune-infiltrating cells that are closely linked to tumor development. By screening and analyzing genes associated with tumor-infiltrating M0 cells, we developed a risk model to provide therapeutic and prognostic guidance in clear cell renal cell carcinoma (ccRCC). First, the infiltration abundance of each immune cell type and its correlation with patient prognosis were analyzed. After assessing the potential link between the depth of immune cell infiltration and prognosis, we screened the infiltrating M0 cells to establish a risk model centered on three key genes (TMEN174, LRRC19, and SAA1). The correlation analysis indicated a positive correlation between the risk score and various stages of the tumor immune cycle, including B-cell recruitment. Furthermore, the risk score was positively correlated with CD8 expression and several popular immune checkpoints (ICs) (TIGIT, CTLA4, CD274, LAG3, and PDCD1). Additionally, the high-risk group (HRG) had higher scores for tumor immune dysfunction and exclusion (TIDE) and exclusion than the low-risk group (LRG). Importantly, the risk score was negatively correlated with the immunotherapy-related pathway enrichment scores, and the LRG showed a greater therapeutic benefit than the HRG. Differences in sensitivity to targeted drugs between the HRG and LRG were analyzed. For commonly used targeted drugs in RCC, including axitinib, pazopanib, temsirolimus, and sunitinib, LRG had lower IC50 values, indicating increased sensitivity. Finally, immunohistochemistry results of 66 paraffin-embedded specimens indicated that SAA1 was strongly expressed in the tumor samples and was associated with tumor metastasis, stage, and grade. SAA1 was found to have a significant pro-tumorigenic effect by experimental validation. In summary, these data confirmed that tumor-infiltrating M0 cells play a key role in the prognosis and treatment of patients with ccRCC. This discovery offers new insights and directions for the prognostic prediction and treatment of ccRCC.

Exploring prognostic and immunological characteristics of pancreatic ductal adenocarcinoma through comprehensive genomic analysis of tertiary lymphoid structures and CD8 + T-cells

PURPOSE

Tertiary lymphoid structures (TLSs) and CD8 + T-cells are potential prognostic indicators for pancreatic ductal adenocarcinoma (PDAC). We established a novel scoring system for evaluating the risk for PDAC based on TLS- and CD8 + T-cell-related genes.

METHODS

We analyzed single-cell sequence data from PDAC patients in the Genome Sequence Archive. Bioinformatics and machine algorithms established and validated a scoring method (T-C score) based on PDAC survival-related genes highly expressed in TLSs and CD8 + T-cells. Patients were stratified into the low- and high-T-C score groups. Differences in survival, pathway enrichment, mutation status, immune cell infiltration, expression of immune checkpoint-associated genes, tumor stemness, and response to antitumor therapy were compared through computer simulation methods.

RESULTS

Overall survival differed significantly between the training and validation cohorts' low- and high-T-C score groups. The low-T-C score group correlated with lower tumor mutation burden and lower levels of tumor stemness compared with the high-T-C score group. Patients with lower T-C scores exhibited advantages in immunotherapeutic responses and might be more sensitive to the chemotherapeutic regimen and multi-kinase inhibitors.

CONCLUSION

The T-C score could serve as an effective model for predicting the survival and therapeutic responses of patients with PDAC.

Immunotherapy and drug sensitivity predictive roles of a novel prognostic model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most significant causes of cancer-related deaths in the worldwide. Currently, predicting the survival of patients with HCC and developing treatment drugs still remain a significant challenge. In this study, we employed prognosis-related genes to develop and externally validate a predictive risk model. Furthermore, the correlation between signaling pathways, immune cell infiltration, immunotherapy response, drug sensitivity, and risk score was investigated using different algorithm platforms in HCC. Our results showed that 11 differentially expressed genes including UBE2C, PTTG1, TOP2A, SPP1, FCN3, SLC22A1, ADH4, CYP2C8, SLC10A1, F9, and FBP1 were identified as being related to prognosis, which were integrated to construct a prediction model. Our model could accurately predict patients' overall survival using both internal and external datasets. Moreover, a strong correlation was revealed between the signaling pathway, immune cell infiltration, immunotherapy response, and risk score. Importantly, a novel potential drug candidate for HCC treatment was discovered based on the risk score and also validated through ex vivo experiments. Our finds offer a novel perspective on prognosis prediction and drug exploration for cancer patients.

Machine Learning-based Development and Validation of a Cell Senescence Predictive and Prognostic Signature in Intrahepatic Cholangiocarcinoma

Background: Previous studies have shown that cellular senescence is strongly associated with tumorigenesis and the tumor microenvironment. Accordingly, we developed a novel prognostic signature for intrahepatic cholangiocarcinoma (ICCA) based on senescence-associated long non-coding RNAs (SR-lncRNAs) and identified a lncRNA-miRNA-mRNA axis involving in ICCA. Methods: Based on the 197 senescence-associated genes (SRGs) from Genacards and their expression in Fu-ICCA cohort, we identified 20 lncRNAs as senescence-associated lncRNAs (SR-lncRNAs) through co-expression and cox-regression analysis. According to 20 SR-lncRNAs, patients with ICCA were classified into 2 molecular subtypes using unsupervised clustering machine learning approach and to explore the prognostic and functional heterogeneity between these two subtypes. Subsequently, we integrated 113 machine learning algorithms to develop senescence-related lncRNA signature, ultimately identifying 11 lncRNAs and constructing prognostic models and risk stratification. The correlation between the signature and the immune landscape, immunotherapy response as well as drug sensitivity are explored too. Results: We developed a novel senescence related signature. The predictive model and risk score calculated by the signature exhibited favorable prognostic predictive performance, which is a suitable independent risk factor for the prognosis of patients with ICCA based on Kaplan-Meier plotter, nomogram and receiving operating characteristic (ROC) curves. The results were validated using external datasets. Estimate, ssGSEA (single sample gene set enrichment analysis), IPS (immunophenotype score) and TIDE (tumor immune dysfunction and exclusion) algorithms revealed higher immune infiltration, higher immune scores, lower immune escape potential and better response to immunotherapy in the high-risk group. In addition, signature identifies eight chemotherapeutic agents, including cisplatin for patients with different risk levels, providing guidance for clinical treatment. Finally, we identified a set of lncRNA-miRNA-mRNA axes involved in ICCA through regulation of senescence. Conclusion: SR-lncRNAs signature can favorably predict the prognosis, risk stratification, immune landscape and immunotherapy response of patients with ICCA and consequently guide individualized treatment.

A pyrimidine metabolism-related signature for prognostic and immunotherapeutic response prediction in hepatocellular carcinoma by integrating analyses

BACKGROUND

Hepatocellular carcinoma (HCC), with discouraging morbidity and mortality, ranks as one of the most prevalent tumors worldwide. Pyrimidine metabolism is a critical process that regulates DNA and RNA synthesis in cells. It is imperative to investigate the significance of pyrimidine metabolism in liver cancer.

METHODS

Transcriptome and clinical data were downloaded from the TCGA database and the GEO database. The genes related to pyrimidine metabolism were sourced from the MSigDB. The pyrimidine metabolism-related signature (PMRS) was constructed through Cox regression and Lasso regression and then verified in the external validation set from the ICGC database. Functional enrichment, immune infiltration analysis, drug sensitivity, and Immunophenoscore (IPS) were further implemented to predict the response to immunotherapy. The role of PMRS in the malignant phenotype of hepatocellular carcinoma was explored by conducting a series of in vitro experiments.

RESULTS

Our study developed a four-genes PMRS which demonstrates a substantial correlation with the prognosis of HCC patients, serving as an independent predictor in clinical practice. The result of risk-stratified analysis yielded evidence that low-risk patients experienced more favorable clinical outcomes. The nomogram exhibited remarkable prognostic predictive value. The subsequent results revealed that low-risk patients manifested a more promising response to immunotherapy. Moreover, the results of cell experiments demonstrated that the downregulation of DCK markedly inhibited the malignant phenotype of hepatocellular carcinoma.

CONCLUSIONS

Our pyrimidine metabolism-centered prognostic signature accurately predicts overall survival, immune status, and treatment response in hepatocellular carcinoma (HCC) patients, offering innovative insights for precise diagnosis, personalized treatment, and improved prognosis.

Tolerogenic probiotics Lactobacillus delbrueckii and Lactobacillus rhamnosus promote anti-inflammatory profile of macrophages-derived monocytes of newly diagnosed patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is known as an autoimmune disorder that is characterized by the breakdown of self-tolerance, resulting in disease onset and progression. Macrophages have been implicated as a factor in the development of SLE through faulty phagocytosis of dead cells or an imbalanced M1/M2 ratio. The study aimed to investigate the immunomodulatory effects of Lactobacillus delbrueckii and Lactobacillus rhamnosus on M1 and M2 macrophages in new case lupus patients. For this purpose, blood monocytes were collected from lupus patients and healthy people and were cultured for 5 days to produce macrophages. For 48 h, the macrophages were then cocultured with either probiotics or lipopolysaccharides (LPS). Flow cytometry and real-time polymerase chain reaction were then used to analyze the expression of cluster of differentiation (CD) 14, CD80, and human leukocyte antigen - DR (HLADR) markers, as well as cytokine expression (interleukin [IL]1-β, IL-12, tumor necrosis factor α [TNF-α], IL-10, and transforming growth factor beta [TGF-β]). The results indicated three distinct macrophage populations, M0, M1, and M2. In both control and patient-derived macrophage-derived monocytes (MDMs), the probiotic groups showed a decrease in CD14, CD80, and HLADR expression compared to the LPS group. This decrease was particularly evident in M0 and M2 macrophages from lupus patients and M1 macrophages from healthy subjects. In addition, the probiotic groups showed increased levels of IL-10 and TGF-β and decreased levels of IL-12, IL1-β, and TNF-α in MDMs from both healthy and lupus subjects compared to the LPS groups. Although there was a higher expression of pro-inflammatory cytokines in lupus patients, there was a higher expression of anti-inflammatory cytokines in healthy subjects. In general, L. delbrueckii and L. rhamnosus could induce anti-inflammatory effects on MDMs from both healthy and lupus subjects.

Machine learning-based disulfidptosis-related lncRNA signature predicts prognosis, immune infiltration and drug sensitivity in hepatocellular carcinoma

Disulfidptosis a new cell death mode, which can cause the death of Hepatocellular Carcinoma (HCC) cells. However, the significance of disulfidptosis-related Long non-coding RNAs (DRLs) in the prognosis and immunotherapy of HCC remains unclear. Based on The Cancer Genome Atlas (TCGA) database, we used Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression model to construct DRL Prognostic Signature (DRLPS)-based risk scores and performed Gene Expression Omnibus outside validation. Survival analysis was performed and a nomogram was constructed. Moreover, we performed functional enrichment annotation, immune infiltration and drug sensitivity analyses. Five DRLs (AL590705.3, AC072054.1, AC069307.1, AC107959.3 and ZNF232-AS1) were identified to construct prognostic signature. DRLPS-based risk scores exhibited better predictive efficacy of survival than conventional clinical features. The nomogram showed high congruence between the predicted survival and observed survival. Gene set were mainly enriched in cell proliferation, differentiation and growth function related pathways. Immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group. Additionally, the high-risk group exhibited higher sensitivity to Afatinib, Fulvestrant, Gefitinib, Osimertinib, Sapitinib, and Taselisib. In conclusion, our study highlighted the potential utility of the constructed DRLPS in the prognosis prediction of HCC patients, which demonstrated promising clinical application value.

Establishment of a Human Immunocompetent 3D Tissue Model to Enable the Long-Term Examination of Biofilm-Tissue Interactions

Different studies suggest an impact of biofilms on carcinogenic lesion formation in varying human tissues. However, the mechanisms of cancer formation are difficult to examine in vivo as well as in vitro. Cell culture approaches, in most cases, are unable to keep a bacterial steady state without any overgrowth. In our approach, we aimed to develop an immunocompetent 3D tissue model which can mitigate bacterial outgrowth. We established a three-dimensional (3D) co-culture of human primary fibroblasts with pre-differentiated THP-1-derived macrophages on an SIS-muc scaffold which was derived by decellularisation of a porcine intestine. After establishment, we exposed the tissue models to define the biofilms of the Pseudomonas spec. and Staphylococcus spec. cultivated on implant mesh material. After 3 days of incubation, the cell culture medium in models with M0 and M2 pre-differentiated macrophages presented a noticeable turbidity, while models with M1 macrophages presented no noticeable bacterial growth. These results were validated by optical density measurements and a streak test. Immunohistology and immunofluorescent staining of the tissue presented a positive impact of the M1 macrophages on the structural integrity of the tissue model. Furthermore, multiplex ELISA highlighted the increased release of inflammatory cytokines for all the three model types, suggesting the immunocompetence of the developed model. Overall, in this proof-of-principle study, we were able to mitigate bacterial overgrowth and prepared a first step for the development of more complex 3D tissue models to understand the impact of biofilms on carcinogenic lesion formation.

Arsenic album 30C exhibits crystalline nano structure of arsenic trioxide and modulates innate immune markers in murine macrophage cell lines

Macrophages are associated with innate immune response and M1-polarized macrophages exhibit pro-inflammatory functions. Nanoparticles of natural or synthetic compounds are potential triggers of innate immunity. As2O3 is the major component of the homeopathic drug, Arsenic album 30C.This has been claimed to have immune-boosting activities, however, has not been validated experimentally. Here we elucidated the underlying mechanism of Ars. alb 30C-mediated immune priming in murine macrophage cell line. Transmission Electron Microscopy (TEM) and X-ray diffraction (XRD) used for the structural analysis of the drug reveals the presence of crystalline As2O3 nanoparticles of cubic structure. Similarly, signatures of M1-macrophage polarization were observed by surface enhanced Raman scattering (SERS) in RAW 264.7 cells with concomitant over expression of M1 cell surface marker, CD80 and transcription factor, NF-κB, respectively. We also observed a significant increase in pro-inflammatory cytokines like iNOS, TNF-α, IL-6, and COX-2 expression with unaltered ROS and apoptosis in drug-treated cells. Enhanced expression of Toll-like receptors 3 and 7 were observed both in transcriptional and translational levels after the drug treatment. In sum, our findings for the first time indicated the presence of crystalline As2O3 cubic nanostructure in Ars. alb 30C which facilitates modulation of innate immunity by activating macrophage polarization.

CDK1-SRC Interaction-Dependent Transcriptional Activation of HSP90AB1 Promotes Antitumor Immunity in Hepatocellular Carcinoma

This study aimed to analyze multiomics data and construct a regulatory network involving kinases, transcription factors, and immune genes in hepatocellular carcinoma (HCC) prognosis. The researchers used transcriptomic, proteomic, and clinical data from TCGA and GEO databases to identify immune genes associated with HCC. Statistical analysis, meta-analysis, and protein-protein interaction analyses were performed to identify key immune genes and their relationships. In vitro and in vivo experiments validated the CDK1-SRC-HSP90AB1 network's effects on HCC progression and antitumor immunity. A prognostic risk model was developed using clinicopathological features and immune infiltration. The immune genes LPA, BIRC5, HSP90AB1, ROBO1, and CCL20 were identified as the key prognostic factors. The CDK1-SRC-HSP90AB1 network promoted HCC cell proliferation and migration, with HSP90AB1 being transcriptionally activated by the CDK1-SRC interaction. Manipulating SRC or HSP90AB1 reversed the effects of CDK1 and SRC on HCC. The CDK1-SRC-HSP90AB1 network also influenced HCC tumor formation and antitumor immunity. Overall, this study highlights the importance of the CDK1-SRC-HSP90AB1 network as a crucial immune-regulatory network in the HCC prognosis.

Characterization of the immune cell function landscape in head and neck squamous carcinoma to assist in prognosis prediction and immunotherapy

BACKGROUND

The malignant characteristics of cancer depend not only on intrinsic properties of cancer cells but also on the functions of infiltrating immune cells. In this study, we aimed to investigate the functional landscape of immune cells in head and neck squamous cell carcinoma (HNSCC).

METHODS

We employed single-sample gene set enrichment analysis to examine the immunophenotypes of HNSCC based on 29 immune cell functions (ICFs) in TCGA and GSE65858 datasets. We analyzed the clinical features, immune microenvironment, molecular profiles, and biological processes. Additionally, we developed and validated an ICF-based risk score for personalized prognosis prediction. We confirmed the value of the ICF score in our cohort using qRT-PCR and immunohistochemistry. Molecular docking was used to predict potential compounds for immunotherapy.

RESULTS

Three immunophenotypes (Immune-L, Immune-M, and Immune-H) were identified in 769 HNSCC samples. The characteristics of Immune-H were consistent with a "Hot" tumor, Immune-L was similar to a "Cold" tumor, and Immune-M exhibited intermediate features. The ICF risk score was associated with immune checkpoints, infiltrating immune cells, tumor mutation burden, and sensitivities to targeted/chemotherapeutic agents. Gene set variation analysis implicated the involvement of metabolic reprogramming pathways in the high-risk group. The combination of "Tumor Immune Dysfunction and Exclusion" and "Immunophenoscore" algorithms indicated that the low-risk group had a higher likelihood of benefiting from immunotherapy. Finally, we identified Eltrombopag and other compounds that may be beneficial for HNSCC immunotherapy.

CONCLUSION

Our study provides a novel perspective on the tumor microenvironment of HNSCC, aiding in the understanding of HNSCC heterogeneity and the development of personalized/precision medicine.

Integrative Analysis of Single-Cell and Bulk RNA Sequencing Reveals Prognostic Characteristics of Macrophage Polarization-Related Genes in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a group of cancers with poor prognosis. The combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) can identify important genes involved in cancer development and progression from a broader perspective.

Methods

The scRNA-seq data and bulk RNA-seq data of LUAD were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Analyzing scRNA-seq for core cells in the GSE131907 dataset, and the uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Macrophage polarization-associated subtypes were acquired from the TCGA-LUAD dataset after analysis, followed by further identification of differentially expressed genes (DEGs) in the TCGA-LUAD dataset (normal/LUAD tissue samples, two subtypes). Venn diagrams were utilized to visualize differentially expressed and highly variable macrophage polarization-related genes. Subsequently, a prognostic risk model for LUAD patients was constructed by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO), and the model was investigated for stability in the external data GSE72094. After analyzing the correlation between the trait genes and significantly mutated genes, the immune infiltration between the high/low-risk groups was then examined. The Monocle package was applied to analyze the pseudo-temporal trajectory analysis of different cell clusters in macrophage clusters. Subsequently, cell clusters of data macrophages were selected as key cell clusters to explore the role of characteristic genes in different cell populations and to identify transcription factors (TFs) that affect signature genes. Finally, qPCR were employed to validate the expression levels of prognosis signature genes in LUAD.

Results

424 macrophage highly variable genes, 3920 DEGs, and 9561 DEGs were obtained from macrophage clusters, the macrophage polarization-related subtypes, and normal/LUAD tissue samples, respectively. Twenty-eight differentially expressed and highly mutated MPRGs were obtained. A prognostic risk model with 7 DE-MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) was constructed. This prognostic model still has a good prediction effect in the GSE72094 dataset. ZNF536 and DNAH9 were mutated in the low-risk group, while COL11A1 was mutated in the high-risk group, and they were highly correlated with the characteristic genes. A total of 11 immune cells were significantly different in the high/low-risk groups. Five cell types were again identified in the macrophage cluster, and then NK cells: CD56hiCD62L+ differentiated earlier and were present mainly on 2 branches. While macrophages were present on 2 branches and differentiated later. It was found that the expression levels of BCLAF1 and MAX were higher in cluster 1, which might be the TFs affecting the expression of the characteristic genes. Moreover, qPCR confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis.

Conclusion

Seven MPRGs (RGS13, ADRB2, DDIT4, MS4A2, ALDH2, CTSH, and PKM) were identified as prognostic genes for LUAD and revealed the mechanisms of MPRGs at the single-cell level.

A novel immune-related gene signature for predicting immunotherapy outcomes and survival in clear cell renal cell carcinoma

Clear cell renal carcinoma (ccRCC) is one of the most common cancers worldwide. In this study, a new model of immune-related genes was developed to predict the overall survival and immunotherapy efficacy in patients with ccRCC. Immune-related genes were obtained from the ImmPort database. Clinical data and transcriptomics of ccRCC samples were downloaded from GSE29609 and The Cancer Genome Atlas. An immune-related gene-based prognostic model (IRGPM) was developed using the least absolute shrinkage and selection operator regression algorithm and multivariate Cox regression. The reliability of the developed models was evaluated by Kaplan-Meier survival curves and time-dependent receiver operating characteristic curves. Furthermore, we constructed a nomogram based on the IRGPM and multiple clinicopathological factors, along with a calibration curve to examine the predictive power of the nomogram. Overall, this study investigated the association of IRGPM with immunotherapeutic efficacy, immune checkpoints, and immune cell infiltration. Eleven IRGs based on 528 ccRCC samples significantly associated with survival were used to construct the IRGPM. Remarkably, the IRGPM, which consists of 11 hub genes (SAA1, IL4, PLAUR, PLXNB3, ANGPTL3, AMH, KLRC2, NR3C2, KL, CSF2, and SEMA3G), was found to predict the survival of ccRCC patients accurately. The calibration curve revealed that the nomogram developed with the IRGPM showed high predictive performance for the survival probability of ccRCC patients. Moreover, the IRGPM subgroups showed different levels of immune checkpoints and immune cell infiltration in patients with ccRCC. IRGPM might be a promising biomarker of immunotherapeutic responses in patients with ccRCC. Overall, the established IRGPM was valuable for predicting survival, reflecting the immunotherapy response and immune microenvironment in patients with ccRCC.

Mechanistic insights into super-enhancer-driven genes as prognostic signatures in patients with glioblastoma

BACKGROUND

Glioblastoma (GBM) is one of the most common malignant brain tumors in adults and is characterized by high aggressiveness and rapid progression, poor treatment, high recurrence rate, and poor prognosis. Although super-enhancer (SE)-driven genes haven been recognized as prognostic markers for several cancers, whether it can be served as effective prognostic markers for patients with GBM has not been evaluated.

METHODS

We first combined histone modification data with transcriptome data to identify SE-driven genes associated with prognosis in patients with GBM. Second, we developed a SE-driven differentially expressed genes (SEDEGs) risk score prognostic model by univariate Cox analysis, KM survival analysis, multivariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression. Its reliability in predicting was verified by two external data sets. Third, through mutation analysis, immune infiltration, we explored the molecular mechanisms of prognostic genes. Next, Genomics of Drug Sensitivity in Cancer (GDSC) and the Connectivity Map (cMap) database were employed to assess different sensitivities to chemotherapeutic agents and small-molecule drug candidates between high- and low-risk patients. Finally, SEanalysis database was chosen to identify SE-driven transcription factors (TFs) regulating prognostic markers which will reveal a potential SE-driven transcriptional regulatory network.

RESULTS

First, we developed a 11-gene risk score prognostic model (NCF2, MTHFS, DUSP6, G6PC3, HOXB2, EN2, DLEU1, LBH, ZEB1-AS1, LINC01265, and AGAP2-AS1) selected from 1,154 SEDEGs, which is not only an independent prognostic factor for patients, but also can effectively predict the survival rate of patients. The model can effectively predict 1-, 2- and 3-year survival of patients and was validated in external Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) datasets. Second, the risk score was positively correlated with the infiltration of regulatory T cell, CD4 memory activated T cell, activated NK cell, neutrophil, resting mast cell, M0 macrophage, and memory B cell. Third, we found that high-risk patients showed higher sensitivity than low-risk patients to both 27 chemotherapeutic agents and 4 small-molecule drug candidates which might benefit further precision therapy for GBM patients. Finally, 13 potential SE-driven TFs imply how SE regulates GBM patient's prognosis.

CONCLUSION

The SEDEG risk model not only helps to elucidate the impact of SEs on the course of GBM, but also provides a bright future for prognosis determination and choice of treatment for GBM patients.

Diosmetin inhibits the growth and invasion of gastric cancer by interfering with M2 phenotype macrophage polarization

Overturning M2 phenotype macrophage polarization is a promising therapeutic strategy for gastric cancer (GC). Diosmetin (DIO) is a natural flavonoid with antitumor effect. The aim of this study was to investigate the effect of DIO on polarization of M2 phenotype macrophages in GC. THP-1 cells were induced to M2 phenotype macrophages and co-cultured with AGS cells. The effects of DIO were determined by flow cytometry, qRT-PCR, CCK-8, Transwell, and western blot. To explore the mechanisms, THP-1 cells were transfected with adenoviral vectors containing tumor necrosis factor receptor-associated factor 2 (TRAF2) or si-TRAF2. DIO (0, 5, 10, and 20 μM) restrained the M2 phenotype macrophage polarization. In addition, DIO (20 μM) reversed the increased viability and invasion of AGS cells induced by the co-culture of M2 macrophages. Mechanistically, TRAF2 knockdown inhibited the effect of M2 phenotype macrophages on AGS cells' growth and invasion. Furthermore, DIO (20 μM) was found to decrease TRAF2/NF-κB activity in GC cells. However, TRAF2 overexpressed reversed the inhibitory effect of DIO on the co-culture system. The in vivo study confirmed that DIO treatment (50 mg/kg) could repress the growth of GC. DIO treatment markedly reduced the expressions of Ki-67 and N-cadherin, and decreased the protein levels of TRAF2 and p-NF-κB/NF-κB. In conclusion, DIO inhibited the growth and invasion of GC cells by interfering with M2 phenotype macrophage polarization through repression of the TRAF2/NF-κB signaling pathway.

A novel signature of the ligand and receptor genes associated with disulfidoptosis for prediction of prognosis, immunologic therapy responses in hepatocellular carcinoma

Backgroud

We aimed to explore the prognostic features of ligand and receptor genes associated with disulfidoptosis in hepatocellular carcinoma (HCC) and establish a risk signature utilizing these genes to predict the prognosis of HCC patients.

Methods

We used scRNA-seq data from GSE166635 to differentiate malignant cells from normal cells using "copykat".The study thoroughly examined the disparities in disulfidoptosis scores and the associated gene expressions between malignant and normal cells.We identified key ligand and receptor genes that are specific to HCC cells.Subsequently, Correlation analysis was conducted to ascertain the ligand and receptor genes associated with disulfidoptosis.We performed univariate Cox regression analysis to identify prognostic ligand and receptor genes associated with disulfidoptosis.We employed LASSO to construct a risk signature using prognostic ligand and receptor genes associated with disulfidoptosis.Lastly, we developed a nomogram model that integrates the risk signature with clinicopathological characteristics.

Results

Malignant cells displayed a marked increase in disulfidoptosis scores and the expression of associated genes compared to normal cells.We identified 110 receptor and ligand genes significantly associated with disulfidoptosis, and narrowed them down to create a risk signature comprising eight genes.Multivariate analysis confirmed the risk signature as an independent prognostic factor for HCC and validated its predictive value for immunotherapy outcomes.A novel nomogram was developed, incorporating stage information and the risk signature derived from disulfidoptosis-related receptor and ligand genes, demonstrating excellent predictive accuracy and reliability in HCC prognosis prediction.

Conclusion

Risk signatures based on disulfidoptosis-associated ligand and receptor genes can effectively predict HCC prognosis and may inform immunotherapy strategies.

Typing and modeling of hepatocellular carcinoma based on disulfidptosis-related amino acid metabolism genes for predicting prognosis and guiding individualized treatment

Introduction

Hepatocellular carcinoma (HCC) is the most common type of cancer worldwide and is a major public health problem in the 21st century. Disulfidopathy, a novel cystine-associated programmed cell death, plays complex roles in various tumors. However, the relationship between disulfidoptosis and prognosis in patients with HCC remains unclear. This study aimed to explore the relationship between disulfideptosis and the prognosis of liver cancer and to develop a prognostic model based on amino acid metabolism and disulfideptosis genes.

Methods

We downloaded the clinicopathological information and gene expression data of patients with HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and classified them into different molecular subtypes based on the expression patterns of disulfidoptosis-associated amino acid metabolism genes (DRAGs). Patients were then classified into different gene subtypes using the differential genes between the molecular subtypes, and the predictive value of staging was assessed using survival and clinicopathological analyses. Subsequently, risk prognosis models were constructed based on Cox regression analysis to assess patient prognosis, receiver operating characteristic (ROC) curves, somatic mutations, microsatellite instability, tumor microenvironment, and sensitivity to antitumor therapeutic agents.

Results

Patients were classified into two subtypes based on differential DRAGs gene expression, with cluster B having a better survival outcome than cluster A. Three gene subtypes were identified based on the differential genes between the two DRAGs molecular subtypes. The patients in cluster B had the best prognosis, whereas those in cluster C had the worst prognosis. The heat map showed better consistency in the patient subtypes obtained using both typing methods. We screened six valuable genes and constructed a prognostic signature. By scoring, we found that patients in the low-risk group had a better prognosis, higher immune scores, and more abundant immune-related pathways compared to the high-risk group, which was consistent with the tumor subtype results.

Discussion

In conclusion, we developed a prognostic signature of disulfidptosis-related amino acid metabolism genes to assist clinicians in predicting the survival of patients with HCC and provide a reference value for targeted therapy and immunotherapy for HCC.

Clinicogenomic characteristics and synthetic lethal implications of germline homologous recombination-deficient hepatocellular carcinoma

BACKGROUNDS AND AIMS

We performed an in-depth examination of pathogenic germline variants (PGVs) and somatic variants in DNA damage response (DDR) genes in hepatocellular carcinoma (HCC) to explore their clinical and genomic impacts.

APPROACH AND RESULTS

We used a merged whole-exome or RNA sequencing data set derived from in-house ( n = 230) and The Cancer Genome Atlas ( n = 362) databases of multiethnic HCC samples. We also evaluated synthetic lethal approaches targeting mutations in homologous recombination (HR) genes using HCC cells selected from five genomic databases of cancer cell lines. A total of 110 PGVs in DDR pathways in 96 patients were selected. Of the PGV carriers, 44 were HR-altered and found to be independently associated with poorer disease-free survival after hepatectomy. The most frequently altered HR gene in both germline and somatic tissues was POLQ , and this variant was detected in 22.7% (10/44) and 23.8% (5/21) of all the corresponding carriers, respectively. PGVs in HR were significantly associated with upregulation of proliferation and replication-related genes and familial risk of HCC. Samples harboring PGVs in HR with loss of heterozygosity were most strongly correlated with the genomic footprints of deficient HR, such as mutation burden and denovoSig2 (analogous to Catalogue of Somatic Mutations in Cancer [COSMIC] 3), and poor outcome. Pharmacologic experiments with HCC cells defective in BRCA2 or POLQ suggested that tumors with this phenotype are synthetic lethal with poly(ADP-ribose) polymerase inhibitors.

CONCLUSIONS

Our findings suggest that germline HR defects in HCC tend to confer a poor prognosis and result in distinctive genomic scarring. Tests of the clinical benefits of HR-directed treatments in the affected patients are needed.

Decoding Immune Signature to Detect the Risk for Early-Stage HCC Recurrence

Hepatocellular carcinoma (HCC) is often recognized as an inflammation-linked cancer, which possesses an immunosuppressive tumor microenvironment. Curative treatments such as surgical resection, liver transplantation, and percutaneous ablation are mainly applicable in the early stage and demonstrate significant improvement of survival rate in most patients. However, 70-80% of patients report HCC recurrence within 5 years of curative treatment, representing an important clinical issue. However, there is no effective recurrence marker after surgical and locoregional therapies, thus, tumor size, number, and histological features such as cancer cell differentiation are often considered as risk factors for HCC recurrence. Host immunity plays a critical role in regulating carcinogenesis, and the immune microenvironment characterized by its composition, functional status, and density undergoes significant alterations in each stage of cancer progression. Recent studies reported that analysis of immune contexture could yield valuable information regarding the treatment response, prognosis and recurrence. This review emphasizes the prognostic value of tumors associated with immune factors in HCC recurrence after curative treatment. In particular, we review the immune landscape and immunological factors contributing to early-stage HCC recurrence, and discuss the immunotherapeutic interventions to prevent tumor recurrence following curative treatments.

Developmental and homeostatic signaling transmitted by the G-protein coupled receptor FPR2

Formyl peptide receptor 2 (FPR2) and its mouse counterpart Fpr2 are the members of the G protein-coupled receptor (GPCR) family. FPR2 is the only member of the FPRs that interacts with ligands from different sources. FPR2 is expressed in myeloid cells as well as epithelial cells, endothelial cells, neurons, and hepatocytes. During the past years, some unusual properties of FPR2 have attracted intense attention because FPR2 appears to possess dual functions by activating or inhibiting intracellular signal pathways based on the nature, concentration of the ligands, and the temporal and spatial settings of the microenvironment in vivo, the cell types it interacts with. Therefore, FPR2 controls an abundant array of developmental and homeostatic signaling cascades, in addition to its "classical" capacity to mediate the migration of hematopoietic and non-hematopoietic cells including malignant cells. In this review, we summarize recent development in FPR2 research, particularly in its role in diseases, therefore helping to establish FPR2 as a potential target for therapeutic intervention.

Short histological kaleidoscope - recent findings in histology. Part III

The paper provides an overview of the current understanding of different cells' biology (e.g., keratinocytes, Paneth cells, myoepithelial cells, myofibroblasts, chondroclasts, monocytes, atrial cardiomyocytes), including their origin, structure, function, and role in disease pathogenesis, and of the latest findings in the medical literature concerning the brown adipose tissue and the juxtaoral organ of Chievitz.

Comprehensive analysis of immunogenic cell death associated genes expression, tumor microenvironment, and prognosis in hepatocellular carcinoma

Background: Immunogenic cell death (ICD) plays an important role in the development of cancers. This study attempted to explore the role of ICD in the prognosis of hepatocellular carcinoma (HCC).

Methods: Gene expression and clinical data were downloaded from The Cancer Genome Alas and Gene Expression Omnibus dataset. The immune/stromal/Estimate scores of the tumor microenvironment (TME) were calculated by ESTIMATE and CIBERSORT algorithms. Kaplan-Meier analysis, functional enrichment analysis, least absolute shrinkage and selection operator (LASSO) analysis, and univariate and multivariate Cox regression analysis were used for prognostic gene screening and prognostic model construction. The correlation of immune cell infiltration and risk scores was analyzed as well. Molecular docking was used to explore the relevance of related genes to anti-cancer drugs.

Results: Ten ICD associated differentially expressed genes in HCC were found, and all of them had good predictive ability for HCC. ICD gene high amount of expression group was associated with poor prognosis (p = 0.015). The TME, immune cell infiltration and gene expression were different between ICD high and low groups (all p &lt; 0.05). Six ICD associated genes (BAX, CASP8, IFNB1, LY96, NT5E and PIK3CA) which could predict the survival status were identified and used to construct the prognostic model for HCC. A risk score was calculated and it could be used as an independent prognostic factor in HCC patients (p &lt; 0.001). In addition, the risk score had a positive correlation with macrophage M0 (r = 0.33, p = 0.0086). Molecular docking indicated that sorafenib could bind strongly to the target protein, representing that sorafenib may exert anticancer effects through these six ICD associated genes.

Conclusion: This study established a prognostic model including six ICD associated genes for HCC, which may deepen our understanding of ICD and guide therapy for HCC patients.

Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs

Tuberculosis (TB) is a common infectious disease linked to host genetics and the innate immune response. It is vital to investigate new molecular mechanisms and efficient biomarkers for Tuberculosis because the pathophysiology of the disease is still unclear, and there aren't any precise diagnostic tools. This study downloaded three blood datasets from the GEO database, two of which (GSE19435 and 83456) were used to build a weighted gene co-expression network for searching hub genes associated with macrophage M1 by the CIBERSORT and WGCNA algorithms. Furthermore, 994 differentially expressed genes (DEGs) were extracted from healthy and TB samples, four of which were associated with macrophage M1, naming RTP4, CXCL10, CD38, and IFI44. They were confirmed as upregulation in TB samples by external dataset validation (GSE34608) and quantitative real-time PCR analysis (qRT-PCR). CMap was used to predict potential therapeutic compounds for tuberculosis using 300 differentially expressed genes (150 downregulated and 150 upregulated genes), and six small molecules (RWJ-21757, phenamil, benzanthrone, TG-101348, metyrapone, and WT-161) with a higher confidence value were extracted. We used in-depth bioinformatics analysis to investigate significant macrophage M1-related genes and promising anti-Tuberculosis therapeutic compounds. However, more clinical trials were necessary to determine their effect on Tuberculosis.

Identification of Aging and Young Subtypes for Predicting Colorectal Cancer Prognosis and Immunotherapy Responses

Colorectal cancer (CRC) is critically related to aging and severely threatens human lives. To better explore the effects of aging on CRC progression and therapy outcome, a reliable aging subtypes identification of CRC is urgently desired. Here, 28 aging-related genes associated with the CRC prognosis were selected by univariate Cox analyses. Based on these 28 genes, CRC patients were divided into the aging subtype and young subtype by non-negative matrix factorization clustering. Aging subtype and young subtype of CRC were identified with distinct molecular features and clinical prognosis. The aging subtype was characterized by upregulation of senescence-associated secretory phenotype, higher frequencies of TP53 and immune checkpoint molecules, and high sensitivity to protein kinase and angiogenesis inhibitors. Furthermore, 14 genes were selected by LASSO penalized Cox regression analyses for aging-related risk signature construction. The constructed aging risk signature exhibited good prediction and the nomogram showed robust discrimination power over the traditional CRC staging system. In conclusion, this study successfully established aging subtype and young subtype of CRC, which is helpful to identify patients with aging characteristics to evaluate prognosis and treatment outcomes. Introducing aging-based subtypes into clinical concern and patient prognostication provides new opportunities for personalized CRC treatment.

Comprehensive analysis of cuproptosis-related lncRNAs in immune infiltration and prognosis in hepatocellular carcinoma

BACKGROUND

Being among the most common malignancies worldwide, hepatocellular carcinoma (HCC) accounting for the third cause of cancer mortality. The regulation of cell death is the most crucial step in tumor progression and has become a crucial target for nearly all therapeutic options. Cuproptosis, a copper-induced cell death, was recently reported in Science. However, its primary function in carcinogenesis is still unclear.

METHODS

Cuproptosis-related lncRNAs significantly associated with overall survival (OS) were screened by stepwise univariate Cox regression. The signature of cuproptosis-related lncRNAs for HCC prognosis was constructed by the LASSO algorithm and multivariate Cox regression. Further Kaplan-Meier analysis, proportional hazards model, and ROC analysis were performed. Functional annotation was performed using gene set enrichment analysis (GSEA). The relationship between prognostic cuproptosis-related lncRNAs and HCC prognosis was further explored by GEPIA( http://gepia.cancer-pku.cn/ ) online analysis tool. Finally, we used the ESTIMATE and XCELL algorithms to estimate stromal and immune cells in tumor tissue and cast each sample to infer the underlying mechanism of cuproptosis-related lncRNAs in the tumor immune microenvironment (TIME) of HCC patients.

RESULTS

Four cuproptosis-related lncRNAs were used to construct a prognostic lncRNA signature, which was an independent factor in predicting OS in HCC patients. Kaplan-Meier curves showed significant differences in survival rates between risk subgroups (p = 0.002). At the same time, we found that the expression levels of most immune checkpoint genes increased with increasing risk scores. Tumorigenesis and immunological-related pathways were primarily enhanced in the high-risk group, as determined by GSEA. The results of drug sensitivity analysis showed that compared with patients in the high-risk group, the IC50 values of erlotinib and lapatinib were lower in patients in the low-risk group, while the opposite was true for sunitinib, paclitaxel, gemcitabine, and imatinib. We also found that elevated AL133243.2 expression was significantly associated with worse OS and disease-free survival (DFS), more advanced T stage and higher tumor grade, and reduced immune cell infiltration, suggesting that HCC patients with low AL133243.2 expression in tumor tissues may have a better response to immunotherapy.

CONCLUSION

Collectively, the cuproptosis-associated lncRNA signature can serve as an independent predictor to guide individual treatment strategies. Furthermore, AL133243.2 is a promising marker for predicting immunotherapy response in HCC patients. This data may facilitate further exploration of more effective immunotherapy strategies for HCC.

Prognostic prediction and multidimensional dissections of a macrophages M0-related gene signature in liver cancer

BACKGROUND

Liver hepatocellular carcinoma (LIHC) is the seventh most commonly diagnosed malignancy and the third leading cause of all cancer death worldwide. The undifferentiated macrophages M0 can be induced into polarized M1 and M2 to exert opposite effects in tumor microenvironment. However, the prognostic value of macrophages M0 phenotype remains obscure in LIHC.

METHODS

The transcriptome data of LIHC was obtained from TCGA database and ICGC database. 365 LIHC samples from TCGA database and 231 LIHC samples from ICGC database were finally included. Macrophages M0-related genes (MRGs) were screened by Pearson correlation analysis and univariate Cox regression analysis based on the infiltration level of Macrophages M0. LASSO regression analysis was employed to construct a prognostic signature based on MRGs, and risk scores were accordingly calculated. Then we investigated the MRGs-based prognostic signature with respects to prognostic value, clinical significance, strengthened pathways, immune infiltration, gene mutation and drug sensitivity. Furthermore, the expression pattern of MRGs in the tumor microenvironment were also detected in LIHC.

RESULTS

A ten-MRG signature was developed and clarified as independent prognostic predictors in LIHC. The risk score-based nomogram showed favorable capability in survival prediction. Several substance metabolism activities like fatty acid/amino acid metabolism were strengthened in low-risk group. Low risk group was deciphered to harbor TTN mutation-driven tumorigenesis, while TP53 mutation was dominant in high-risk group. We also ascertained that the infiltration levels of immune cells and expressions of immune checkpoints are significantly influenced by the risk score. Besides, we implied that patients in low-risk group may be more sensitive to several anti-cancer drugs. What's more important, single-cell analysis verified the expression of MRGs in the tumor microenvironment of LIHC.

CONCLUSION

Multidimensional evaluations verified the clinical utility of the macrophages M0-related gene signature to predict prognosis, assist risk decision and guide treatment strategy for patients with LIHC.