Immunotherapy Response In Patients Research Articles

AKAP12 positive fibroblast determines immunosuppressive contexture and immunotherapy response in patients with TNBC by promoting macrophage M2 polarization

BackgroundTriple-negative breast cancer (TNBC) is a molecular subtype of breast cancer with high aggressiveness and poor prognosis. Cancer-associated fibroblasts (CAFs) are major components of the TNBC microenvironment and play an...

Comprehensive analysis reveals that P4HA3 is a prognostic and diagnostic gastric cancer biomarker that can predict immunotherapy efficacy

Gastric cancer (GC) is one of the most challenging malignant tumors worldwide, primarily because of its high incidence and mortality rates. Prolyl 4-hydroxylase subunit alpha 3 (P4HA3) has been established as a pivotal factor for facilitating cell proliferation, invasion, and metastasis across multiple human tumors. Nevertheless, the precise role of P4HA3in GC has not been fully elucidated. In this study, we used data from The Cancer Genome Atlas (TCGA) to examine the role of P4HA3 as a potential biomarker for predicting immunotherapy response in patients with GC. Our comprehensive analysis of data from the TCGA, TIMER, and other databases revealed a significant association between elevated P4HA3 expression in GC and adverse prognostic outcomes. Furthermore, we confirmed that P4HA3 expression was strongly correlated with immune infiltrating cells, immune infiltration markers, the tumor mutational burden (TMB), microsatellite instability (MSI), the immune score, the stromal score, and immune checkpoints, thus highlighting P4HA3 as a crucial and dependable therapeutic target within the context of immune-based antitumor strategies. Our findings suggest that P4HA3 may function as an immune-related biomarker in the pathogenesis and treatment of GC, indicating that P4HA3 is a promising prognostic and therapeutic target for this malignancy.

The branched N-glycan of PD-L1 predicts immunotherapy responses in patients with recurrent/metastatic HNSCC

Immunotherapy has revolutionized cancer treatment, but the lack of a reliable predictive biomarker for treatment response remains a challenge. Alpha-1,6-Mannosylglycoprotein 6-β-N-Acetylglucosaminyltransferase 5 (MGAT5) is a key regulator of complex N-glycan synthesis, and its dysregulation is associated with cancer progression. The lectin Phaseolus vulgaris leukoagglutinin (PHA-L) specifically binds to mature MGAT5 products. Previous studies have indicated elevated PHA-L staining in head and neck squamous cell carcinoma (HNSCC), which implies increased activity of MGAT5. However, the specific role of MGAT5 in HNSCC remains unclear. In this study, we found significantly higher PHA-L staining and MGAT5 expression in HNSCC tumors compared to adjacent non-tumor tissues. Using a mass spectrometry (MS)-based glycoproteomic approach, we identified 163 potential protein substrates of MGAT5. Functional analysis revealed that protein substrates of MGAT5 regulated pathways related to T cell proliferation and activation. We further discovered that PD-L1 was among the protein substrates of MGAT5, and the expression of MGAT5 protected tumor cells from cytotoxic T lymphocyte (CTL) killing. Treatment of nivolumab alleviated the protective effects of MGAT5 on CTL activity. Consistently, patients with MGAT5-positive tumors showed improved responses to immunotherapy compared to those with MGAT5-negative tumors. Using purified PD-L1 from HNSCC cells and a glycoproteomic approach, we further deciphered that the N35 and N200 sites carry the majority of complex N-glycans on PD-L1. Our findings highlight the critical role of MGAT5-mediated branched N-glycans on PD-L1 in modulating the interaction with the immune checkpoint receptor PD-1. Consequently, we propose that MGAT5 could serve as a biomarker to predict patients’ responses to anti-PD-1 therapy. Furthermore, targeting the branched N-glycans at N35 and N200 of PD-L1 may lead to the development of novel diagnostic and therapeutic approaches.

Construction of M2 macrophage-related gene signature for predicting prognosis and revealing different immunotherapy response in bladder cancer patients.

Bladder cancer development is closely associated with the dynamic interaction and communication between M2 macrophages and tumor cells. However, specific biomarkers for targeting M2 macrophages in immunotherapy remain limited and require further investigation. In this study, we identified key co-expressed genes in M2 macrophages and developed gene signatures to predict prognosis and immunotherapy response in patients. Public database provided the bioinformatics data used in the analysis. We created and verified an M2 macrophage-related gene signature in these datasets using Lasso-Cox analysis. The predictive value and immunological functions of our risk model were examined in bladder cancer patients, and 158 genes were found to be significantly positively correlated with M2 macrophages. Moreover, we identified two molecular subgroups of bladder cancer with markedly different immunological profiles and clinical prognoses. The five key risk genes identified in this model were validated, including CALU, ECM1, LRP1, CYTL1, and CCDC102B, demonstrating the model can accurately predict prognosis and identify unique responses to immunotherapy in patients with bladder cancer. In summary, we constructed and validated a five-gene signature related to M2 macrophages, which shows strong potential for forecasting bladder cancer prognosis and immunotherapy response.

Revealing a cancer-associated fibroblast-based risk signature for pancreatic adenocarcinoma through single-cell and bulk RNA-seq analysis.

Proliferation of stromal connective tissue is a hallmark of pancreatic adenocarcinoma (PAAD). The engagement of activated cancer-associated fibroblasts (CAFs) contributes to the progression of PAAD through their involvement in tumor fibrogenesis. However, the prognostic significance of CAF-based risk signature in PAAD has not been explored. The single-cell RNA sequencing (scRNA-seq) data sourced from GSE155698 within the Gene Expression Omnibus (GEO) database was supplemented by bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and microarray data retrieved from the GEO database. The scRNA-seq data underwent processing via the Seurat package to identify distinct CAF clusters utilizing specific CAF markers. Differential gene expression analysis between normal and tumor samples was conducted within the TCGA-PAAD cohort. Univariate Cox regression analysis pinpointed genes associated with CAF clusters, identifying prognostic CAF-related genes. These genes were utilized in LASSO regression to craft a predictive risk signature. Subsequently, integrating clinicopathological traits and the risk signature, a nomogram model was constructed. Our scRNA-seq analysis unveiled four distinct CAF clusters in PAAD, with two linked to PAAD prognosis. Among 207 identified DEGs, 148 exhibited significant correlation with these CAF clusters, forming the basis of a seven-gene risk signature. This signature emerged as an independent predictor in multivariate analysis for PAAD and demonstrated predictive efficacy in immunotherapeutic outcomes. Additionally, a novel nomogram, integrating age and the CAF-based risk signature, exhibited robust predictability and reliability in prognosticating PAAD. Moreover, the risk signature displayed substantial correlations with stromal and immune scores, as well as specific immune cell types. The prognosis of PAAD can be accurately predicted using the CAF-based risk signature, and a thorough analysis of the PAAD CAF signature may aid in deciphering the patient's immunotherapy response and presenting fresh cancer treatment options.

CN93 Quality of life scale-based machine learning approach to predict immunotherapy response in patients with advanced non-small cell lung cancer

CN93 Quality of life scale-based machine learning approach to predict immunotherapy response in patients with advanced non-small cell lung cancer

SHC1 serves as a prognostic and immunological biomarker in clear cell renal cell carcinoma: a comprehensive bioinformatics and experimental analysis

SHC1 plays a crucial regulatory role in various tumors, but its significance in predicting prognosis and immune response in clear cell renal cell carcinoma (ccRCC) is yet to be determined. In this study, we conducted a bioinformatics analysis of SHC1 expression, prognosis, and immunological functions in ccRCC using multiple databases. The association between SHC1 and immune infiltration, immune escape, and immunotherapy in ccRCC was systematically established. In addition, we validated our results by western blot of tumor and adjacent-tumor samples from nine ccRCC patients, as well as three renal carcinoma cell lines compared to a normal renal cell line. Our analysis revealed that the mRNA expression level of SHC1 in ccRCC tissues is significantly higher than that in normal tissues. Consistently, western blot experiment showed ccRCC tissues and cell lines exhibit higher protein levels that normal tissues and cell lines. Importantly, patients with low expression of SHC1 demonstrated a higher survival rate, indicating that SHC1 could serve as an independent prognostic factor for predicting survival in ccRCC. Additionally, high expression of SHC1 was associated with increased severe immune cell infiltration, enhanced immune escape, and higher immunotherapy scores. Hence, SHC1 emerges as a novel and easily detectable biomarker for predicting clinical outcomes, immune escape, and immunotherapy response in patients with ccRCC.

H3K9 lactylation in malignant cells facilitates CD8+ T cell dysfunction and poor immunotherapy response

Histone lysine lactylation (Kla) is a post-translational modification, and its role in tumor immune escape remains unclear. Here, we find that increased histone lactylation is associated with poor response to immunotherapy in head and neck squamous cell carcinoma (HNSCC). H3K9la is identified as a specific modification site in HNSCC. Using cleavage under targets and tagmentation analyses, interleukin-11 (IL-11) is identified as a downstream regulatory gene of H3K9la. IL-11 transcriptionally activates immune checkpoint genes through JAK2/STAT3 signaling in CD8+ Tcells. Additionally, IL-11 overexpression promotes tumor progression and CD8+ Tcell dysfunction invivo. Moreover, IL11 knockdown reverses lactate-induced CD8+ Tcell exhaustion, and cholesterol-modified siIL11 restores CD8+ Tcell killing activity and enhances immunotherapy efficacy. Clinically, H3K9la positively correlates with IL-11 expression and unfavorable immunotherapy responses in patients. This study reveals the crucial role of histone lactylation in immune escape, providing insights into immunotherapy strategies for HNSCC.

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Integrated bioinformatics analysis and experimental validation reveal the relationship between ALOX5AP and the prognosis and immune microenvironment in glioma

BackgroundTreatment of gliomas, the most prevalent primary malignant neoplasm of the central nervous system, is challenging. Arachidonate 5-lipoxygenase activating protein (ALOX5AP) is crucial for converting arachidonic acid into leukotrienes and is associated with poor prognosis in multiple cancers. Nevertheless, its relationship with the prognosis and the immune microenvironment of gliomas remains incompletely understood.MethodsThe differential expression of ALOX5AP was evaluated based on public Databases. Kaplan–Meier, multivariate Cox proportional hazards regression analysis, time-dependent receiver operating characteristic, and nomogram were used to estimate the prognostic value of ALOX5AP. The relationship between ALOX5AP and immune infiltration was calculated using ESTIMATE and CIBERSORT algorithms. Relationships between ALOX5AP and human leukocyte antigen molecules, immune checkpoints, tumor mutation burden, TIDE score, and immunophenoscore were calculated to evaluate glioma immunotherapy response. Single gene GSEA and co-expression network-based GO and KEGG enrichment analysis were performed to explore the potential function of ALOX5AP. ALOX5AP expression was verified using multiplex immunofluorescence staining and its prognostic effects were confirmed using a glioma tissue microarray.ResultALOX5AP was highly expressed in gliomas, and the expression level was related to World Health Organization (WHO) grade, age, sex, IDH mutation status, 1p19q co-deletion status, MGMTp methylation status, and poor prognosis. Single-cell RNA sequencing showed that ALOX5AP was expressed in macrophages, monocytes, and T cells but not in tumor cells. ALOX5AP expression positively correlated with M2 macrophage infiltration and poor immunotherapy response. Immunofluorescence staining demonstrated that ALOX5AP was upregulated in WHO higher-grade gliomas, localizing to M2 macrophages. Glioma tissue microarray confirmed the adverse effect of ALOX5AP in the prognosis of glioma.ConclusionALOX5AP is highly expressed in M2 macrophages and may act as a potential biomarker for predicting prognosis and immunotherapy response in patients with glioma.

Identifying and validating necroptosis-associated features to predict clinical outcome and immunotherapy response in patients with glioblastoma.

Necroptosis is a type of programmed cell death involved in the pathogenesis of cancers. This work developed a prognostic glioblastoma (GBM) model based on necroptosis-related genes. RNA-Seq data were collected from the TCGA database. The "WGCNA" method was used to identify co-expression modules, based on which GO and KEGG analyses were conducted. A protein-protein interaction (PPI) network was compiled. The number of key prognostic genes was reduced applying COX regression and least absolute shrinkage and selection operator (LASSO) analysis to build a RiskScore model. Differences in immune microenvironments were assessed using CIBERSORT, ESTIMATE, MCP-count, and TIMER databases. The potential impact of key prognostic genes on GBM was validated by cellular experiments. GBM patients in the higher necroptosis score group had higher immune scores and worse survival. The Brown module, which was closely related to the necroptosis score, was considered as a key gene module. Three key genes (GZMB, PLAUR, SOCS3) were obtained by performing regression analysis on the five clusters. The RiskScore model was significantly, positively, correlated with necroptosis score. Low-risk patients could benefit from immunotherapy, while high-risk patients may be more suitable to take multiple chemotherapy drugs. The nomogram showed strong performance in survival prediction. GZMB, PLAUR, and SOCS3 played key roles in GBM development. Among them, high-expressed GZMB was related to the invasive and migratory abilities of GBM cells. A genetic signature associated with necroptosis was developed, and we constructed a RiskScore model to provide reference for predicting clinical outcomes and immunotherapy responses of patients with GBM.

Correlation of noninvasive imaging of tumour-infiltrating lymphocytes with survival and BCG immunotherapy response in patients with bladder cancer：a multicentre cohort study

Background: Tumour-infiltrating lymphocytes (TILs) are strongly correlated with the prognosis and immunotherapy response in bladder cancer. The TIL status is typically assessed through microscopy as part of tissue pathology. Here, we developed Rad-TIL model, a novel radiomics model, to predict TIL status in patients with bladder cancer. Material and Methods: We enrolled 1089 patients with bladder cancer and developed the Rad-TIL model by using a machine-learning method based on computed tomography (CT) images. We applied a radiogenomics cohort to reveal the key pathways underlying the Rad-TIL model. Finally, we used an independent treatment cohort to evaluate the predictive efficacy of the Rad-TIL model for Bacillus Calmette-Guérin (BCG) immunotherapy. Results: We developed the Rad-TIL model by integrating tumoral and peritumoral features on CT images and obtained areas under the receiver operating characteristic curves of 0.844 and 0.816 in the internal and external validation cohorts, respectively. Patients were stratified into two groups based on the predicted radiomics score of TILs (RSTIL). RSTIL exhibited prognostic significance for both overall and cancer-specific survival in each cohort (hazard ratios: 2.27 to 3.15, all P<0.05). Radiogenomics analysis revealed a significant association of RSTIL with immunoregulatory pathways and immune checkpoint molecules (all P<0.05). Notably, BCG immunotherapy response rates were significantly higher in high-RSTIL patients than in low-RSTIL patients (P=0.007). Conclusion: The Rad-TIL model, a noninvasive method for assessing TIL status, can predict clinical outcomes and BCG immunotherapy response in patients with bladder cancer.

Salivary Microbiome Relates to Neoadjuvant Immunotherapy Response in OSCC

Most patients diagnosed with oral squamous cell carcinoma (OSCC) present with locally advanced stages, which are typically associated with poor outcomes. Although immunotherapy offers potential improvements in patient survival, its efficacy is hampered by low response rates. The microbiome is widely involved in tumor immunity and may play a role in immunotherapy. This study aimed to investigate the potential association between the oral (salivary) microbiome and immunotherapy response in patients with OSCC. Salivary metagenome sequencing was performed on 47 patients with OSCC undergoing neoadjuvant immunotherapy (NAIT) in a clinical trial (NCT04649476). Patients were divided into responders and nonresponders based on their pathological responses. The results showed that the species richness of the salivary microbiome was lower in the nonresponders before NAIT than in the responders. Differential analysis revealed that nonresponders exhibited a lower relative abundance of 34 bacterial species and a higher relative abundance of 4 bacterial species. Notably, low levels of Eubacterium infirmum, Actinobaculum, and Selenomas (EAS) in the saliva may be associated with the nonresponse of patients with OSCC to NAIT. A nomogram based on EAS was developed and validated to determine the efficacy of NAIT. The area under the curve for the training cohort was 0.81 (95% confidence interval, 0.66 to 0.81). Quantitative polymerase chain reaction confirmed that low levels of salivary EAS effectively identified nonresponders to NAIT. Furthermore, the low abundance of salivary EAS was closely correlated with a low density of intratumoral CD4+, CD14+, CD68+, and FOXP3+ cells. Metabolic functional annotation revealed numerous biosynthetic processes associated with EAS that were more active in responders. In summary, this study provides valuable data resources for the salivary microbiome and reveals that nonresponders have different salivary microbiome profiles than responders do before NAIT. Low salivary EAS levels can serve as potential biomarkers for distinguishing nonresponders from responders.

PD-L1+ macrophage and tumor cell abundance and proximity to T cells in the pretreatment large B-cell lymphoma microenvironment impact CD19 CAR-T cell immunotherapy efficacy.

CD19-targeted chimeric antigen receptor T-cell (CAR-T) immunotherapy has transformed the management of relapsed/refractory large B-cell lymphoma (LBCL), yet durable remissions are observed in less than half of treated patients. The tumor microenvironment (TME) is a key and understudied factor impacting CD19 CAR-T therapy outcomes. Using NanoString nCounter transcriptome profiling (n = 24) and multiplex immunohistochemistry (mIHC, n = 15), we studied the TME in pretreatment biopsies from patients with LBCL undergoing CD19 CAR-T therapy. Patients who achieved complete response (CR) after CAR-T therapy demonstrated higher expression of genes associated with T-cell trafficking and function, whereas those who did not achieve CR had higher expression of genes associated with macrophages and T-cell dysfunction. Distinct patterns of immune infiltration and fibrosis in the TME were associated with CAR-T therapy outcomes, and these findings were corroborated using artificial intelligence-assisted image analyses. Patients who achieved CR had a lower proportion of the biopsy occupied by an interspersed immune infiltrate and a higher proportion of hypocellular/fibrotic regions. Furthermore, mIHC revealed lower density of CD4+ T cells and higher densities of both macrophages and tumor cells expressing PD-L1 in non-CR patients. Spatial analysis revealed that PD-1+ T cells were in close proximity to PD-L1+ macrophages or PD-L1+ tumor cells in patients who did not compared to those who did achieve CR after CAR-T therapy. These findings suggest that morphologic patterns in the TME and engagement of the PD-1/PD-L1 axis in pretreatment biopsies may impact CD19 CAR-T immunotherapy response in patients with LBCL.

Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1.

Tryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations. Sequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff etal. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort. Risk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells. Our study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.

Identifying and validating angiogenesis-related genes remodeling tumor microenvironment and suppressing immunotherapy response in gastric cancer

Angiogenesis significantly correlates with tumor microenvironment remodeling and immunotherapy response. Our study aimed to construct a prognostic angiogenesis-related model for gastric cancer. Using public database, a angiogenetic related five-gene (FGF1, GRB14, PAK3, PDGFRA, and PRKD1) model was identified. The top 25 % of patients were defined as high-risk, and the remaining as low-risk. The area under the curve for 1-, 3-, and 5-year overall survival (OS) were 0.646, 0.711, and 0.793, respectively. Survival analysis showed a better 10-year OS in low-risk patients in the construction (HR = 0.57, p = 0.002) and validation cohorts. GO and GSEA revealed that DEGs were enriched in extracellular matrix receptor interactions, dendritic cell antigen processing/presentation regulation, and angiogenesis pathways. CIBERSORT analysis revealed abundant naïve B cells, resting mast cells, resting CD4+ memory T cells, M2 macrophages, and monocytes in high-risk subgroups. The TIMER database showed strong positive correlations between PAK3, FGF1, PRKD1, and PDGFRA expression levels and the infiltration of CD4+ T cells and macrophages. The IOBR analysis revealed an immunosuppressive environment in the high-risk subgroup. Low-risk patients show a higher response rate to anti-PD1 treatment. TMA showed that FGF1 overexpression was associated with poor prognosis and CD4+ T cells and macrophage infiltration. In vivo study based on the 615 mice indicated that inhibiting FGF1 function could suppress tumor growth and enhance anti-PD1 therapeutic efficacy. In summary, we established a five-angiogenesis-related gene model to predict survival outcomes and immunotherapy responses in patients with gastric cancer and identified FGF1 as a prognostic gene and potential target for improving immune treatment.

The use of peripheral CD3+γδ+Vδ2+ T lymphocyte cells in combination with the ALBI score to predict immunotherapy response in patients with advanced hepatocellular carcinoma: a retrospective study

BackgroundCurrently, there is a lack of effective indicators for predicting the efficacy of immunotherapy in patients with advanced hepatocellular carcinoma (HCC). This study aimed to investigate the expression and prognostic value of peripheral T lymphocyte subsets in advanced HCC.MethodsPatients with advanced HCC who were treated with immune checkpoint inhibitors (ICIs) from December 2021 to December 2023 were included in the study. Flow cytometry was used to detect lymphocyte subsets before treatment. The patients were divided into disease control (DC) and nondisease control (nDC) groups based on treatment efficacy. Relationships between the clinical characteristics/peripheral T lymphocytes and immunotherapy efficacy were analyzed. The effectiveness of peripheral T lymphocyte subsets in predicting immunotherapy efficacy for patients with advanced HCC was analyzed using receiver operating characteristic (ROC) curves.ResultsA total of 40 eligible patients were included in this study. Non-DC was significantly associated with higher albumin-bilirubin (ALBI) scores. The percentages of γδ+Vδ2+PD1+ T cells and γδ+Vδ2+Tim3+ T cells were greater in the nDC group than in the DC group. Multivariable regression analysis revealed that the ALBI score and T lymphocytes expressing γδ+Vδ2+PD1+ and γδ+Vδ2+Tim3+ were founded to be independent influencing factors. The area under the ROC curve (AUC) values for these combinations was 0.944 (95% CI, 0.882 ~ 1.000).ConclusionsThe calculation of the ALBI score and determination of the percentages CD3+γδ+Vδ2+PD1+ T lymphocytes and CD3+γδ+Vδ2+Tim3+ T lymphocytes in the peripheral blood of patients with advanced HCC are helpful for predicting the patients’ responses to ICIs, helping to screen patients who may clinically benefit from immunotherapy.Retrospectively registerednumber: ChiCTR2400080409, date of registration: 2024-01-29.

Predicting the Prognosis and Immunotherapeutic Response of Triple-Negative Breast Cancer by Constructing a Prognostic Model Based on CD8+ T Cell-Related Immune Genes

ObjectiveTriple-negative breast cancer (TNBC) poses a significant challenge for treatment efficacy. CD8+ T cells, which are pivotal immune cells, can be effectively analyzed for differential gene expression across diverse cell populations owing to rapid advancements in sequencing technology. By leveraging these genes, our objective was to develop a prognostic model that accurately predicts the prognosis of patients with TNBC and their responsiveness to immunotherapy. MethodsSample information and clinical data of TNBC were sourced from The Cancer Genome Atlas and METABRIC databases. In the initial stage, we identified 67 differentially expressed genes associated with immune response in CD8+ T cells. Subsequently, we narrowed our focus to three key genes, namely CXCL13, GBP2, and GZMB, which were used to construct a prognostic model. The accuracy of the model was assessed using the validation set data and receiver operating characteristic (ROC) curves. Furthermore, we employed various methods, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, immune infiltration, and correlation analyses with CD274 (PD-L1) to explore the model's predictive efficacy in immunotherapeutic responses. Additionally, we investigated the potential underlying biological pathways that contribute to divergent treatment responses. ResultsWe successfully developed a model capable of predicting the prognosis of patients with TNBC. The areas under the curve (AUC) values for the 1-, 3-, and 5-year survival predictions were 0.618, 0.652, and 0.826, respectively. Employing this risk model, we stratified the samples into high- and low-risk groups. Through KEGG enrichment analysis, we observed that the high-risk group predominantly exhibited enrichment in metabolism-related pathways such as drug and chlorophyll metabolism, whereas the low-risk group demonstrated significant enrichment in cytokine pathways. Furthermore, immune landscape analysis revealed noteworthy variations between (PD-L1) expression and risk scores, indicating that our model effectively predicted the response of patients to immune-based treatments. ConclusionOur study demonstrates the potential of CXCL13, GBP2, and GZMB as prognostic indicators of clinical outcomes and immunotherapy responses in patients with TNBC. These findings provide valuable insights and novel avenues for developing immunotherapeutic approaches targeting TNBC.

Comprehensively prognostic and immunological analyses of GLP-1 signaling-related genes in pan-cancer and validation in colorectal cancer.

Background: Glucagon-like peptide-1 (GLP-1) has crucial impact on glycemic control and weight loss physiologically. GLP-1 receptor agonists have been approved for treatment of diabetes and obesity. Emerging evidence suggests that GLP-1 receptor agonists exert anticancer effect in tumorigenesis and development. However, the role and mechanism of GLP-1 signaling-related genes in pan-cancer still need further study. Methods: We comprehensively investigated the aberrant expression and genetic alterations of GLP-1 signaling-related genes in 33 cancer types. Next, GLP-1 signaling score of each patient in The Cancer Genome Atlas were established by the single-sample gene set enrichment analysis. In addition, we explored the association of GLP-1 signaling score with prognostic significance and immune characteristics. Furthermore, qRT-PCR and immunohistochemistry staining were applied to verify the expression profiling of GLP-1 signaling-related genes in colorectal cancer (CRC) tissues. Wound-healing assays and migration assays were carried out to validate the role of GLP-1 receptor agonist in CRC cell lines. Results: The expression profiling of GLP-1 signaling-related genes is commonly altered in pan-cancer. The score was decreased in cancer tissues compared with normal tissues and the lower expression score was associated with worse survival in most of cancer types. Notably, GLP-1 signaling score was strongly correlated with immune cell infiltration, including T cells, neutrophils, dendritic cells and macrophages. In addition, GLP-1 signaling score exhibited close association with tumor mutation burden, microsatellite instability and immunotherapy response in patients with cancer. Moreover, we found that the expression of GLP-1 signaling-related genes ITPR1 and ADCY5 were significantly reduced in CRC tissues, and GLP-1 receptor agonist semaglutide impaired the migration capacity of CRC cells, indicating its protective role. Conclusion: This study provided a preliminary understanding of the GLP-1 signaling-related genes in pan-cancer, showing the prognosis significance and potential immunotherapeutic values in most cancer types, and verified the potential anticancer effect of GLP-1 receptor agonist in CRC.

A novel CAF-cancer cell crosstalk-related gene prognostic index based on machine learning: prognostic significance and prediction of therapeutic response in head and neck squamous cell carcinoma

BackgroundCancer-associated fibroblast (CAF)-cancer cell crosstalk (CCCT) plays an important role in tumor microenvironment shaping and immunotherapy response. Current prognostic indexes are insufficient to accurately assess immunotherapy response in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to develop a CCCT-related gene prognostic index (CCRGPI) for assessing the prognosis and response to immune checkpoint inhibitor (ICI) therapy of HNSCC patients.MethodsTwo cellular models, the fibroblast-cancer cell indirect coculture (FCICC) model, and the fibroblast-cancer cell organoid (FC-organoid) model, were constructed to visualize the crosstalk between fibroblasts and cancer cells. Based on a HNSCC scRNA-seq dataset, the R package CellChat was used to perform cell communication analysis to identify gene pairs involved in CCCT. Least absolute shrinkage and selection operator (LASSO) regression was then applied to further refine the selection of these gene pairs. The selected gene pairs were subsequently subjected to stepwise regression to develop CCRGPI. We further performed a comprehensive analysis to determine the molecular and immune characteristics, and prognosis associated with ICI therapy in different CCRGPI subgroups. Finally, the connectivity map (CMap) analysis and molecular docking were used to screen potential therapeutic drugs.ResultsFCICC and FC-organoid models showed that cancer cells promoted the activation of fibroblasts into CAFs, that CAFs enhanced the invasion of cancer cells, and that CCCT was somewhat heterogeneous. The CCRGPI was developed based on 4 gene pairs: IGF1-IGF1R, LGALS9-CD44, SEMA5A-PLXNA1, and TNXB-SDC1. Furthermore, a high CCRGPI score was identified as an adverse prognostic factor for overall survival (OS). Additionally, a high CCRGPI was positively correlated with the activation of the P53 pathway, a high TP53 mutation rate, and decreased benefit from ICI therapy but was inversely associated with the abundance of various immune cells, such as CD4+ T cells, CD8+ T cells, and B cells. Moreover, Ganetespib was identified as a potential drug for HNSCC combination therapy.ConclusionsThe CCRGPI is reliable for predicting the prognosis and immunotherapy response of HSNCC patients and may be useful for guiding the individualized treatment of HNSCC patients.