EFEMP2 indicates assembly of M0 macrophage and more malignant phenotypes of glioma

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.

Endoplasmic reticulum stress-related prognosis signature characterizes the immune landscape and predicts the prognosis of colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is characterized by high mortality and poor prognosis. Endoplasmic reticulum stress-related gene (ERSG) plays an indispensable role in the progression and immunotherapy of COAD. In this study, we evaluated the prognostic value of ERSGs in COAD.

Methods

We constructed and validated the ERSG-related prognostic signature based on public databases using univariate Cox analysis, Kaplan-Meier survival analysis, the LASSO method, and multivariate Cox analysis. In addition, TCGA-COAD, the Human Protein Atlas, and quantitative real-time PCR (q-PCR) were used to detect the mRNA and protein expressions of ERSGs in normal and cancer tissues/cells. The immunotherapeutic cohort was used to evaluate the predictive value of the ERSG signature for immunotherapeutic sensitivity.

Results

The ERSG signature, consisted of HSPA1A, SERPINA1, and DAPK1, could predict the prognosis of patients with COAD. Clinicopathologic characteristics were significantly correlated with risk scores. There were significant differences in the proportion of tumor-infiltrating immune cells, the TP53 mutation rate, the expression of immune checkpoint-related genes, and IC50 of the chemotherapeutic drugs between the low- and high-risk groups. Compared with normal tissues, the mRNA and protein expressions of three ERSGs were decreased in cancer tissues. Compared with NCM460, the mRNA levels of HSPA1A and DAPK1 were decreased in the majority of COAD cell lines, whereas the mRNA level of SERPINA1 was increased in HCT116 and SW480, and reduced in SW620. The ERSG signature could be used as a predictor of immunotherapeutic outcomes.

Conclusion

The ERSG signature has a predictive value in the prognosis and immunotherapeutic sensitivity in COAD, helping guide the personalized treatment.

Developing and validating a prognostic disulfidptosis-related signature for glioblastoma: predicting radioresistance and synergestic effect with immunotherapy

BACKGROUND

Programmed cell death (PCD) modulated radioresistance is one of the predominant causes of treatment failure in glioblastoma (GBM). Disulfidptosis, a newly discovered form of PCD, plays a crucial role in GBM progression. However, the association among disulfidptosis, radiosensitivity and radiotherapy (RT) in GBM remain unclear.

METHODS

We systematically analyzed disulfidptosis-related genes in 1075 GBM patients and constructed a disulfidptosis-related gene signature (DRS). Correlations among the DRS, patient prognosis and immune microenvironment were fully explored. The effects of DRS and EFEMP2 on radiotherapy efficacy were investigated via single cell sequencing analysis and validated via in vitro and in vivo experiments.

RESULTS

The DRS was identified as a robust and independent prognostic biomarker for GBM by multivariate Cox regression analysis, receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) in multiple cohorts. High DRS is characterized by radioresistance, and EFEMP2 was proven to be the key gene involved in this process by single cell sequencing analysis, CCK-8 assay and a clonogenic survival assay. In high-DRS patients, the cancer-immunity cycle is attenuated because the antitumor cytotoxicity of CD8+ T cells is inhibited by immune checkpoints. Preclinically, the overexpression of EFEMP2 induced radioresistance and enhancing the efficacy of programmed cell death ligand-1 (PD-L1) blockade in GL261-bearing mice. The combination of irradiation and anti-PD-L1 therapy had a synergistic effect on GBM murine models in which EFEMP2 was overexpressed.

CONCLUSION

Our study bioinformatically and experimentally reveals the molecular landscape of disulfidptosis in GBM, develops a predictive signature for predicting prognosis as well as radioresistance, and provides a synergistic treatment that combines radiotherapy with immunotherapy for radioresistant GBM patients with high DRS or EFEMP2 expression.

Establishment of an alternative splicing prognostic risk model and identification of FN1 as a potential biomarker in glioblastoma multiforme

Aberrant alternative splicing and abnormal alternative splicing events (ASEs) in glioblastoma multiforme (GBM) remain largely elusive. The prognostic-associated ASEs in GBM were identified and summarized into 123 genes using GBM and LGG datasets from ASCancer Atlas and TCGA. The eleven genes (C2, COL3A1, CTSL, EIF3L, FKBP9, FN1, HPCAL1, HSPB1, IGFBP4, MANBA, PRKAR1B) were screened to develop an alternative splicing prognostic risk score (ASRS) model through machine learning algorithms. The model was trained on the TCGA-GBM cohort and validated with four external datasets from CGGA and GEO, achieving AUC values of 0.808, 0.814, 0.763, 0.859, and 0.836 for 3-year survival rates, respectively. ASRS could be an independent prognostic factor for GBM patients (HR > 1.8 across three datasets) through multivariate Cox regression analysis. The high-risk group demonstrated poorer prognosis, elevated immune scores, increased levels of immune cell infiltration, and greater differences in drug sensitivity. We found that FN1, used for model construction, contained 4 abnormal ASEs resulting in high expression of non-canonical transcripts and the presence of premature termination codon. These abnormal ASEs may be regulated by tumour-related splicing factors according to the PPI network. Furthermore, both mRNA and protein levels of FN1 were highly expressed in GBM compared to LGG, correlating with poor prognosis in GBM. In conclusion, our findings highlight the role of ASEs in affecting the progression of GBM, and the model showed a potential application for prognostic risk of patients. FN1 may serve as a promising splicing biomarker for GBM, and mechanisms of processes of aberrant splicing need to be revealed in the future.

Integration of single-cell and bulk RNA sequencing to identify a distinct tumor stem cells and construct a novel prognostic signature for evaluating prognosis and immunotherapy in LUAD

BACKGROUND

Cancer stem cells (CSCs) are crucial for lung adenocarcinoma (LUAD). This study investigates tumor stem cell gene signatures in LUAD using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq), aiming to develop a prognostic tumor stem cell marker signature (TSCMS) model.

METHODS

LUAD scRNA-seq and RNA-seq data were analyzed. CytoTRACE software quantified the stemness score of tumor-derived epithelial cell clusters. Gene Set Variation Analysis (GSVA) identified potential biological functions in different clusters. The TSCMS model was constructed using Lasso-Cox regression, and its prognostic value was assessed through Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses. Immune infiltration was evaluated using the Cibersortx algorithm, and drug response prediction was performed using the pRRophetic package. TAF10 functional investigations in LUAD cells involved bioinformatics analysis, qRT-PCR, Western blot, immunohistochemistry, and assays for cell proliferation.

RESULTS

Seven distinct cell clusters were identified by CytoTRACE, with epithelial cell cluster 1 (Epi_C1) showing the highest stemness potential. The TSCMS model included 49 tumor stemness-related genes; high-risk patients exhibited lower immune and ESTIMATE scores and increased tumor purity. Significant differences in immune landscapes and chemotherapy sensitivity were observed between risk groups. TAF10 positively correlated with RNA expression-based stemness scores in various tumors, including LUAD. It was over-expressed in LUAD cell lines and clinical tumor tissues, with high expression linked to poor prognosis. Silencing TAF10 inhibited LUAD cell proliferation and tumor sphere formation.

CONCLUSIONS

This study demonstrates the TSCMS model's prognostic value in LUAD, reveals insights into immune infiltration and therapeutic response, and identifies TAF10 as a potential therapeutic target.

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.

CXCL8 modulates M0 macrophage proliferation and polarization to influence tumor progression in cervical cancer

Cervical cancer (CESC) presents significant clinical challenges due to its complex tumor microenvironment (TME) and varied treatment responses. This study identified undifferentiated M0 macrophages as high-risk immune cells critically involved in CESC progression. Co-culture experiments further demonstrated that M0 macrophages significantly promoted HeLa cell proliferation, migration, and invasion, underscoring their pivotal role in modulating tumor cell behavior within the TME. A nine-gene prognostic model constructed from immune gene signatures highlighted CXCL8 as a key regulator of M0 macrophage behavior. Functional experiments demonstrated that CXCL8 knockdown in M0 macrophages inhibited their proliferation, shifted polarization toward an M1-dominant phenotype, and reduced tumor-promoting M2 polarization. Co-culture experiments with CXCL8-deficient M0 macrophages further revealed a suppression of HeLa cell proliferation, migration, and invasion. These findings position M0 macrophages as central regulators within the TME and suggest that targeting pathways like CXCL8 could provide novel therapeutic strategies for improving outcomes in CESC patients.

Inhibition of fibulin-3 ameliorates periodontal inflammation through reducing M1 macrophage polarization via EGFR/PI3K/AKT pathway

BACKGROUND

This study aimed to evaluate the role of fibulin-3 (FBLN3) in macrophage polarization, its mechanism, and its effect on periodontitis.

METHODS

We conducted studies on periodontitis using both clinical samples and ligature-induced mouse periodontitis model. The inflammatory state was assessed using microcomputed tomography, hematoxylin and eosin staining, immunohistochemical staining, and immunofluorescence staining. In vitro, bone marrow-derived macrophages, and RAW 264.7 macrophages were treated with lipopolysaccharide (LPS) and interleukin (IL)-4 to induce polarization. The role of FBLN3 in macrophage polarization was investigated using overexpression plasmids or siRNAs. Furthermore, local injection of adeno-associated virus was employed to suppress FBLN3 expression in periodontal tissues.

RESULTS

FBLN3 levels were greater in periodontitis tissues. FBLN3 promoted M1 polarization and suppressed M2 polarization in macrophages. The overexpression of FBLN3 promoted M1 polarization via the EGFR/PI3K/AKT signaling pathway, an effect that the epidermal growth factor receptor (EGFR) inhibitor PD153035 reversed. Suppressing FBLN3 expression improved periodontal inflammation and reduced alveolar bone loss in periodontitis.

CONCLUSIONS

FBLN3 suppression can mitigate periodontitis by decreasing the M1 macrophage ratio. FBLN3 regulates M1 macrophage polarization through the EGFR/PI3K/AKT signaling pathway.

PLAIN LANGUAGE SUMMARY

Disruption in the collaboration between extracellular matrix (ECM) and immune system is a significant pathology in periodontitis. Macrophages are a crucial part of the immune system and have unique functions, such as polarization. Fibulin-3, an ECM protein, may play a vital role in this dynamic interplay. Fibulin-3 expression is elevated in periodontitis and is closely related to immune cell function. Inhibiting fibulin-3 can alleviate periodontitis by reducing infiltration of immune cells and M1 macrophage ratio. Furthermore, fibulin-3 promoted macrophage M1 polarization by activating the PI3K/AKT signaling pathway through EGFR binding. Our findings offer a clinically relevant rationale for immune response modulation through fibulin-3.

Upregulation of HSD11B1 promotes cortisol production and inhibits NK cell activation in pancreatic adenocarcinoma

Cortisol is a glucocorticoid hormone that has immunosuppressive function. Elevated basal cortisol levels are present in patients with some kinds of cancers, but its role in the microenvironment of pancreatic adenocarcinoma (PAAD) remains unclear. This study analyzed the expression of genes involved in cortisol generation by using high-throughput sequencing data from TCGA portal and found HSD11B1 was significantly upregulated in patients with PAAD. The correlations between HSD11B1 level and the expression of 23 immunosuppressive receptors were analyzed by Spearman's correlation analysis. The function of HSD11B1 was examined in primary NK cells and PAAD cell lines. The levels of cortisol in medium and cell lysates were detected by ELISA. In vitro killing assay was used to evaluate the cytotoxicity of NK cells. Cell surface levels of CD96, Tim-3, PD-1, TIGIT, CTLA-4, NKp46, NKp30, NKD2G and LFA-1A, and intracellular levels of CD107a and IFN-γ were examined by flow cytometry. We observed that patients with higher HSD11B1 level had shorter survival time. HSD11B1 is positively correlated with the mRNA levels of 11 immunosuppressive receptors in PAAD. Higher HSD11B1 level relates to reduced abundance of activated NK cells in the tumors. HSD11B1 overexpressed NK cells exhibit exhausted phenotype with increased cortisol production, reduced viability, and reduced cytotoxicity against cancer cells. Overexpression of HSD11B1 did not change the viability of tumor cells but upregulated cortisol production. Targeting HSD11B1 by a specific inhibitor improved the NK cells responsiveness. In conclusion, HSD11B1 is upregulated in patients with PAAD, and higher HSD11B1 level is related to poor prognosis. Upregulation of HSD11B1 in NK and tumor cells increased the production and secretion of cortisol and induces NK cell exhaustion.

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.

Insights of immune cell heterogeneity, tumor-initiated subtype transformation, drug resistance, treatment and detecting technologies in glioma microenvironment

BACKGROUND

With the gradual understanding of glioma development and the immune microenvironment, many immune cells have been discovered. Despite the growing comprehension of immune cell functions and the clinical application of immunotherapy, the precise roles and characteristics of immune cell subtypes, how glioma induces subtype transformation of immune cells and its impact on glioma progression have yet to be understood.

AIM OF THE REVIEW

In this review, we comprehensively center on the four major immune cells within the glioma microenvironment, particularly neutrophils, macrophages, lymphocytes, myeloid-derived suppressor cells (MDSCs), and other significant immune cells. We discuss (1) immune cell subtype markers, (2) glioma-induced immune cell subtype transformation, (3) the mechanisms of each subtype influencing chemotherapy resistance, (4) therapies targeting immune cells, and (5) immune cell-associated single-cell sequencing. Eventually, we identified the characteristics of immune cell subtypes in glioma, comprehensively summarized the exact mechanism of glioma-induced immune cell subtype transformation, and concluded the progress of single-cell sequencing in exploring immune cell subtypes in glioma.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In conclusion, we have analyzed the mechanism of chemotherapy resistance detailly, and have discovered prospective immunotherapy targets, excavating the potential of novel immunotherapies approach that synergistically combines radiotherapy, chemotherapy, and surgery, thereby paving the way for improved immunotherapeutic strategies against glioma and enhanced patient outcomes.

The extracellular matrix protein EFEMP2 is involved in the response to VHSV infection in the olive flounder Paralichthys olivaceus

The EGF-containing fibulin-like extracellular matrix protein 2 (EFEMP2) is involved in connective tissue development, elastic fiber formation, and tumor growth. In this study, we characterized the cDNA of EFEMP2 (PoEFEMP2), a member of the fibulin family of ECM proteins, in the olive flounder Paralichthys olivaceus. The coding region of PoEFEMP2 encodes a protein that contains six calcium-binding EGF-like (EGF-CA) domains and four complement Clr-like EGF-like (cEGF) domains. PoEFEMP2 shows 67.51-96.77 % similarities to orthologs in a variety of fish species. PoEFEMP2 mRNA was detected in all tissues examined; the highest levels of PoEFEMP2 mRNA expression were observed in the heart, testis, ovary and muscle. The PoEFEMP2 mRNA level increases during early development. In addition, the PoEFEMP2 mRNA level increased at 3 h post-infection (hpi) and decreased from 6 to 48 hpi in flounder Hirame natural embryo (HINAE) cells infected with viral hemorrhagic septicemia virus (VHSV). Disruption of PoEFEMP2 using the clustered regularly interspaced short palindromic repeats/CRISPR-associated-9 (CRISPR/Cas9) system resulted in a significant upregulation of VHSV G mRNA levels and immune-related genes expression in knockout cells. These findings implicate PoEFEMP2 in antiviral responses in P. olivaceus.

Discovery of a Therapeutic Agent for Glioblastoma Using a Systems Biology-Based Drug Repositioning Approach

Glioblastoma (GBM), a highly malignant tumour of the central nervous system, presents with a dire prognosis and low survival rates. The heterogeneous and recurrent nature of GBM renders current treatments relatively ineffective. In our study, we utilized an integrative systems biology approach to uncover the molecular mechanisms driving GBM progression and identify viable therapeutic drug targets for developing more effective GBM treatment strategies. Our integrative analysis revealed an elevated expression of CHST2 in GBM tumours, designating it as an unfavourable prognostic gene in GBM, as supported by data from two independent GBM cohorts. Further, we pinpointed WZ-4002 as a potential drug candidate to modulate CHST2 through computational drug repositioning. WZ-4002 directly targeted EGFR (ERBB1) and ERBB2, affecting their dimerization and influencing the activity of adjacent genes, including CHST2. We validated our findings by treating U-138 MG cells with WZ-4002, observing a decrease in CHST2 protein levels and a reduction in cell viability. In summary, our research suggests that the WZ-4002 drug candidate may effectively modulate CHST2 and adjacent genes, offering a promising avenue for developing efficient treatment strategies for GBM patients.

Glioma-Stem-Cell-Derived Exosomes Remodeled Glioma-Associated Macrophage via NEAT1/miR-125a/STAT3 Pathway

Glioblastoma (GBM), as the most common primary brain tumor, usually results in an extremely poor prognosis, in which glioma stem cells (GSCs) and their immunosuppressive microenvironment prominently intervene in the resistance to radiotherapy and chemotherapy that directly leads to tumor recurrence and shortened survival time. The specific mechanism through which exosomes generated from GSCs support the creation of an immunosuppressive microenvironment remains unknown, while it is acknowledged to be engaged in intercellular communication and the regulation of the glioma immunosuppressive microenvironment. The elevated expression of LncRNA-NEAT1 was found in glioma cells after radiotherapy, chemotherapy, and DNA damage stimulation, and NEAT1 could promote the malignant biological activities of GSCs. Emerging evidence suggests that lncRNAs may reply to external stimuli or DNA damage by playing a role in modulating different aspects of tumor biology. Our study demonstrated a promotive role of the carried NEAT1 by GSC-derived exosomes in the polarization of M2-like macrophages. Further experiments demonstrated the mediative role of miR-125a and its target gene STAT3 in NEAT1-induced polarization of M2-like macrophages that promote glioma progression. Our findings elucidate the mechanism by which GSCs influence the polarization of M2-like macrophages through exosomes, which may contribute to the formation of immunosuppressive microenvironments. Taken together, our study reveals the miR-125a-STAT3 pathway through which exosomal NEAT1 from treatment-resistant GSCs contributes to M2-like macrophage polarization, indicating the potential of exosomal NEAT1 for treating glioma.

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.

A novel disulfidptosis-related lncRNAs signature for predicting survival and immune response in hepatocellular carcinoma

The accumulation of intracellular disulfides induces a novel and unique form of metabolic-related cell death known as disulfidptosis. A previous study revealed the prognostic value of a risk model of disulfidptosis-related genes in hepatocellular carcinoma (HCC). However, to date, no studies have investigated the relationship between disulfidptosis-related long non-coding RNAs (DRLs) and HCC. In this study, we collected and analyzed RNA sequencing data from 370 HCC samples to explore the DRLs in the tumorigenesis and development of HCC. By employing Lasso Cox regression and multivariate Cox regression analyses, we identified five prognostic DRLs, which were used to construct a prognostic signature. The signature was subsequently validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, Cox regression analyses, nomograms, and calibration curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed, revealing that the DRLs signature was associated with HCC and several cancer-related pathways. Furthermore, the DRLs signature showed correlations with the infiltration of M0 and M1 macrophages, immune-related functions, and multiple immune checkpoints, including PDCD1, LAG3, CTLA4, TIGIT, CD47, and others. Analysis using the tumor immune dysfunction and exclusion (TIDE) approach demonstrated that the DRLs signature could predict the response to immunotherapy. Finally, we screened potential chemotherapy drugs that could sensitize HCC. In conclusion, our novel DRLs signature provides valuable insights into predicting patient survival and immunotherapy responses.

PVT1 promotes proliferation and macrophage immunosuppressive polarization through STAT1 and CX3CL1 regulation in glioblastoma multiforme

AIMS

This study aimed to investigate the role of plasmacytoma variant translocation 1 (PVT1), a long non-coding RNA, in glioblastoma multiforme (GBM) and its impact on the tumor microenvironment (TME).

METHODS

We assessed aberrant PVT1 expression in glioma tissues and its impact on GBM cell growth in vitro and in vivo. Additionally, we investigated PVT1's role in influencing glioma-associated macrophages. To understand PVT1's role in cell growth and the immunosuppressive TME, we performed a series of comprehensive experiments.

RESULTS

PVT1 was overexpressed in GBM due to copy number amplification, correlating with poor prognosis. Elevated PVT1 promoted GBM cell proliferation, while its downregulation inhibited growth in vitro and in vivo. PVT1 inhibited type I interferon-stimulated genes (ISGs), with STAT1 as the central hub. PVT1 correlated with macrophage enrichment and regulated CX3CL1 expression, promoting recruitment and M2 phenotype polarization of macrophages. PVT1 localized to the cell nucleus and bound to DHX9, enriching at the promoter regions of STAT1 and CX3CL1, modulating ISGs and CX3CL1 expression.

CONCLUSION

PVT1 plays a significant role in GBM, correlating with poor prognosis, promoting cell growth, and shaping an immunosuppressive TME via STAT1 and CX3CL1 regulation. Targeting PVT1 may hold therapeutic promise for GBM patients.

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.

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.

EFEMP2 upregulates PD-L1 expression via EGFR/ERK1/2/c-Jun signaling to promote the invasion of ovarian cancer cells

BACKGROUND

Fibulin-like extracellular matrix protein 2 (EFEMP2) has been reported to be related to the progression of various cancers. We have previously reported that EFEMP2 was highly expressed in ovarian cancer and was strongly associated with poor prognosis in patients. This study intends to further explore its interacting proteins and possible downstream signaling pathways.

METHOD

The expression of EFEMP2 was detected by RT-qPCR, ICC and western blot in 4 kinds of ovarian cancer cells with different migration and invasion ability. Cell models with strong or weak EFEMP2 expression were constructed by lentivirus transfection. The effects of the down-regulation and up-regulation of EFEMP2 on the biological behavior of ovarian cancer cells were studied through in-vitro and in-vivo functional tests. The phosphorylation pathway profiling array and KEGG database analyses identified the downstream EGFR/ERK1/2/c-Jun signaling pathway and the programmed death-1 (PD-L1) pathway enrichment. Additionally, the protein interaction between EFEMP2 and EGFR was detected by immunoprecipitation.

RESULT

EFEMP2 was positively correlated with the invasion ability of ovarian cancer cells, its down-regulation inhibited the migrative, invasive and cloning capacity of cancer cells in vitro and suppressed the tumor proliferation and intraperitoneal diffusion in vivo, while its up-regulation did the opposite. Moreover, EFEMP2 could bind to EGFR to induce PD-L1 regulation in ovarian cancer, which was caused by the activation of EGFR/ERK1/2/c-Jun signaling. Similar to EFEMP2, PD-L1 was also highly expressed in aggressive cells and had the ability to promote the invasion and metastasis of ovarian cancer cells both in vitro and in vivo, and PD-L1 upregulation was partly caused by EFEMP2 activation. Afatinib combined with trametinib had an obvious effect of inhibiting the intraperitoneal diffusion of ovarian cancer cells, especially in the group with low expression of EFEMP2, while overexpression of PD-L1 could reverse this phenomenon.

CONCLUSION

EFEMP2 could bind to EGFR to activate ERK1/2/c-Jun pathway and regulate PD-L1 expression, furthermore PD-L1 was extremely essential for EFEMP2 to promote ovarian cancer cells invasion and dissemination in vitro and in vivo. Targeted therapy against the source gene EFEMP2 is our future research direction, which may better inhibit the invasion and metastasis of ovarian cancer cells.

High Expression of CDCA7 in the Prognosis of Glioma and Its Relationship with Ferroptosis and Immunity

CDCA7 is a copy number amplification gene that promotes tumorigenesis. However, the clinical relevance and potential mechanisms of CDCA7 in glioma are unclear. CDCA7 expression level data were obtained from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases, and the enriched genes and related signaling pathways were explored. Data on genes in CDCA7-related signaling pathways and nine marker genes of ferroptosis were retrieved and a protein-protein interaction (PPI) network analysis was performed. The correlation of CDCA7 to ferroptosis and tumor infiltration of 22 kinds of human immune cells and the association between CDCA7 and immune checkpoint molecules were analyzed. CDCA7 was significantly increased in gliomas in comparison to healthy tissues. Gene Ontology (GO) and gene set enrichment analysis (GSEA) revealed the impact of CDCA7 expression on multiple biological processes and signaling pathways. CDCA7 may affect ferroptosis by interacting with genes in the cell cycle pathway and P53 pathway. The increase in CDCA7 was positively correlated with multiple ferroptosis suppressor genes and genes involved in tumor-infiltrating immune cells and immune checkpoint molecules in glioma. CDCA7 can be a new prognostic factor for glioma, which is closely related to ferroptosis, tumor immune cell infiltration, and immune checkpoint.

Identification of A novel anoikis-related genes-based signature for non-small cell lung cancer

The prognostic value of anoikis in NSCLC and its mechanism in tumorigenesis and progress have not been fully elucidated. This study aimed to reveal the correlation between anoikis-related genes (ARGs) and tumor prognosis, to reveal molecular and immune features, and to evaluate the anticancer drug sensitivity and the efficacy of immunotherapy of NSCLC. ARGs were selected from both the GeneCards and Harmonizome databases and then were intersected with the Cancer Genome Atlas (TCGA) database by differential expression analysis, followed by functional analysis of the target ARGs. An ARGs-based prognostic signature was constructed using LASSO (least absolute shrinkage and selection operator) Cox regression analysis; Kaplan-Meier analysis, univariant and multivariant Cox analysis were used to validate the value of this model in NSCLC prognosis. Differential analyses on molecular and immune landscapes were applied in the model. Anticancer drug sensitivity and efficacy in immune-checkpoint inhibitors (ICI) therapy were analyzed. A total of 509 ARGs and 168 differentially expressed ARGs in NSCLC were generated. Functional analysis revealed enrichment in extracolonic apoptotic signaling pathway, collagen-containing ECM, and integrin binding, and indicated an association with the PI3K-Akt signaling pathway. Subsequently, a 14-genes signature was generated. The high-risk group had a worse prognosis, with higherM0 and M2 macrophage infiltration, and fewer CD8 T-cells and T follicular helper (TFH) cells. The high-risk group had higher expression of immune checkpoint genes, HLA-I genes, and higher TIDE scores than the low-risk group, leading to less benefit of ICI therapy. Additionally, an Immunohistochemical staining comparison revealed that FADD was highly expressed in tumor tissue, compared to normal tissue, consistent with the previous results.

Development and validation of an inflammatory response-related prognostic model and immune infiltration analysis in glioblastoma

Background

Despite receiving standard treatment, the prognosis of glioblastoma (GBM) patients is still poor. Considering the heterogeneity of each patient, it is imperative to identify reliable risk model that can effectively predict the prognosis of each GBM patient to guide the personalized treatment.

Methods

Transcriptomic gene expression profiles and corresponding clinical data of GBM patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Inflammatory response-related genes were extracted from Gene Set Enrichment Analysis (GSEA) website. Univariate Cox regression analysis was used for prognosis-related inflammatory genes (P<0.05). A polygenic prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Validation was performed through CGGA cohort. Overall survival (OS) was compared by Kaplan-Meier analysis. A nomogram was plotted to accurately predict the prognosis for each patient. GSEA was used for the pathway enrichment analysis. The single sample GSEA (ssGSEA) algorithm was implemented to conduct the immune infiltration analysis. The potential role of oncostatin M receptor (OSMR) in GBM was investigated through the in vitro experiment.

Results

A prognostic risk model consisting of 4 genes (PTPRN, OSMR, MYD88, and EFEMP2) was developed. GBM patients in the high-risk group had worse OS. The time-dependent ROC curves showed an area under the curve (AUC) of 0.782, 0.765, and 0.784 for 1-, 2-, and 3-year survival in TCGA cohort, while the AUC in the CGGA cohort was 0.589, 0.684, and 0.785 at 1, 2, and 3 years, respectively. The risk score, primary-recurrent-secondary (PRS) type, and isocitrate dehydrogenase (IDH) mutation could predict the prognosis of GBM patients well. The nomogram accurately predicted the 1-, 2-, and 3-year OS for each patient. Immune cell infiltration was associated with the risk score and the model could predict immunotherapy responsiveness. The expression of the prognostic gene was correlated with the sensitivity to antitumor drugs. Interference of OSMR inhibited proliferation and migration and promoted apoptosis of GBM cells.

Conclusions

The prognostic model based on 4 inflammatory response-related genes had reliable predictive power to effectively predict clinical outcome in GBM patients and provided the guide for the personalized treatment.

Integrated analysis of genome-wide DNA methylation and cancer-associated fibroblasts identified prognostic biomarkers and immune checkpoint blockade in lower grade gliomas

Background

Cancer-associated fibroblasts (CAFs) are vital components of prominent cellular components in lower-grade gliomas (LGGs) that contribute to LGGs' progression, treatment resistance, and immunosuppression. Epigenetic modification and immunity have significant implications for tumorigenesis and development.

Methods

We combined aberrant methylation and CAFs abundances to build a prognostic model and the impact on the biological properties of LGGs. Grouping based on the median CAFs abundances score of samples in the TCGA-LGGs dataset, differentially expressed genes and aberrantly methylated genes were combined for subsequent analysis.

Results

We identified five differentially methylated and expressed genes (LAT32, SWAP70, GSAP, EMP3, and SLC2A10) and established a prognostic gene signature validated in the CGGA-LGGs dataset. Immunohistochemistry (IHC) and in vitro tests were performed to verify these expressions. The high-risk group increased in tumor-promoting immune cells and tumor mutational burden. Notably, risk stratification had different ICB sensitivities in LGGs, and there were also significant sensitivity differences for temozolomide and the other three novel chemotherapeutic agents.

Conclusion

Our study reveals characteristics of CAFs in LGGs, refines the direct link between epigenetics and tumor stroma, and might provide clinical implications for guiding tailored anti-CAFs therapy in combination with immunotherapy for LGGs patients.

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.

SLC22A8: An indicator for tumor immune microenvironment and prognosis of ccRCC from a comprehensive analysis of bioinformatics

Clear cell renal cell carcinoma (ccRCC) is one of the most common renal malignancies worldwide. SLC22A8 plays a key role in renal excretion of organic anions. However, its role in ccRCC remains unclear; therefore, this study aimed to elucidate the relationship between SLC22A8 and ccRCC. The The Cancer Genome Atlas-kidney renal clear cell carcinoma cohort was included in this study. The Wilcoxon signed-rank test and logistic regression were used to analyze the relationship between SLC22A8 expression and clinicopathological characteristics. Multifactorial analysis and Kaplan-Meier survival curves were adopted for correlation between SLC22A8 expression and clinicopathological parameters and overall survival. Utilizing the UALCAN database, the correlation of the expression levels of SLC22A8 DNA methylation in ccRCC was explored. Immunological characterization of SLC22A8 regarding the ccRCC tumor microenvironment was carried out by the single sample Gene Set Enrichment Analysis algorithm and the CIBERSORT algorithm. With the CellMiner database, the analysis of the association between SLC22A8 gene expression and drug sensitivity was further performed. Eventually, gene ontology and Kyoto Encyclopedia of Gene and Genome enrichment analyses were applied to identify the functional and signaling pathways involved in SLC22A8. SLC22A8 expression is associated with age, grade, stage, and tumor status. SLC22A8 protein expression levels, phosphorylated protein levels, and DNA methylation expression levels were lower in ccRCC tissues than in normal tissues, and low methylation levels predicted poor overall survival. Comprehensive analysis of tumor immune infiltration and the tumor microenvironment indicated a higher level of overall immunity in the SLC22A8 low expression group. Gene Enrichment Analysis results showed that low expression of SLC22A8 was associated with immune pathways, such as phagocytosis recognition and humoral immune response. SLC22A8 expression was significantly correlated with survival and immune infiltration in ccRCC and can be used as a prognostic biomarker for ccRCC.

A lipid metabolism-related risk signature for patients with gliomas constructed with TCGA and CGGA data

Lipid metabolism affects cell proliferation, differentiation, membrane homeostasis and drug resistance. An in-depth exploration of lipid metabolism in gliomas might provide a novel direction for gliomas treatment. A lipid metabolism-related risk signature was constructed in our study to assess the prognosis of patients with gliomas. Lipid metabolism-related genes were extracted. Differentially expressed genes (DEGs) were screened, and a risk signature was built. The ability of the risk signature to predict the outcomes of patients with gliomas was assessed using the log-rank test and Cox regression analysis. The relationships between immunological characteristics, drug sensitivity and the risk score were evaluated, and the risk-related mechanisms were also estimated. Twenty lipid metabolism-related DEGs associated with the patient prognosis were included in the risk signature. The survival rate of high-risk patients was worse than that of low-risk patients. The risk score independently predicted the outcomes of patients. Immunological parameters, drug sensitivity, immunotherapy benefits, and numerous molecular mechanisms were significantly associated with the risk score. A lipid metabolism-related risk signature might effectively assess the prognosis of patients with gliomas. The risk score might guide individualized treatment and further clinical decision-making for patients with gliomas.

Classification of colon adenocarcinoma based on immunological characterizations: Implications for prognosis and immunotherapy

Accurate immune molecular typing is pivotal for screening out patients with colon adenocarcinoma (COAD) who may benefit from immunotherapy and whose tumor microenvironment (TME) was needed for reprogramming to beneficial immune-mediated responses. However, little is known about the immune characteristic of COAD. Here, by calculating the enrichment score of immune characteristics in three online COAD datasets (TCGA-COAD, GSE39582, and GSE17538), we identified 17 prognostic-related immune characteristics that overlapped in at least two datasets. We determined that COADs could be stratified into three immune subtypes (IS1-IS3), based on consensus clustering of these 17 immune characteristics. Each of the three ISs was associated with distinct clinicopathological characteristics, genetic aberrations, tumor-infiltrating immune cell composition, immunophenotyping (immune "hot" and immune "cold"), and cytokine profiles, as well as different clinical outcomes and immunotherapy/therapeutic response. Patients with the IS1 tumor had high immune infiltration but immunosuppressive phenotype, IS3 tumor is an immune "hot" phenotype, whereas those with the IS2 tumor had an immune "cold" phenotype. We further verified the distinct immune phenotype of IS1 and IS3 by an in-house COAD cohort. We propose that the immune subtyping can be utilized to identify COAD patients who will be affected by the tumor immune microenvironment. Furthermore, the ISs may provide a guide for personalized cancer immunotherapy and for tumor prognosis.

A Differentiation-Related Gene Prognostic Index Contributes to Prognosis and Immunotherapy Evaluation in Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common gastrointestinal tumor with a poor prognosis, which is associated with poor differentiation of tumor cells. However, the potential value of cell differentiation-related molecules in predicting the benefit and prognosis of immune checkpoint inhibitors (ICI) therapy remains unknown. Herein, to investigate the differentiation trajectory of HCC cells and their clinical significance, a differentiation-related gene prognostic index (DRGPI) based on HCC differentiation-related genes (HDRGs) was constructed to elucidate the immune characteristics and therapeutic benefits of ICI in the HCC subgroup defined by DRGPI. Single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from four HCC samples were integrated for bioinformatics analysis. Then, PON1, ADH4, SQSTM1, HSP90AA1, and STMN1 were screened out to construct a DRGPI. More intriguingly, RT-qPCR validation of the expression of these genes yielded consistent results with the TCGA database. Next, the risk scoring (RS) constructed based on DRGPI suggested that the overall survival (OS) of the DRGPI-high patients was significantly worse than that of the DRGPI-low patients. A nomogram was constructed based on DRGPI-RS and clinical characteristics, which showed strong predictive performance and high accuracy. The comprehensive results indicated that a low DRGPI score was associated with low TP53 mutation rates, high CD8 T cell infiltration, and more benefit from ICI therapy. Homoplastically, the high DRGPI score reflected the opposite results. Taken together, our study highlights the significance of HCC cell differentiation in predicting prognosis, indicating immune characteristics, and understanding the therapeutic benefits of ICI, and suggests that DRGPI is a valuable prognostic biomarker for HCC.

An M0 macrophage-related prognostic model for hepatocellular carcinoma

BACKGROUND

The role of M0 macrophages and their related genes in the prognosis of hepatocellular carcinoma (HCC) remains poorly characterized.

METHODS

Multidimensional bioinformatic methods were used to construct a risk score model using M0 macrophage-related genes (M0RGs).

RESULTS

Infiltration of M0 macrophages was significantly higher in HCC tissues than in normal liver tissues (P = 2.299e-07). Further analysis revealed 35 M0RGs that were associated with HCC prognosis; two M0RGs (OLA1 and ATIC) were constructed and validated as a prognostic signature for overall survival of patients with HCC. Survival analysis revealed the positive relationship between the M0RG signature and unfavorable prognosis. Correlation analysis showed that this risk model had positive associations with clinicopathological characteristics, somatic gene mutations, immune cell infiltration, immune checkpoint inhibitor targets, and efficacy of common drugs.

CONCLUSIONS

The constructed M0RG-based risk model may be promising for the clinical prediction of prognoses and therapeutic responses in patients with HCC.

Characterization of stem cell landscape and identification of stemness-relevant prognostic gene signature to aid immunotherapy in colorectal cancer

Background

It is generally accepted that colorectal cancer (CRC) originates from cancer stem cells (CSCs), which are responsible for CRC progression, metastasis and therapy resistance. The high heterogeneity of CSCs has precluded clinical application of CSC-targeting therapy. Here, we aimed to characterize the stemness landscapes and screen for certain patients more responsive to immunotherapy.

Methods

Twenty-six stem cell gene sets were acquired from StemChecker database. Consensus clustering algorithm was applied for stemness subtypes identification on 1,467 CRC samples from TCGA and GEO databases. The differences in prognosis, tumor microenvironment (TME) components, therapy responses were evaluated among subtypes. Then, the stemness-risk model was constructed by weighted gene correlation network analysis (WGCNA), Cox regression and random survival forest analyses, and the most important marker was experimentally verified.

Results

Based on single-sample gene set enrichment analysis (ssGSEA) enrichments scores, CRC patients were classified into three subtypes (C1, C2 and C3). C3 subtype exhibited the worst prognosis, highest macrophages M0 and M2 infiltrations, immune and stromal scores, and minimum sensitivity to immunotherapies, but was more sensitive to drugs like Bosutinib, Docetaxel, Elesclomol, Gefitinib, Lenalidomide, Methotrexate and Sunitinib. The turquoise module was identified by WGCNA that it was most positively correlated with C3 but most negatively with C2, and five hub genes in turquoise module were identified for stemness model construction. CRC patients with higher stemness scores exhibited worse prognosis, more immunosuppressive components in TME and lower immunotherapeutic responses. Additionally, the model’s immunotherapeutic prediction efficacy was further confirmed from two immunotherapy cohorts (anti-PD-L1 in IMvigor210 cohort and anti-PD-1 in GSE78220 cohort). Mechanistically, Gene Set Enrichment Analysis (GSEA) results revealed high stemness score group was enriched in interferon gamma response, interferon alpha response, P53 pathway, coagulation, apoptosis, KRAS signaling upregulation, complement, epithelial–mesenchymal transition (EMT) and IL6-mediated JAK-STAT signaling gene sets.

Conclusions

Our study characterized three stemness-related subtypes with distinct prognosis and TME patterns in CRC patients, and a 5-gene stemness-risk model was constructed by comprehensive bioinformatic analyses. We suggest our stemness model has prospective clinical implications for prognosis evaluation and might facilitate physicians selecting prospective responders for preferential use of current immune checkpoint inhibitors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-022-02913-0.

EPHX2 Inhibits Colon Cancer Progression by Promoting Fatty Acid Degradation

Tumor cells use metabolic reprogramming to keep up with the need for bioenergy, biosynthesis, and oxidation balance needed for rapid tumor division. This phenomenon is considered a marker of tumors, including colon cancer (CRC). As an important pathway of cellular energy metabolism, fatty acid metabolism plays an important role in cellular energy supply and oxidation balance, but presently, our understanding of the exact role of fatty acid metabolism in CRC is limited. Currently, no lipid metabolism therapy is available for the treatment of CRC. The establishment of a lipidmetabolism model regulated by oncogenes/tumor suppressor genes and associated with the clinical characteristics of CRC is necessary to further understand the mechanism of fatty acid metabolism in CRC. In this study, through multi-data combined with bioinformatic analysis and basic experiments, we introduced a tumor suppressor gene, EPHX2, which is rarely reported in CRC, and confirmed that its inhibitory effect on CRC is related to fatty acid degradation.

Ferroptosis-Related Gene Contributes to Immunity, Stemness and Predicts Prognosis in Glioblastoma Multiforme

Ferroptosis, a recently discovered regulated programmed cell death, is associated with tumorigenesis and progression in glioblastoma. Based on widely recognized ferroptosis-related genes (FRGs), the regulation of ferroptosis patterns and corresponding characteristics of immune infiltration of 516 GBM samples with GSE13041, TCGA-GBM, and CGGA-325 were comprehensively analyzed. Here, we revealed the expression, mutations, and CNV of FRGs in GBM. We identified three distinct regulation patterns of ferroptosis and found the hub genes of immunity and stemness among DEGs in three patterns. A prognostic model was constructed based on five FRGs and verified at the mRNA and protein level. The risk score can not only predict the prognosis but also the degree of immune infiltration and ICB responsiveness by functional annotation. The overall assessment of FRGs in GBM patients will guide the direction of improved research and develop new prognostic prediction tools.

Identification of an IL-4-Related Gene Risk Signature for Malignancy, Prognosis and Immune Phenotype Prediction in Glioma

Background: Emerging molecular and genetic biomarkers have been introduced to classify gliomas in the past decades. Here, we introduced a risk signature based on the cellular response to the IL-4 gene set through Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Methods: In this study, we provide a bioinformatic profiling of our risk signature for the malignancy, prognosis and immune phenotype of glioma. A cohort of 325 patients with whole genome RNA-seq expression data from the Chinese Glioma Genome Atlas (CGGA) dataset was used as the training set, while another cohort of 667 patients from The Cancer Genome Atlas (TCGA) dataset was used as the validating set. The LASSO model identified a 10-gene signature which was considered as the optimal model. Results: The signature was confirmed to be a good predictor of clinical and molecular features involved in the malignancy of gliomas. We also identified that our risk signature could serve as an independently prognostic biomarker in patients with gliomas (p < 0.0001). Correlation analysis showed that our risk signature was strongly correlated with the Tregs, M0 macrophages and NK cells infiltrated in the microenvironment of glioma, which might be a supplement to the existing incomplete innate immune mechanism of glioma phenotypes. Conclusions: Our IL-4-related gene signature was associated with more aggressive and immunosuppressive phenotypes of gliomas. The risk score could predict prognosis independently in glioma, which might provide a new insight for understanding the IL-4 involved mechanism of gliomas.

A Novel Immune-Gene Pair Signature Revealing the Tumor Microenvironment Features and Immunotherapy Prognosis of Muscle-Invasive Bladder Cancer

Immunotherapy has been a milestone for muscle-invasive bladder cancer (MIBC), but only a small portion of patients can benefit from it. Therefore, it is crucial to develop a robust individualized immune-related signature of MIBC to identify patients potentially benefiting from immunotherapy. The current study identified patients from the Cancer Genome Atlas (TCGA) and immune genes from the ImmPort database, and used improved data analytical methods to build up a 45 immune-related gene pair signature, which could classify patients into high-risk and low-risk groups. The signature was then independently validated by a Gene Expression Omnibus (GEO) dataset and IMvigor210 data. The subsequent analysis confirmed the worse survival outcomes of the high-risk group in both training (p < 0.001) and validation cohorts (p = 0.018). A signature-based risk score was proven to be an independent risk factor of overall survival (p < 0.001) and could predict superior clinical net benefit compared to other clinical factors. The CIBERSORT algorithm revealed the low-risk group had increased CD8⁺ T cells plus memory-activated CD4⁺ T-cell infiltration. The low-risk group also had higher expression of PDCD1 (PD-1), CD40, and CD27, and lower expression of CD276 (B7-H3) and PDCD1LG2 (PD-L2). Importantly, IMvigor210 data indicated that the low-risk group had higher percentage of “inflamed” phenotype plus less “desert” phenotype, and the survival outcomes were significantly better for low-risk patients after immunotherapy (p = 0.014). In conclusion, we proposed a novel and promising prognostic immune-related gene pair (IRGP) signature of MIBC, which could provide us a panoramic view of the tumor immune microenvironment of MIBC and independently identify MIBC patients who might benefit from immunotherapy.

Therapeutic Targets in Diffuse Midline Gliomas-An Emerging Landscape

Simple Summary

Diffuse midline gliomas (DMGs) remain one of the most devastating childhood brain tumour types, for which there is currently no known cure. In this review we provide a summary of the existing knowledge of the molecular mechanisms underlying the pathogenesis of this disease, highlighting current analyses and novel treatment propositions. Together, the accumulation of these data will aid in the understanding and development of more effective therapeutic options for the treatment of DMGs.

Abstract

Diffuse midline gliomas (DMGs) are invariably fatal pediatric brain tumours that are inherently resistant to conventional therapy. In recent years our understanding of the underlying molecular mechanisms of DMG tumorigenicity has resulted in the identification of novel targets and the development of a range of potential therapies, with multiple agents now being progressed to clinical translation to test their therapeutic efficacy. Here, we provide an overview of the current therapies aimed at epigenetic and mutational drivers, cellular pathway aberrations and tumor microenvironment mechanisms in DMGs in order to aid therapy development and facilitate a holistic approach to patient treatment.

The roles of ferroptosis regulatory gene SLC7A11 in renal cell carcinoma: A multi-omics study

Background

The ferroptosis inhibitory gene Solute carrier family 7 member 11 (SLC7A11) provides a new strategy for anticancer treatment. However, its function in renal cell carcinoma (RCC) remains elusive.

Methods

The expression and somatic mutation information of SLC7A11 in RCC samples were determined using The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), Oncomine, and cBioPortal databases. The prognostic value of SLC7A11 was assessed through survival analysis, Receiver operating characteristic curve (ROC) analysis, independent prognostic analysis, clinical subgroup analysis, and nomogram. Its prognostic value was also validated in the ICGC and GSE29607 cohorts. Gene set enrichment analysis (GSEA) was employed to investigate the effects of SLC7A11 on multiple metabolic pathways. The CIBERSORT algorithm and single‐sample gene set enrichment analysis (ssGSEA) method were applied to evaluate the effects of SLC7A11 on the tumor immune microenvironment (TIM). SLC7A11’s therapeutic correlations were analyzed using the GSE87121, GSE67501, and GSDC datasets. Finally, the biofunctions of SLC7A11 in renal cancer cells and ferroptosis were ascertained by MTT, wound healing, transwell, and western blot assays.

Results

Through multiple datasets, SLC7A11 was found to be markedly upregulated in RCC. In terms of prognosis, SLC7A11 overexpression conferred a worse prognosis and was identified as an independent prognostic factor. Its prognostic value was validated in ICGC cohort. Moreover, high SL7CA11 expression could stimulate nucleotides, fatty acids, and amino acid metabolism to meet the proliferative consumption of tumor cells. As for the immune effect, SLC7A11 suppressed antitumor immunity by reducing the abundances of CD8+ T and NK cells. Regarding the therapeutic response, SLC7A11 expression was not correlated with the sensitivities of most chemotherapy and targeted drugs. Finally, SLC7A11 promoted the proliferation, migration, and invasion of renal cancer cells by enhancing GPX4 output, which in turn inhibits ferroptosis.

Conclusions

SLC7A11 not only deeply influences RCC prognosis and TIM, but also promotes RCC progression by inhibiting ferroptosis and inducing metabolic reprogramming. In addition, SLC7A11 weakly affects the therapeutic effect and sensitivities of multiple chemotherapy and targeted drugs.

Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

Background

Enhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.

Methods

Gene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.

Results

A total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8⁺ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.

Conclusion

Our results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

Macrophages/Microglia in the Glioblastoma Tumor Microenvironment

The complex interaction between glioblastoma and its microenvironment has been recognized for decades. Among various immune profiles, the major population is tumor-associated macrophage, with microglia as its localized homolog. The present definition of such myeloid cells is based on a series of cell markers. These good sentinel cells experience significant changes, facilitating glioblastoma development and protecting it from therapeutic treatments. Huge, complicated mechanisms are involved during the overall processes. A lot of effort has been dedicated to crack the mysterious codes in macrophage/microglia recruiting, activating, reprogramming, and functioning. We have made our path. With more and more key factors identified, a lot of new therapeutic methods could be explored to break the ominous loop, to enhance tumor sensitivity to treatments, and to improve the prognosis of glioblastoma patients. However, it might be a synergistic system rather than a series of clear, stepwise events. There are still significant challenges before the light of truth can shine onto the field. Here, we summarize recent advances in this field, reviewing the path we have been on and where we are now.