DEPDC1B: A novel tumor suppressor gene associated with immune infiltration in colon adenocarcinoma.

BACKGROUND: Recent research indicates a positive correlation between DEP structural domain-containing 1B (DEPDC1B) and the cell cycle in various tumors. However, the role of DEPDC1B in the infiltration of the tumor immune microenvironment (TIME) remains unexplored. METHODS: We analyzed the differential expression and prognostic significance of DEPDC1B in colon adenocarcinoma (COAD) using the R package "limma" and the Gene Expression Profiling Interactive Analysis (GEPIA) website. Gene set enrichment analysis (GSEA) was employed to investigate the functions and interactions of DEPDC1B expression in COAD. Cell Counting Kit-8 (CCK-8) assays and colony formation assays were utilized to assess the proliferative function of DEPDC1B. Correlations between DEPDC1B expression and tumor-infiltrating immune cells, immune checkpoints, tumor mutational burden (TMB), and microsatellite instability (MSI) status were examined using Spearman correlation analysis and CIBERSORT. RESULTS: DEPDC1B was highly expressed in COAD. Elevated DEPDC1B expression was associated with lower epithelial-to-mesenchymal transition (EMT) and TNM stages, leading to a favorable prognosis. DEPDC1B mRNA was prominently expressed in COAD cell lines. CCK-8 and colony formation assays demonstrated that DEPDC1B inhibited the proliferation of COAD cells. Analysis using the CIBERSORT database and Spearman correlation revealed that DEPDC1B correlated with four types of tumor-infiltrating immune cells. Furthermore, high DEPDC1B expression was linked to the expression of PD-L1, CTLA4, SIGLEC15, PD-L2, TMB, and MSI-H. High DEPDC1B expression also indicated responsiveness to anti-PD-L1 immunotherapy. CONCLUSIONS: DEPDC1B inhibits the proliferation of COAD cells and positively regulates the cell cycle, showing a positive correlation with CCNB1 and PBK expression. DEPDC1B expression in COAD is associated with tumor-infiltrating immune cells, immune checkpoints, TMB, and MSI-H in the tumor immune microenvironment. This suggests that DEPDC1B may serve as a novel prognostic marker and a potential target for immunotherapy in COAD.

A disulfidptosis-related glucose metabolism and immune response prognostic model revealing the immune microenvironment in lung adenocarcinoma.

Background: Lung adenocarcinoma accounts for the majority of lung cancer cases and impact survival rate of patients severely. Immunotherapy is an effective treatment for lung adenocarcinoma but is restricted by many factors including immune checkpoint expression and the inhibitory immune microenvironment. This study aimed to explore the immune microenvironment in lung adenocarcinoma via disulfidptosis. Methods: Public datasets of lung adenocarcinoma from the TCGA and GEO was adopted as the training and validation cohort. Based on the differences in the expression of disulfidptosis -related genes, a glucose metabolism and immune response prognostic model was constructed. The prognostic value and clinical relationship of the model were further explored. Immune-related analyses were performed according to CIBERSORT, ssGSEA, TIDE, IPS. Results: We verified that the model could accurately predict the survival expectancy of lung adenocarcinoma patients. Patients with lung adenocarcinoma and a low-risk score had better survival outcomes according to the model. Moreover, the high-risk group tended to have an immunosuppressive effect, as reflected by the immune cell components, phenotypes and functions. We also found that the clinically relevant immune checkpoint CTLA-4 was significantly higher in low-risk group (P<0.05), indicating that the high-risk group may suffer worse tumor immunotherapy efficacy. Finally, we found that this model has accurate predictive value for the efficacy of immune checkpoint blockade in non-small cell lung cancer (P<0.05). Conclusion: The prognostic model demonstrated the feasibility of predicting survival and immunotherapy efficacy via disulfidptosis-related genes and will facilitate the development of personalized anticancer therapy.

Characterization of latently infected EBV+ antibody-secreting B cells isolated from ovarian tumors and malignant ascites.

Introduction: Intra-tumoral B cells mediate a plethora of immune effector mechanisms with key roles in anti-tumor immunity and serve as positive prognostic indicators in a variety of solid tumor types, including epithelial ovarian cancer (EOC). Several aspects of intra-tumoral B cells remain unclear, such as their state of activation, antigenic repertoires, and capacity to mature into plasma cells. Methods: B lymphocytes were isolated from primary EOC tissue and malignant ascites and were maintained in cell culture medium. The stably maintained cell lines were profiled with flow cytometry and B cell receptor sequencing. Secreted antibodies were tested with a human proteome array comprising more than 21,000 proteins, followed by ELISA for validation. Originating tumor samples were used for spatial profiling with chip cytometry. Results: Antibody-secreting B lymphocytes were isolated from the ovarian tumor microenvironment (TME) of four different EOC patients. The highly clonal cell populations underwent spontaneous immortalization in vitro, were stably maintained in an antibody-secreting state, and showed presence of Epstein-Barr viral (EBV) proteins. All originating tumors had high frequency of tumor-infiltrating B cells, present as lymphoid aggregates, or tertiary lymphoid structures. The antigens recognized by three of the four cell lines are coil-coil domain containing protein 155 (CCDC155), growth factor receptor-bound protein 2 (GRB2), and pyruvate dehydrogenase phosphatase2 (PDP2), respectively. Anti-CCDC155 circulating IgG antibodies were detected in 9 of 20 (45%) of EOC patients' sera. Tissue analyses with multiparameter chip cytometry shows that the antibodies secreted by these novel human B cell lines engage their cognate antigens on tumor cells. Discussion: These studies demonstrate that within the tumor-infiltrating lymphocyte population in EOC resides a low frequency population of antibody-secreting B cells that have been naturally exposed to EBV. Once stably maintained, these novel cell lines offer unique opportunities for future studies on intratumor B cell biology and new target antigen recognition, and for studies on EBV latency and/or viral reactivation in the TME of non-EBV related solid tumors such as the EOC.

Integrative single-cell analysis of longitudinal t(8;21) AML reveals heterogeneous immune cell infiltration and prognostic signatures.

Introduction: Immunotherapies targeting T cells in solid cancers are revolutionizing clinical treatment. Novel immunotherapies have had extremely limited benefit for acute myeloid leukemia (AML). Here, we characterized the immune microenvironment of t(8;21) AML patients to determine how immune cell infiltration status influenced prognosis. Methods: Through multi-omics studies of primary and longitudinal t(8;21) AML samples, we characterized the heterogeneous immune cell infiltration in the tumor microenvironment and their immune checkpoint gene expression. Further external cohorts were also included in this research. Results: CD8+ T cells were enriched and HAVCR2 and TIGIT were upregulated in the CD34+CD117dim%-High group; these features are known to be associated with immune exhaustion. Data integration analysis of single-cell dynamics revealed that a subset of T cells (cluster_2) (highly expressing GZMB, NKG7, PRF1 and GNLY) evolved and expanded markedly in the drug-resistant stage after relapse. External cohort analysis confirmed that the cluster_2 T-cell signature could be utilized to stratify patients by overall survival outcome. Discussion: In conclusion, we discovered a distinct T-cell signature by scRNA-seq that was correlated with disease progression and drug resistance. Our research provides a novel system for classifying patients based on their immune microenvironment.

Revealing the crucial roles of suppressive immune microenvironment in cardiac myxoma progression.

Cardiac myxoma is a commonly encountered tumor within the heart that has the potential to be life-threatening. However, the cellular composition of this condition is still not well understood. To fill this gap, we analyzed 75,641 cells from cardiac myxoma tissues based on single-cell sequencing. We defined a population of myxoma cells, which exhibited a resemblance to fibroblasts, yet they were distinguished by an increased expression of phosphodiesterases and genes associated with cell proliferation, differentiation, and adhesion. The clinical relevance of the cell populations indicated a higher proportion of myxoma cells and M2-like macrophage infiltration, along with their enhanced spatial interaction, were found to significantly contribute to the occurrence of embolism. The immune cells surrounding the myxoma exhibit inhibitory characteristics, with impaired function of T cells characterized by the expression of GZMK and TOX, along with a substantial infiltration of tumor-promoting macrophages expressed growth factors such as PDGFC. Furthermore, in vitro co-culture experiments showed that macrophages promoted the growth of myxoma cells significantly. In summary, this study presents a comprehensive single-cell atlas of cardiac myxoma, highlighting the heterogeneity of myxoma cells and their collaborative impact on immune cells. These findings shed light on the complex pathobiology of cardiac myxoma and present potential targets for intervention.

Establishment of a CD8+ T cells-related prognostic risk model for acral melanoma based on single-cell and bulk RNA sequencing.

BACKGROUND: CD8+ T cells have been recognized as crucial factors in the prognosis of melanoma. However, there is currently a lack of gene markers that accurately describe their characteristics and functions in acral melanoma (AM), which hinders the development of personalized medicine. METHODS: Firstly, we explored the composition differences of immune cells in AM using single-cell RNA sequencing (scRNA-seq) data and comprehensively characterized the immune microenvironment of AM in terms of composition, developmental differentiation, function, and cell communication. Subsequently, we constructed and validated a prognostic risk scoring model based on differentially expressed genes (DEGs) of CD8+ T cells using the TCGA-SKCM cohort through Lasso-Cox method. Lastly, immunofluorescence staining was performed to validate the expression of four genes (ISG20, CCL4, LPAR6, DDIT3) in AM and healthy skin tissues as included in the prognostic model. RESULTS: The scRNA-seq data revealed that memory CD8+ T cells accounted for the highest proportion in the immune microenvironment of AM, reaching 70.5%. Cell-cell communication analysis showed extensive communication relationships among effector CD8+ T cells. Subsequently, we constructed a prognostic scoring model based on DEGs derived from CD8+ T cell sources. Four CD8+ T cell-related genes were included in the construction and validation of the prognostic model. Additionally, immunofluorescence results demonstrated that ISG20 and CCL4 were downregulated, while LPAR6 and DDIT3 were upregulated in AM tissues compared to normal skin tissues. CONCLUSION: Identifying biomarkers based on the expression levels of CD8+ T cell-related genes may be an effective approach for establishing prognostic models in AM patients. The independently prognostic risk evaluation model we constructed provides new insights and theoretical support for immunotherapy in AM.

Identification and validation of inflammatory subtypes in intrahepatic cholangiocellular carcinoma.

BACKGROUND: Inflammation plays a critical role in tumor development. Inflammatory cell infiltration and inflammatory mediator synthesis cause changes in the tumor microenvironment (TME) in several cancers, especially in intrahepatic cholangiocellular carcinoma (ICC). However, methods to ascertain the inflammatory state of patients using reliable biomarkers are still being explored. METHOD: We retrieved the RNA sequencing and somatic mutation analyses results and the clinical characteristics of 244 patients with ICC from published studies. We performed consensus clustering to identify the molecular subtypes associated with inflammation. We compared the prognostic patterns, clinical characteristics, somatic mutation profiles, and immune cell infiltration patterns across inflammatory subtypes. We performed quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) to confirm gene expression. We performed logistic regression analyses to construct a nomogram predicting the inflammatory status of patients with ICC. RESULTS: Our results confirmed that ICC can be categorized into an inflammation-high subtype (IHS) and an inflammation-low subtype (ILS). Patients from each group had distinct prognosis, clinical characteristics, and TME composition. Patients with ICC in the IHS group showed poorer prognosis owing to the immunosuppressive microenvironment and high frequency of KRAS and TP53 mutations. Cancer-associated fibroblast (CAF)-derived COLEC11 reduced myeloid inflammatory cell infiltration and attenuated inflammatory responses. The results of qRT-PCR and IHC experiments confirmed that COLEC11 expression levels were significantly reduced in tumor tissues compared to those in paracancerous tissues. Patients with ICC in the IHS group were more likely to respond to treatment with immune checkpoint inhibitors (ICIs) owing to their higher tumor mutational burden (TMB) scores, tumor neoantigen burden (TNB) scores, neoantigen counts, and immune checkpoint expression levels. Finally, we developed a nomogram to effectively predict the inflammatory status of patients with ICC based on their clinical characteristics and inflammatory gene expression levels. We evaluated the calibration, discrimination potential, and clinical utility of the nomogram. CONCLUSION: The inflammatory response in IHS is primarily induced by myeloid cells. COLEC11 can reduce the infiltration level of this group of cells, and myeloid inflammatory cells may be a novel target for ICC treatment. We developed a novel nomogram that could effectively predict the inflammatory state of patients with ICC, which will be useful for guiding individualized treatment plans.

Comprehensive investigation of tumor immune microenvironment and prognostic biomarkers in osteosarcoma through integrated bulk and single-cell transcriptomic analysis.

Osteosarcoma (OS) is an aggressive and highly lethal bone tumor, highlighting the urgent need for further exploration of its underlying mechanisms. In this study, we conducted analyses utilizing bulk transcriptome sequencing data of OS and healthy control samples, as well as single cell sequencing data, obtained from public databases. Initially, we evaluated the differential expression of four tumor microenvironment (TME)-related gene sets between tumor and control groups. Subsequently, unsupervised clustering analysis of tumor tissues identified two significantly distinct clusters. We calculated the differential scores of the four TME-related gene sets for Clusters 1 (C1) and 2 (C2), using Gene Set Variation Analysis (GSVA, followed by single-variable Cox analysis. For the two clusters, we performed survival analysis, examined disparities in clinical-pathological distribution, analyzed immune cell infiltration and immune evasion prediction, assessed differences in immune infiltration abundance, and evaluated drug sensitivity. Differentially expressed genes (DEGs) between the two clusters were subjected to Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). We conducted Weighted Gene Co-expression Network Analysis (WGCNA) on the TARGET-OS dataset to identify key genes, followed by GO enrichment analysis. Using LASSO and multiple regression analysis we conducted a prognostic model comprising eleven genes (ALOX5AP, CD37, BIN2, C3AR1, HCLS1, ACSL5, CD209, FCGR2A, CORO1A, CD74, CD163) demonstrating favorable diagnostic efficacy and prognostic potential in both training and validation cohorts. Using the model, we conducted further immune, drug sensitivity and enrichment analysis. We performed dimensionality reduction and annotation of cell subpopulations in single cell sequencing analysis, with expression profiles of relevant genes in each subpopulation analyzed. We further substantiated the role of ACSL5 in OS through a variety of wet lab experiments. Our study provides new insights and theoretical foundations for the prognosis, treatment, and drug development for OS patients.

HIGD1B, as a novel prognostic biomarker, is involved in regulating the tumor microenvironment and immune cell infiltration; its overexpression leads to poor prognosis in gastric cancer patients.

Background: HIGD1B (HIG1 Hypoxia Inducible Domain Family Member 1B) is a protein-coding gene linked to the occurrence and progression of various illnesses. However, its precise function in gastric cancer (GC) remains unclear. Methods: The expression of HIGD1B is determined through the TCGA and GEO databases and verified using experiments. The association between HIGD1B and GC patients' prognosis was analyzed via the Kaplan-Meier (K-M) curve. Subsequently, the researchers utilized ROC curves to assess the diagnostic capacity of HIGD1B and employed COX analysis to investigate risk factors for GC. The differentially expressed genes (DEGs) were then subjected to functional enrichment analysis, and a nomogram was generated to forecast the survival outcome and probability of GC patients. Additionally, we evaluated the interaction between HIGD1B and the immune cell infiltration and predicted the susceptibility of GC patients to therapy. Results: HIGD1B is markedly elevated in GC tissue and cell lines, and patients with high HIGD1B expression have a poorer outcome. In addition, HIGD1B is related to distinct grades, stages, and T stages. The survival ROC curves of HIGD1B and nomogram for five years were 0.741 and 0.735, suggesting appropriate levels of diagnostic efficacy. According to Cox regression analysis, HIGD1B represents a separate risk factor for the prognosis of gastric cancer (p<0.01). GSEA analysis demonstrated that the HIGD1B is closely related to cancer formation and advanced pathways. Moreover, patients with high HIGD1B expression exhibited a higher level of Tumor-infiltration immune cells (TIICs) and were more likely to experience immune escape and drug resistance after chemotherapy and immunotherapy. Conclusion: This study explored the potential mechanisms and diagnostic and prognostic utility of HIGD1B in GC, as well as identified HIGD1B as a valuable biomarker and possible therapeutic target for GC.

Scinderin is a potential prognostic biomarker and correlated with immunological regulation: from pan-cancer analysis to liver hepatocellular carcinoma.

Aim: This study aimed to systematically dissect the role of Scinderin (SCIN) in tumorigenesis. Methods: Bioinformatics techniques were employed based on cancer data from TCGA, ENCORI, HPA, GEPIA2, UALCAN, Kaplan-Meier plotter, TIMER, TISIDB, cBioPortal, HCCDB, GeneMANIA and LinkedOmics database. Experiments in vitro and in vivo were conducted to dissect the role of SCIN in liver hepatocellular carcinoma (LIHC). Results: Significantly differential expression of SCIN was found in nine types of cancers, including LIHC. Through pan-cancer analysis, the correlations between SCIN expression with prognosis and immune cell infiltration were proven, especially in LIHC, ovarian serous cystadenocarcinoma and lung adenocarcinoma. The highest frequency of alteration in SCIN (6.81%) was seen in patients with uterine corpus endometrial carcinoma, in which "mutation" was the predominant type, with a frequency of about 5.29%; meanwhile, S673F and S381Y were the two most frequent mutation sites. Furthermore, the abnormal expression of SCIN exhibited a strong relationship with immune cell subtypes, immune checkpoint genes, tumor mutation burden, microsatellite instability, neoantigen, molecular subtypes, mismatch repair signatures and DNA methyl-transferase in different cancer types. Through comparative analysis, we discovered that SCIN was dramatically up-regulated in LIHC, and associated with poor survival. Experiments in vitro and in vivo suggested the knockdown of SCIN could suppress tumor cell proliferation and improve the survival rate partly in animal models. Conclusion: This study reveals SCIN may be a promising biomarker for prognosis and treatment in certain cancers, especially in LIHC.

Tumour-intrinsic endomembrane trafficking by ARF6 shapes an immunosuppressive microenvironment that drives melanomagenesis and response to checkpoint blockade therapy.

Tumour-host immune interactions lead to complex changes in the tumour microenvironment (TME), impacting progression, metastasis and response to therapy. While it is clear that cancer cells can have the capacity to alter immune landscapes, our understanding of this process is incomplete. Herein we show that endocytic trafficking at the plasma membrane, mediated by the small GTPase ARF6, enables melanoma cells to impose an immunosuppressive TME that accelerates tumour development. This ARF6-dependent TME is vulnerable to immune checkpoint blockade therapy (ICB) but in murine melanoma, loss of Arf6 causes resistance to ICB. Likewise, downregulation of ARF6 in patient tumours correlates with inferior overall survival after ICB. Mechanistically, these phenotypes are at least partially explained by ARF6-dependent recycling, which controls plasma membrane density of the interferon-gamma receptor. Collectively, our findings reveal the importance of endomembrane trafficking in outfitting tumour cells with the ability to shape their immune microenvironment and respond to immunotherapy.

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes.

Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating single-cell transcriptomic data with machine learning. We identified 44 significantly expressed NKRGs involved in cytokine and T cell-related functions. Using 101 machine learning algorithms, the Lasso + RSF model showed the highest predictive accuracy with nine key NKRGs. We explored cell-to-cell communication using CellChat, assessed immune-related pathways and tumour microenvironment with gene set variation analysis and ssGSEA, and observed immune components by HE staining. Additionally, drug activity predictions identified potential therapies, and gene expression validation through immunohistochemistry and RNA-seq confirmed the clinical applicability of NKRGs. The nomogram showed high concordance between predicted and actual survival, linking higher tumour purity and risk scores to a reduced immune score. This NKRG-based model offers a novel approach for risk assessment and personalized treatment in BC, enhancing the potential of precision medicine.

Understanding tissue-resident macrophages unlocks the potential for novel combinatorial strategies in breast cancer.

Tissue-resident macrophages (TRMs) are an integral part of the innate immune system, but their biology is not well understood in the context of cancer. Distinctive resident macrophage populations are identified in different organs in mice using fate mapping studies. They develop from the yolk sac and self-maintain themselves lifelong in specific tissular niches. Similarly, breast-resident macrophages are part of the mammary gland microenvironment. They reside in the breast adipose tissue stroma and close to the ductal epithelium and help in morphogenesis. In breast cancer, TRMs may promote disease progression and metastasis; however, precise mechanisms have not been elucidated. TRMs interact intimately with recruited macrophages, cytotoxic T cells, and other immune cells along with cancer cells, deciding further immunosuppressive or cytotoxic pathways. Moreover, triple-negative breast cancer (TNBC), which is generally associated with poor outcomes, can harbor specific TRM phenotypes. The influence of TRMs on adipose tissue stroma of the mammary gland also contributes to tumor progression. The complex crosstalk between TRMs with T cells, stroma, and breast cancer cells can establish a cascade of downstream events, understanding which can offer new insight for drug discovery and upcoming treatment choices. This review aims to acknowledge the previous research done in this regard while exploring existing research gaps and the future therapeutic potential of TRMs as a combination or single agent in breast cancer.

Targeting mitochondrial damage: shining a new light on immunotherapy.

Mitochondrial damage has a particular impact on the immune system and tumor microenvironment, which can trigger cell stress, an inflammatory response, and disrupt immune cell function, thus all of which can accelerate the progression of the tumor. Therefore, it is of essence to comprehend how the immune system function and the tumor microenvironment interact with mitochondrial dysfunction for cancer treatment. Preserving the integrity of mitochondria or regulating the function of immune cells, such as macrophages, may enhance the efficacy of cancer therapy. Future research should concentrate on the interactions among mitochondria, the immune system, and the tumor microenvironment to identify new therapeutic strategies.

Impact of mitochondrial damage on tumor microenvironment and immune response: a comprehensive bibliometric analysis.

Background: Mitochondrial damage contributes to apoptosis, oxidative stress, and inflammation, which collectively impact the immune system's function and the tumor microenvironment (TME). These processes, in turn, influence tumor cell growth, migration, and response to treatment. Objective: We conducted a bibliometric analysis to elucidate the complex interactions between mitochondrial damage, the immune system, and the TME. Methods: Data were sourced from the Science Citation Index Core Collection (WoSCC) and analyzed using advanced tools like VOSviewer and Citespace. Our focus was on literature published between 1999 and 2023 concerning the interactions between mitochondrial damage and the TME, as well as immune responses to tumors. The analysis included regional contributions, journal influence, institutional collaborations, authorship, co-cited authors, and keyword citation bursts. Results: Our research encompassed 2,039 publications, revealing an increasing trend in annual output exploring the relationship between mitochondrial damage, TME dynamics, and immune responses. China, the United States, and South Korea emerged as the leading contributors. Prominent institutions included Institut National de la Santé et de la Recherche Médicale, University of Texas System, China Medical University, and Sun Yat-sen University. Key journals in this field are the International Journal of Molecular Sciences, Mitochondrion, and the European Journal of Pharmacology. Liang H and Wallace DC were identified as the most productive and co-cited authors, respectively. Keyword analysis highlighted the critical roles of inflammatory responses, oxidative stress, and the immune system in recent research. Conclusion: This bibliometric analysis provides a comprehensive overview of historical and current research trends, underscoring the pivotal role of mitochondrial damage in the TME and immune system.

WD repeat domain 43 as a new predictive indicator and its connection with tumor immune cell infiltration in pan-cancer.

BACKGROUND: WD repeat domain 43 (WDR43) is a protein component that encodes WD-repeats and is involved in ribosome biogenesis. However, little is known about the role of WDR43 in cancer prognosis and immune modulation. METHODS: In this study, we analyzed the expression and prognostic significance of WDR43 in pan-cancer using the Cancer Genome Atlas, the Genotype-Tissue Expression, and the Human Protein Atlas. We also examined the differential expression of WDR43 in liver hepatocellular carcinoma (LIHC) and adjacent tissues of 48 patients using immunohistochemistry. Additionally, we investigated the correlation between WDR43 and clinical characteristics, gene alterations, tumor mutation burden, microsatellite instability, mismatch repair, tumor microenvironment, immune infiltrating cells, and immune-related genes using bioinformatics methods. Gene set enrichment analysis was conducted, and potential biological mechanisms were identified. RESULTS: Immunohistochemistry staining showed that WDR43 was overexpressed in LIHC among 48 patients. Upregulation of WDR43 was associated with unfavorable prognosis, including overall survival in various types of cancer such as LIHC, uterine corpus endometrial cancer, head and neck squamous cell carcinoma, and pancreatic adenocarcinoma. Differential expression of WDR43 was significantly correlated with microsatellite instability, mismatch repair, and immune cell infiltration. Gene ontology annotation analysis revealed that these genes were significantly enriched in immune-related functions, including immune response, immune regulation, and signaling pathways. CONCLUSION: We conducted a thorough investigation of the clinical features, phases of tumor development, immune infiltration, gene mutation, and functional enrichment analysis of WDR43 in various types of cancer. This research offers valuable insight into the significance and function of WDR43 in clinical therapy.

Hypoxia-related signature to risk stratify patients for the benefit of immune checkpoint inhibitors therapy in head and neck squamous cell carcinoma: An experimental study.

BACKGROUND: Increasing evidence has shown that hypoxia is a biomarker of tumor proliferation and metastasis. This research aimed to identify a hypoxia-associated gene prognostic index (HAGPI) in head and neck squamous cell carcinoma (HNSCC) and based on HAGPI-defined subgroups to predict prognosis and response to immune checkpoint inhibitors therapy. METHODS: RNA-sequencing transcriptomic data for patients with HNSCC were downloaded from The Cancer Genome Atlas (TCGA). Protein-protein interaction network analysis was performed to select hypoxia-related hub genes. Univariate and multivariate cox regression analyses were used to identify hub genes to develop the HAGPI. Afterward expression data were imported into CIBERSORT to evaluate the relative proportion of 22 immune cells and compared the relative proportions of immune cells between the 2 HAGPI subgroups. The relationship between immunopheno score (IPS) and HAGPI was validated for immune checkpoint inhibitors (ICIs) response in TCGA cohorts. RESULTS: The HAGPI was constructed based on HS3ST1, HK1, PGK1, STC2, SERPINE1, PKLR genes. In high-HAGPI patients, the primary and secondary endpoint events in TCGA and GEO cohorts were significantly lower than low-HAGPI groups (P < .05). HAGPI-high patients exhibited a poorer prognosis than HAGPI-low patients did. The abundance of M2 macrophages and NK cell were significantly enhanced in the high-HAGPI while T cells regulatory and T cells CD8, were markedly elevated in the low-HAGPI. Meanwhile, patients in the low-HAGPI patients had higher levels of immunosuppressant expression and less aggressive phenotypes. Furthermore, IPS analysis showed that the low-HAGPI group with higher IPS represented a more immunogenic phenotype. CONCLUSION: The current study developed and verified a HAPGI model that can be considered as an independent prognostic biomarker and elucidated the tumor immune microenvironment of HNSCC.

Interaction between microglial cells and CD1C+ B dendritic cells leads to CD8+ T cells depletion during the early stages of renal clear cell carcinoma.

Renal clear cell carcinoma (RCC) is a type of malignant tumor, which, in addition to surgical resection, radiotherapy, and chemotherapy, has been widely treated through immunotherapy recently. However, the influence of the tumor microenvironment and the infiltrating immune cells within it on immunotherapy remains unclear. It is imperative to study the interactions between various immune cells of RCC. The scRNA-seq dataset from GEO's database was used to analyze the immune cells present in tumor tissue and peripheral blood samples. Through quality control, clustering, and identification, the types and proportions of infiltrating immune cells were determined. The cellular differences were determined, and gene expression levels of the differentially present cells were investigated. A protein-protein interaction network analysis was performed using string. KEGG and GO analyses were performed to investigate abnormal activities. The microglia marker CD68 and CD1C+ B dendritic cells marker CD11C were detected using multiplex immunofluorescence staining. Many depleted CD8+ T cells (exhausted CD8+ T cells) appeared in tumor tissues as well as microglia. CD1C+ B dendritic cells did not infiltrate tumor tissues. HSPA1A was correlated with DNAJB1 in microglia. Compared with Paracancer tissues, microglia increased while CD1C+ B dendritic cells decreased in pathological stages I and I-II in cancerous tissues. An altered tumor microenvironment caused by increases in microglia in RCC in the early stage resulted in an inability of CD1C+ B dendritic cells to infiltrate, resulting in CD8+ T cells being unable to receive the antigens presented by them, and in turn being depleted in large quantities.

Anlotinib potentiates anti-PD1 immunotherapy via transferrin receptor-dependent CD8+ T-cell infiltration in hepatocellular carcinoma.

BACKGROUND: The therapeutic potential of immune checkpoint blockade (ICB) extends across various cancers; however, its effectiveness in treating hepatocellular carcinoma (HCC) is frequently curtailed by both inherent and developed resistance. OBJECTIVE: This research explored the effectiveness of integrating anlotinib (a broad-spectrum tyrosine kinase inhibitor) with programmed death-1 (PD-1) blockade and offers mechanistic insights into more effective strategies for treating HCC. METHODS: Using patient-derived organotypic tissue spheroids and orthotopic HCC mouse models, we assessed the effectiveness of anlotinib combined with PD-1 blockade. The impact on the tumour immune microenvironment and underlying mechanisms were assessed using time-of-flight mass cytometry, RNA sequencing, and proteomics across cell lines, mouse models, and HCC patient samples. RESULTS: The combination of anlotinib with an anti-PD-1 antibody enhanced the immune response against HCC in preclinical models. Anlotinib remarkably suppressed the expression of transferrin receptor (TFRC) via the VEGFR2/AKT/HIF-1α signaling axis. CD8+ T-cell infiltration into the tumour microenvironment correlated with low expression of TFRC. Anlotinib additionally increased the levels of the chemokine CXCL14, crucial for attracting CD8+ T cells. CXCL14 emerged as a downstream effector of TFRC, exhibiting elevated expression following the silencing of TFRC. Importantly, low TFRC expression was also associated with a better prognosis, enhanced sensitivity to combination therapy, and a favourable response to anti-PD-1 therapy in patients with HCC. CONCLUSIONS: Our findings highlight anlotinib's potential to augment the efficacy of anti-PD-1 immunotherapy in HCC by targeting TFRC and enhancing CXCL14-mediated CD8+ T-cell infiltration. This study contributes to developing novel therapeutic strategies for HCC, emphasizing the role of precision medicine in oncology. HIGHLIGHTS: Synergistic effects of anlotinib and anti-PD-1 immunotherapy demonstrated in HCC preclinical models. Anlotinib inhibits TFRC expression via the VEGFR2/AKT/HIF-1α pathway. CXCL14 upregulation via TFRC suppression boosts CD8+ T-cell recruitment. TFRC emerges as a potential biomarker for evaluating prognosis and predicting response to anti-PD-1-based therapies in advanced HCC patients.