Tumor Immune Dysfunction And Exclusion Research Articles

Machine learning-derived multi-omics prognostic signature of pyroptosis-related lncRNA with regard to ZKSCAN2-DT and tumor immune infiltration in colorectal cancer.

Colorectal cancer (CRC) has become the most prevalent gastrointestinal malignant tumor, ranking third (10.2%) in incidence and second (9.2%) in death among all malignancies globally. The most common histological subtype of CRC is colon adenocarcinoma (COAD), although the cause of CRC remains unknown, as there are no valid biomarkers. A thorough investigation was used to build a credible biomolecular risk model based on the pyroptosis-associated lncRNAs discovered for COAD prediction. Furthermore, Cibersort and Tumor Immune Dysfunction and Exclusion (TIDE), the methods of exploring tumor immune infiltration, were adopted in our paper to detect the effects of differential lncRNAs on the tumor microenvironment. Finally, quantitative real-time polymerase chain reaction (qPCR), as the approach of exploring expressions, was utilized on four different cell lines. Seven pyroptosis-related lncRNAs have been identified as COAD predictive risk factors. Cox analysis, both univariate and multivariate, revealed that the established signature might serve as a novel independent factor with prognostic meaning in COAD patients. ZKSCAN2-DT was shown to be considerably overexpressed in the COAD cell line when compared to normal human colonic epithelial cells. Furthermore, ssGSEA analysis results revealed that the immune infiltration percentage of most immune cells dropped considerably as ZKSCAN2-DT expression increased, implying that ZKSCAN2-DT may play an important role in COAD immunotherapy. Our research is the first to identify pyroptosis-related lncRNAs connected with COAD patient prognosis and to construct a predictive prognosis signature, directing COAD patient prognosis in therapeutic interventions.

A Prognosis and Immunological Study of Cellular Aging Related lncRNAs in Lung Adenocarcinoma

Background: Recently, cellular senescence has been recognized as a novel hallmark of cancer. However, the mechanisms that control cellular senescence remain poorly understood. This study seeks to discover long non-coding RNAs (lncRNAs) that are linked to senescence and can predict outcomes in lung adenocarcinoma (LUAD) patients. Methods: Using RNA sequencing data from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and senescence genes from the CellAge database, a group of lncRNAs related to senescence was initially identified. Subsequently, through univariate and multivariate Cox regression analyses, a senescence-related lncRNA signature (SenLncSig) linked to the prognosis of LUAD was developed. LUAD patients were categorized into high-risk and low-risk groups based on the median risk score of SenLncSig. Kaplan-Meier analysis was employed to assess differences in overall survival (OS) between these subgroups. Additionally, disparities in Gene Set Enrichment Analysis (GSEA), immune infiltration, scores from the tumor immune dysfunction and exclusion (TIDE) module, and choices of chemotherapy and targeted therapy were also evaluated between the high-risk and low-risk groups. Results: Based on univariate and multivariate Cox regression analyses, we established a prognostic model composed of seven senescence-related lncRNAs, named SenLncSig, which includes AL031775.2, AC026355.1, AL365181.2, AC090559.1, AL606489.1, AL513550.1, and C20orf197. This model divides patients into high-risk and low-risk groups, with the high-risk group showing significantly shorter overall survival compared to the low-risk group, with a hazard ratio (HR) of 1.326. The predictive accuracy of SenLncSig was validated through ROC curves and principal component analysis (PCA). Additionally, we developed an intuitive survival prediction tool that combines SenLncSig with clinical pathological features to assess patients' overall survival (OS). Enrichment analysis (GSEA) revealed that SenLncSig is involved in multiple pathways related to tumor immune modulation. Risk model analysis indicated significant differences between the high and low-risk groups in terms of immune status and responses to immunotherapy, chemotherapy, and targeted therapy. Conclusion: In this study, a lncRNA signature named as SenLncSig was developed that not only identifies the senescence phenotype and predicts prognosis but also has the capability to forecast the response of LUAD to immunotherapy.

Tumor immune dysfunction and exclusion subtypes in bladder cancer and pan-cancer: a novel molecular subtyping strategy and immunotherapeutic prediction model

BackgroundMolecular subtyping is expected to enable precise treatment. However, reliable subtyping strategies for clinical application remains defective and controversial. Given the significance of tumor immune dysfunction and exclusion (TIDE), we aimed to develop a novel TIDE-based subtyping strategy to guide personalized immunotherapy in the bladder cancer (BC).MethodsTranscriptome data of BC was used to evaluate the heterogeneity and the status of TIDE patterns. Subsequently, consensus clustering was applied to classify BC patients based on TIDE marker-genes. Patients’ clinicopathological, molecular features and signaling pathways of the different TIDE subtypes were well characterized. We also utilize the deconvolution algorithms to analyze the tumor microenvironment, and further explore the sensitivity and mechanisms of each subtype to immunotherapy. Furthermore, BC patient clinical information, real-world BC samples and urine samples were collected for the validation of our findings, which were used for RNA-seq analysis, H&E staining, immunohistochemistry and immunofluorescence staining, and enzyme-linked immunosorbent assay. Finally, we also explored the conservation of our novel TIDE subtypes in pan-cancers.ResultsWe identified 69 TIDE biomarker genes and classified BC samples into three subtypes using consensus clustering. Subtype I showed the lowest TIDE status and malignancy with the best prognosis and highest sensitivity to immune checkpoint blockade (ICB) treatment, which was enriched of metabolic related signaling pathways. Subtype III represented the highest TIDE status and malignancy with the poorest prognosis and resistance to ICB treatment, resulting from its inhibitory immune microenvironment and T cell terminal exhaustion. Subtype II was in a transitional state with intermediate TIDE level, malignancy, and prognosis. We further confirmed the existence and characteristics of our novel TIDE subtypes using real-world BC samples and collected patient clinical data. This subtyping method was proved to be more efficient than previous known methods in identifying non-responders to immunotherapy. We also propose that combining our TIDE subtypes with known biomarkers can potentially improve the sensitivity and specificity of these biomarkers. Moreover, besides guiding ICB treatment, this classification approach can assist in selecting the frontline or recommended drugs. Finally, we confirmed that the TIDE subtypes are conserved across the pan-tumors.ConclusionsOur novel TIDE-based subtyping method can serve as a powerful clinical tool for BC and pan-cancer patients, and potentially guiding personalized therapy decisions for selecting potential beneficiaries and excluding resistant patients of ICB therapy.

Exploration of prognostic and treatment markers in hepatocellular carcinoma via GPCR-related genes analysis

BackgroundG protein-coupled receptors (GPCRs), the biggest family of signaling receptors, account for 34 % of all the drug targets approved by the Food and Drug Administration (FDA). It has been gradually recognized that GPCRs are of significance for tumorigenesis, but in-depth studies are still required to explore specific mechanisms. In this study, the role of GPCRs in hepatocellular carcinoma (HCC) was elucidated, and GPCR-related genes were employed for building a risk-score model for the prognosis and treatment efficacy prediction of HCC patients. MethodsPatients’ data on HCC were sourced from the Liver Hepatocellular Carcinoma-Japan (LIRI-JP) and The Cancer Genome Atlas (TCGA) databases, while GPCR-related genes were obtained from the Molecular Signatures Database (MSigDB). Univariant and multivariant Cox regression analyses, as well as least absolute shrinkage and selection operator (LASSO) were performed with the aim of identifying differentially expressed GPCR-related genes and grouping patients. Differential expression and functional enrichment analyses were performed; protein-protein interaction (PPI) mechanisms were explored; hub genes and micro ribonucleic acid (miRNA)-target gene regulatory networks were constructed. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to evaluate immune infiltration levels and genetic variations. Sensitivity to immunotherapy and common antitumor drugs was predicted via the database Genomics of Drug Sensitivity in Cancer (GDSC). ResultsA GPCR-related risk score containing eight GPCR-related genes (atypical chemokine receptor 3 (ACKR3), C–C chemokine receptor type 3 (CCR3), CCR7, frizzled homolog 5 (FZD5), metabotropic glutamate receptor 8 (GRM8), hydroxycarboxylic acid receptor 1 (HCAR1), 5-hydroxytryptamine receptor 5A (HTR5A) and nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 6 (NLRP6)) was set up. In addition, patients were classified into groups with high and low risks. Patients in the high-risk group exhibited a worse prognosis but demonstrated a more favorable immunotherapy response rate compared with those in the low-risk group. Distinct sensitivity to chemotherapeutic drugs was observed. A clinical prediction model on the basis of GPCR-related risk scores was constructed. Areas under the curves (AUC) corresponding to one-, three- and five-year survival were 0.731, 0.765 and 0.731, respectively. ConclusionsIn this study, an efficient HCC prognostic prediction model was constructed by only GPCR-related genes, which are all potential targets for HCC treatment.

Development of a Novel Prognostic Model for Esophageal Squamous Cell Carcinoma: Insights into Immune Cell Interactions and Drug Sensitivity

Esophageal squamous cell carcinoma (ESCC) presents a five-year survival rate below 20%, underscoring the need for improved prognostic markers. Our study analyzed ESCC-specific datasets to identify consistently differentially expressed genes. A Venn analysis followed by gene network interactions revealed 23 key genes, from which we built a prognostic model using the COX algorithm (p = 0.000245, 3-year AUC = 0.967). This model stratifies patients into risk groups, with high-risk individuals showing worse outcomes and lower chemotherapy sensitivity. Moreover, a link between risk scores and M2 macrophage infiltration, as well as significant correlations with immune checkpoint genes (e.g., SIGLEC15, PDCD1LG2, and HVCR2), was discovered. High-risk patients had lower Tumor Immune Dysfunction and Exclusion (TIDE) values, suggesting potential responsiveness to immune checkpoint blockade (ICB) therapy. Our efficient 23-gene prognostic model for ESCC indicates a dual utility in assessing prognosis and guiding therapeutic decisions, particularly in the context of ICB therapy for high-risk patients.

Characterization of polyamine metabolism predicts prognosis, immune profile, and therapeutic efficacy in lung adenocarcinoma patients.

Polyamine modification patterns in lung adenocarcinoma (LUAD) and their impact on prognosis, immune infiltration, and anti-tumor efficacy have not been systematically explored. Patients from The Cancer Genome Atlas (TCGA) were classified into subtypes according to polyamine metabolism-related genes using the consensus clustering method, and the survival outcomes and immune profile were compared. Meanwhile, the geneCluster was constructed according to the differentially expressed genes (DEGs) of the subtypes. Subsequently, the polyamine metabolism-related score (PMRS) system was established using the least absolute shrinkage and selection operator (LASSO) multivariate regression analysis in the TCGA training cohort (n = 245), which can be applied to characterize the prognosis. To verify the predictive performance of the PMRS, the internal cohort (n = 245) and the external cohort (n = 244) were recruited. The relationship between the PMRS and immune infiltration and antitumor responses was investigated. Two distinct patterns (C1 and C2) were identified, in which the C1 subtype presented an adverse prognosis, high CD8+ T cell infiltration, tumor mutational burden (TMB), immune checkpoint, and low tumor immune dysfunction and exclusion (TIDE). Furthermore, two geneClusters were established, and similar findings were observed. The PMRS, including three genes (SMS, SMOX, and PSMC6), was then constructed to characterize the polyamine metabolic patterns, and the patients were divided into high- and low-PMRS groups. As confirmed by the validation cohort, the high-PMRS group possessed a poor prognosis. Moreover, external samples and immunohistochemistry confirmed that the three genes were highly expressed in tumor samples. Finally, immunotherapy and chemotherapy may be beneficial to the high-PMRS group based on the immunotherapy cohorts and low half-maximal inhibitory concentration (IC50) values. We identified distinct polyamine modification patterns and established a PMRS to provide new insights into the mechanism of polyamine action and improve the current anti-tumor strategy of LUAD.

Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma.

Fatty acids synthesis and metabolism (FASM)-driven lipid mobilization is essential for energy production during nutrient shortages. However, the molecular characteristics, physiological function and clinical prognosis value of FASM-associated gene signatures in hepatocellular carcinoma (HCC) remain elusive. The Gene Expression Omnibus database (GEO), the Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) database were utilized to acquire transcriptome data and clinical information of HCC patients. The ConsensusClusterPlus was employed for unsupervised clustering. Subsequently, immune cell infiltration, stemness index and therapeutic response among distinct clusters were decoded. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to anticipate the response of patients towards immunotherapy, and the genomics of drug sensitivity in cancer (GDSC) tool was employed to predict their response to antineoplastic medications. Least absolute shrinkage and selection operator (LASSO) regression analysis and protein-protein interaction (PPI) network were employed to construct prognostic model and identity hub gene. Single cell RNA sequencing (scRNA-seq) and CellChat were used to analyze cellular interactions. The hub gene of FASM effect on promoting tumor progression was confirmed through a series of functional experiments. Twenty-six FASM-related genes showed differential expression in HCC. Based on these FASM-related differential genes, two molecular subtypes were established, including Cluster1 and Cluster2 subtype. Compared with cluster2, Cluster1 subtype exhibited a worse prognosis, higher risk, higher immunosuppressive cells infiltrations, higher immune escape, higher cancer stemness and enhanced treatment-resistant. PPI network identified Acetyl-CoA carboxylase1 (ACACA) as central gene of FASM and predicted a poor prognosis. A strong interaction between cancer stem cells (CSCs) with high expression of ACACA and macrophages through CD74 molecule (CD74) and integrin subunit beta 1 (ITGB1) signaling was identified. Finally, increased ACACA expression was observed in HCC cells and patients, whereas depleted ACACA inhibited the stemness straits and drug resistance of HCC cells. This study provides a resource for understanding FASM heterogeneity in HCC. Evaluating the FASM patterns can help predict the prognosis and provide new insights into treatment response in HCC patients.

Comprehensive Analysis of N6-Methylandenosine-Related lncRNAs in Clear Cell Renal Cell Carcinoma: A Correlation With Prognosis, Tumor Progression, and Therapeutic Response

This study aims to develop a prognostic signature based on m6A-related lncRNAs for clear cell renal cell carcinoma (ccRCC). Differential expression analysis and Pearson correlation analysis were used to identify m6A-related lncRNAs associated with patient outcomes in The Cancer Genome Atlas (TCGA) database. Our approach led to the development of an m6A-related lncRNA risk score (MRLrisk), formulated using six identified lncRNAs: NFE4, AL008729.2, AL139123.1, LINC02154, AC124854.1 and ARHGAP31-AS1. Higher MRLrisk was identified as a risk factor for patients’ prognosis in ccRCC. Furthermore, an MRLrisk-based nomogram was developed and demonstrated as a reliable tool for prognosis prediction in ccRCC. Enrichment analysis and tumor mutation signature studies were conducted to investigate MRLrisk-related biological phenotypes. The tumor immune dysfunction and exclusion (TIDE) score was employed to infer patients’ response to immunotherapy, indicating a negative correlation between high MRLrisk and immunotherapy response. Our focus then shifted to LINC02154 for deeper exploration. We assessed LINC02154 expression in 28 ccRCC/normal tissue pairs and 3 ccRCC cell lines through quantitative real-time polymerase chain reaction (qRT-PCR). Functional experiments, including EdU incorporation, flow cytometry and transwell assays, were performed to assess the role of LINC02154 in ccRCC cell functions, discovering that its downregulation hinders cancer cell proliferation and migration. Furthermore, the influence of LINC02154 on ccRCC cells’ sensitivity to Sunitinib was explored using CCK-8 assays, demonstrating that decreased LINC02154 expression increases Sunitinib sensitivity. In summary, this study successfully developed an MRLrisk model with significant prognostic value for ccRCC and established LINC02154 as a critical biomarker and prospective therapeutic target in ccRCC management.

Tumor lymphangiogenesis index reveals the immune landscape and immunotherapy response in lung adenocarcinoma.

Lymphangiogenesis (LYM) has an important role in tumor progression and is strongly associated with tumor metastasis. However, the clinical application of LYM has not progressed as expected. The potential value of LYM needs to be further developed in lung adenocarcinoma (LUAD) patients. The Sequencing data and clinical characteristics of LUAD patients were downloaded from The Cancer Genome Atlas and GEO databases. Multiple machine learning algorithms were used to screen feature genes and develop the LYM index. Immune cell infiltration, immune checkpoint expression, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and drug sensitivity analysis were used to explore the correlation of LYM index with immune profile and anti-tumor therapy. We screened four lymphangiogenic feature genes (PECAM1, TIMP1, CXCL5 and PDGFB) to construct LYM index based on multiple machine learning algorithms. We divided LUAD patients into the high LYM index group and the low LYM index group based on the median LYM index. LYM index is a risk factor for the prognosis of LUAD patients. In addition, there was a significant difference in immune profile between high LYM index and low LYM index groups. LUAD patients in the low LYM index group seemed to benefit more from immunotherapy based on the results of TIDE algorithm. Overall, we confirmed that the LYM index is a prognostic risk factor and a valuable predictor of immunotherapy response in LUAD patients, which provides new evidence for the potential application of LYM.

Identification and validation of tryptophan metabolism-related lncRNAs in lung adenocarcinoma prognosis and immune response

BackgroundTryptophan (Trp) is an essential amino acid. Increasing evidence suggests that tryptophan metabolism plays a complex role in immune escape from Lung adenocarcinoma (LUAD). However, the role of long non-coding RNAs (lncRNAs) in tryptophan metabolism remains to be investigated.MethodsThis study uses The Cancer Genome Atlas (TCGA)-LUAD dataset as the training cohort, and several datasets from the Gene Expression Omnibus (GEO) database are merged into the validation cohort. Genes related to tryptophan metabolism were identified from the Molecular Signatures Database (MSigDB) database and further screened for lncRNAs with Trp-related expression. Subsequently, a prognostic signature of lncRNAs related to tryptophan metabolism was constructed using Cox regression analysis, (Least absolute shrinkage and selection operator regression) and LASSO analysis. The predictive performance of this risk score was validated by Kaplan–Meier (KM) survival analysis, (receiver operating characteristic) ROC curves, and nomograms. We also explored the differences in immune cell infiltration, immune cell function, tumor mutational load (TMB), tumor immune dysfunction and exclusion (TIDE), and anticancer drug sensitivity between high- and low-risk groups. Finally, we used real-time fluorescence quantitative PCR, CCK-8, colony formation, wound healing, transwell, flow cytometry, and nude mouse xenotransplantation models to elucidate the role of ZNF8-ERVK3-1 in LUAD.ResultsWe constructed 16 tryptophan metabolism-associated lncRNA prognostic models in LUAD patients. The risk score could be used as an independent prognostic indicator for the prognosis of LUAD patients. Kaplan–Meier survival analysis, ROC curves, and risk maps validated the prognostic value of the risk score. The high-risk and low-risk groups showed significant differences in phenotypes, such as the percentage of immune cell infiltration, immune cell function, gene mutation frequency, and anticancer drug sensitivity. In addition, patients with high-risk scores had higher TMB and TIDE scores compared to patients with low-risk scores. Finally, we found that ZNF8-ERVK3-1 was highly expressed in LUAD tissues and cell lines. A series of in vitro experiments showed that knockdown of ZNF8-ERVK3-1 inhibited cell proliferation, migration, and invasion, leading to cell cycle arrest in the G0/G1 phase and increased apoptosis. In vivo experiments with xenografts have shown that knocking down ZNF8-ERVK3-1 can significantly inhibit tumor size and tumor proliferation.ConclusionWe constructed a new prognostic model for tryptophan metabolism-related lncRNA. The risk score was closely associated with common clinical features such as immune cell infiltration, immune-related function, TMB, and anticancer drug sensitivity. Knockdown of ZNF8-ERVK3-1 inhibited LUAD cell proliferation, migration, invasion, and G0/G1 phase blockade and promoted apoptosis.

Uncovering a novel DNA repair-related radiosensitivity model for evaluation of radiotherapy susceptibility in uterine corpus endometrial cancer

BackgroundUterine corpus endometrial cancer (UCEC) exhibit heterogeneity in their DNA repair capacity, which can impact their response to radiotherapy. Our study aimed to identify potential DNA repair-related biomarkers for predicting radiation response in UCEC. MethodsWe conducted a thorough analysis of 497 UCEC samples obtained from TCGA database. Using LASSO regression analysis, we constructed a radiosensitivity signature and subsequently divided patients into the radiosensitive (RS) and the radioresistant (RR) groups based on their radiosensitivity index. The GSVA and GSEA were performed to explore functional annotations. The CIBERSORT and ESTIMATE algorithms were utilized to investigate the immune infiltration status of different groups. Additionally, we utilized the Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenotype Score (IPS), and pRRophetic algorithms to predict the effectiveness of different treatment modalities. ResultsWe constructed a radiosensitivity index consists of four DNA repair-related genes. Patients in the RS group demonstrated significantly improved prognosis compared to patients in the RR group when treated with radiotherapy. We observed that the RR group exhibited a higher proportion of the POLE ultra-mutated subtype, while the RR group had a higher proportion of the copy number high subtype. GSVA enrichment analysis revealed that the RS group exhibited enrichment in DNA damage repair pathways. Notably, the RS group demonstrated a higher proportion of naïve B cells and follicular helper T cells, while regulatory T cells (Tregs) and memory B cells were more abundant in the RR group. Furthermore, patients in the RS-PD-L1-high subgroup exhibited enrichment in immune-related pathways and increased sensitivity to immunotherapy, which is likely to contribute to their improved prognosis. Additionally, we conducted in vitro experiments to validate the expression of radiosensitivity genes in non-radioresistant (AN3CA) and radioresistant (AN3CA/IR) endometrial cancer cells. ConclusionsIn conclusion, our research successfully constructed a radiosensitivity signature with robust predictive capacity. These findings shed light on the association between immune activation, PD-L1 expression, and the response to immunotherapy in the context of radiotherapy.

Identification of a cisplatin resistant-based prognostic immune related gene signature in MIBC

Cisplatin resistance plays a significant role in the dismal prognosis and progression of muscle-invasive bladder cancer (MIBC). However, the strategies to predict prognosis and cisplatin resistance are inefficient, and it remains unclear whether cisplatin resistance is associated with tumor immunity. In this study, we integrated the transcriptional data from cisplatin-resistant cell lines and a TCGA-MIBC cohort to establish cisplatin-resistance-related cluster classification and a cisplatin-resistance-related gene risk score (CRRGRS). Kaplan–Meier survival curves showed that compared with those in low CRRGRS group, MIBC patients belonging to high CRRGRS group had worse prognosis in TCGA-MIBC cohort and external GEO cohorts. Meanwhile, CRRGRS was able to help forecast chemotherapy and immunotherapy response of MIBC patients in the TGCA cohort and IMvigor210 cohort. Moreover, compared with the low CRRGRS group, the high CRRGS group possessed a relatively immunosuppressive “cold tumor” phenotype with a higher tumor immune dysfunction and exclusion (TIDE) score, ESTIMATE score, stromal score and immune score and a lower immunophenoscore (IPS) score. The upregulated expression levels of immune checkpoint genes, including PD-1, PD-L1 and CTLA4, in the high CRRGRS group also further indicated that a relative immunosuppressive tumor microenvironment may exist in MIBC patients belonging to high CRRGRS group. In addition, we integrated CRRGRS and clinical characteristics with prognostic value to develop a nomogram, which could help forecast overall survival of MIBC patients. Furthermore, DIAPH3 was identified as a regulator of proliferation and cisplatin resistance in MIBC. The expression of DIAPH3 was increased in cisplatin-resistant cell lines and chemotherapy-unsensitive people. Further mechanism exploration revealed that DIAPH3 facilitated tumor proliferation and cisplatin resistance by regulating the NF-kB and epithelial-mesenchymal transition (EMT) pathways. In conclusion, the comprehensive investigations of CRRGRS increased the understanding of cisplatin resistance and provided promising insights to restrain tumor growth and overcome chemoresistance by targeting DIAPH3.

Pan-cancer and single-cell analyses identify CD44 as an immunotherapy response predictor and regulating macrophage polarization and tumor progression in colorectal cancer.

Cluster of differentiation (CD) 44 is a non-kinase cell surface transmembrane glycoprotein critical for tumor maintenance and progression. We conducted a systematic analysis of the expression profile and genomic alteration profile of CD44 in 33 types of cancer. The immune characteristics of CD44 were comprehensively explored by TIMER2.0 and CIBERSORT. In addition, the CD44 transcriptional landscape was examined at the single-cell level. Then, Pseudotime trajectory analysis of CD44 gene expression was performed using Monocle 2, and CellChat was utilized to compare the crosstalk differences between CD44+monocytes and CD44- monocytes. Tumor immune dysfunction and exclusion (TIDE) was used to evaluate the predictive ability of CD44 for immune checkpoint blockade (ICB) responses. The effects of CD44 on colorectal cancer (CRC) and macrophage polarization were investigated by knocking down the expression of CD44 in HCT-116 cell and macrophages in vitro. The expression of CD44 elevated in most cancers, predicting unfavorable prognosis. In addditon, CD44 was correlation with immune cell infiltration and key immune regulators. CD44+ monocytes had a higher information flow intensity than CD44- monocytes. CD44 had good predictive ability for immune checkpoint blockade responses. Knockdown of CD44 inhibited the proliferation, migration, and invasion of HCT-116 cell in vitro. Knockdown of CD44 inhibited M2 macrophage polarization. These findings suggest that CD44 is involved in regulating tumor development, macrophage polarization, and has certain predictive value for patient clinical prognosis and response to immunotherapy.

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets

BackgroundLung adenocarcinoma (LUAD) has a complex tumor heterogeneity. Our research attempts to clearness LUAD subtypes and build a reliable prognostic signature according to the activity changes of the hallmark and immunologic gene sets. MethodsAccording to The Cancer Genome Atlas (TCGA) - LUAD dataset, changes in marker and immune gene activity were analyzed, followed by identification of prognosis-related differential gene sets (DGSs) and their related LUAD subtypes. Survival analysis, correlation with clinical characteristics, and immune microenvironment assessment for subtypes were performed. Moreover, the differentially expressed genes (DEGs) between different subtypes were identified, followed by the construction of a prognostic risk score (RS) model and nomogram model. The tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) of different risk groups were compared. ResultsTwo LUAD subtypes were determined according to the activity changes of the hallmark and immunologic gene sets. Cluster 2 had worse prognosis, more advanced tumor and clinical stages than cluster 1. Moreover, a prognostic RS signature was established using two LUAD subtype-related DEGs, which could stratify patients at different risk levels. Nomogram model incorporated RS and clinical stage exerted good prognostic performance in LUAD patients. A shorter survival time and higher TMB were observed in the high-risk patients. ConclusionsOur findings revealed that our constructed prognostic signature could exactly predict the survival status of LUAD cases, which was helpful in predicting the prognosis and guiding personalized therapeutic strategies for LUAD.

A novel tumor mutation-related long non-coding RNA signature for predicting overall survival and immunotherapy response in lung adenocarcinoma

BackgroundImmunotherapy has changed the treatment landscape for lung cancer. This study aims to construct a tumor mutation-related model that combines long non-coding RNA (lncRNA) expression levels and tumor mutation levels in tumor genomes to detect the possibilities of the lncRNA signature as an indicator for predicting the prognosis and response to immunotherapy in lung adenocarcinoma (LUAD). MethodsWe downloaded the tumor mutation profiles and RNA-seq expression database of LUAD from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were extracted based on the cumulative number of mutations. Cox regression analyses were used to identify the prognostic lncRNA signature, and the prognostic value of the five selected lncRNAs was validated by using survival analysis and the receiver operating characteristic (ROC) curve. We used qPCR to validate the expression of five selected lncRNAs between human lung epithelial and human lung adenocarcinoma cell lines. The ImmuCellAI, immunophenoscore (IPS) scores and Tumor Immune Dysfunction and Exclusion (TIDE) analyses were used to predict the response to immunotherapy for this mutation related lncRNA signature. ResultsA total of 162 lncRNAs were detected among the differentially expressed lncRNAs between the Tumor mutational burden (TMB)-high group and the TMB-low group. Then, five lncRNAs (PLAC4, LINC01116, LINC02163, MIR223HG, FAM83A-AS1) were identified as tumor mutation-related candidates for constructing the prognostic prediction model. Kaplan‒Meier curves showed that the overall survival of the low-risk group was significantly better than that of the high-risk group, and the results of the GSE50081 set were consistent. The expression levels of PD1, PD-L1 and CTLA4 in the low-risk group were higher than those in the high-risk group. The IPS scores and TIDE scores of patients in the low-risk group were significantly higher than those in the high-risk group. ConclusionOur findings demonstrated that the five lncRNAs (PLAC4, LINC01116, LINC02163, MIR223HG, FAM83A-AS1) were identified as candidates for constructing the tumor mutation-related model which may serve as an indicator of tumor mutation levels and have important implications for predicting the response to immunotherapy in LUAD.

Abstract 1585: Tenascin C in pancreatic cancer-associated fibroblasts enhances epithelial mesenchymal transition and is associated with resistance to immune checkpoint inhibitor

Abstract Backgrounds: Tenascin C (TNC) is an extracellular matrix glycoprotein that is abundantly expressed in cancer stroma and its overexpression correlates with tumor progression in various types of cancer including pancreatic adenocarcinoma (PAAD). However, the role of TNC in regards to response to immune checkpoint inhibitor (ICI) still remains to be identified. In this study, we aimed to investigate the potential involvement of TNC in the response to ICI among PAAD patients. Materials and Methods: Transcriptomic profiles were downloaded by TCGA and GTEx database and TNC mRNA levels were compared between tumors and normal tissues. These transcriptomic data were deconvoluted using CIBERSORTx to identify specific enriched pathways in cancer cells and cancer-associated fibroblasts (CAFs) in PAAD patients. Bioinformatic programs were used to predict paracrine communications between cancer cells and cancer-associated fibroblasts (CAFs), and the Tumor Immune Dysfunction and Exclusion (TIDE) score was calculated to predict response to ICI treatment in PAAD patients. An independent immunotherapeutic cohort was applied to validate prediction values for immunotherapy. Results: TNC mRNA levels were significantly upregulated in tumors compared to normal tissues in eight cancer types including PAAD. TNC-high (TNC-H) PAAD patients (n = 138) had significant shorter overall survival than TNC-low (TNC-L) patients (n = 40) (p = 0.0125, Log-rank test). Using CIBERSORTx, TNC was estimated to be predominantly expressed in CAFs in PAAD. In pathway analysis, both cancer cells and CAFs had significant enrichments of the epithelial mesenchymal transition pathway in TNC-H patients. Ligand-receptor interaction analysis showed that TNC produced in CAFs had the highest interaction potentials to bind to integrin families such as ITGαV and ITGβ3 in cancer cells. In an independent immunotherapy data cohort, patients with TNC-H and ITGαV-high or ITGβ3-high expression were associated with poor response to ICI treatment and had shorter overall survival than those with both low expressions. Conclusions: In conclusion, these findings suggest that TNC-high CAFs can play a crucial role in tumor progression and resistance to ICI therapy in PAAD patients, and targeting TNC and its interactions with cancer cells may provide a potential strategy for improving the efficacy of ICI therapy in PAAD. Citation Format: Satoru Furuhashi, Yoshifumi Morita, Akio Matsumoto, Shinya Ida, Ryuta Muraki, Ryo Kitajima, Makoto Takeda, Hirotoshi Kikuchi, Yoshihiro Hiramatsu, Hiroya Takeuchi. Tenascin C in pancreatic cancer-associated fibroblasts enhances epithelial mesenchymal transition and is associated with resistance to immune checkpoint inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1585.

GPX8+ cancer-associated fibroblast, as a cancer-promoting factor in lung adenocarcinoma, is related to the immunosuppressive microenvironment

BackgroundCancer-associated fibroblasts (CAFs) play a crucial role in the tumor microenvironment of lung adenocarcinoma (LUAD) and are often associated with poorer clinical outcomes. This study aimed to screen for CAF-specific genes that could serve as promising therapeutic targets for LUAD.MethodsWe established a single-cell transcriptional profile of LUAD, focusing on genetic changes in fibroblasts. Next, we identified key genes associated with fibroblasts through weighted gene co-expression network analysis (WGCNA) and univariate Cox analysis. Then, we evaluated the relationship between glutathione peroxidase 8 (GPX8) and clinical features in multiple independent LUAD cohorts. Furthermore, we analyzed immune infiltration to shed light on the relationship between GPX8 immune microenvironment remodeling. For clinical treatment, we used the tumor immune dysfunction and exclusion (TIDE) algorithm to assess the immunotherapy prediction efficiency of GPX8. After that, we screened potential therapeutic drugs for LUAD by the connectivity map (cMAP). Finally, we conducted a cell trajectory analysis of GPX8+ CAFs to show their unique function.ResultsFibroblasts were found to be enriched in tumor tissues. Then we identified GPX8 as a key gene associated with CAFs through comprehensive bioinformatics analysis. Further analysis across multiple LUAD cohorts demonstrated the relationship between GPX8 and poor prognosis. Additionally, we found that GPX8 played a role in inducing the formation of an immunosuppressive microenvironment. The TIDE method indicated that patients with low GPX8 expression were more likely to be responsive to immunotherapy. Using the cMAP, we identified beta-CCP as a potential drug-related to GPX8. Finally, cell trajectory analysis provided insights into the dynamic process of GPX8+ CAFs formation.ConclusionsThis study elucidates the association between GPX8+ CAFs and poor prognosis, as well as the induction of immunosuppressive formation in LUAD. These findings suggest that targeting GPX8+ CAFs could potentially serve as a therapeutic strategy for the treatment of LUAD.

Identification and validation of a novel signature based on macrophage marker genes for predicting prognosis and drug response in kidney renal clear cell carcinoma by integrated analysis of single cell and bulk RNA sequencing.

Macrophages are found in a variety of tumors and play a critical role in shaping the tumor microenvironment, affecting tumor progression, metastasis, and drug resistance. However, the clinical relevance of marker genes associated with macrophage in kidney renal clear cell carcinoma (KIRC) has yet to be documented. In this study, we initiated a thorough examination of single-cell RNA sequencing (scRNA-seq) data for KIRC retrieved from the Gene Expression Omnibus (GEO) database and determined 244 macrophage marker genes (MMGs). Univariate analysis, LASSO regression, and multivariate regression analysis were performed to develop a five-gene prognostic signature in The Cancer Genome Atlas (TCGA) database, which could divide KIRC patients into low-risk (L-R) and high-risk (H-R) groups. Then, a nomogram was constructed to predict the survival rate of KIRC patients at 1, 3, and 5 years, which was well assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analyses (DCA). Functional enrichment analysis showed that immune-related pathways (such as immunoglobulin complex, immunoglobulin receptor binding, and cytokine-cytokine receptor interaction) were mainly enriched in the H-R group. Additionally, in comparison to the L-R cohort, patients belonging to the H-R cohort exhibited increased immune cell infiltration, elevated expression of immune checkpoint genes (ICGs), and a higher tumor immune dysfunction and exclusion (TIDE) score. This means that patients in the H-R group may be less sensitive to immunotherapy than those in the L-R group. Finally, IFI30 was validated to increase the ability of KIRC cells to proliferate, invade and migrate in vitro. In summary, our team has for the first time developed and validated a predictive model based on macrophage marker genes to accurately predict overall survival (OS), immune characteristics, and treatment benefit in KIRC patients.

A comprehensive multi-omics analysis identifies a robust scoring system for cancer-associated fibroblasts and intervention targets in colorectal cancer

BackgroundCancer-associated fibroblasts (CAF) play a critical role in promoting tumor growth, metastasis, and immune evasion. While numerous studies have investigated CAF, there remains a paucity of research on their clinical application in colorectal cancer (CRC).MethodsIn this study, we collected differentially expressed genes between CAF and normal fibroblasts (NF) from previous CRC studies, and utilized machine learning analysis to differentiate two distinct subtypes of CAF in CRC. To enable practical application, a CAF-related genes (CAFGs) scoring system was developed based on multivariate Cox regression. We then conducted functional enrichment analysis, Kaplan–Meier plot, consensus molecular subtypes (CMS) classification, and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to investigate the relationship between the CAFGs scoring system and various biological mechanisms, prognostic value, tumor microenvironment, and response to immune checkpoint blockade (ICB) therapy. Moreover, single-cell transcriptomics and proteomics analyses have been employed to validate the significance of scoring system-related molecules in the identity and function of CAF.ResultsWe unveiled significant distinctions in tumor immune status and prognosis not only between the CAF clusters, but also across high and low CAFGs groups. Specifically, patients in CAF cluster 2 or with high CAFGs scores exhibited higher CAF markers and were enriched for CAF-related biological pathways such as epithelial–mesenchymal transition (EMT) and angiogenesis. In addition, CAFGs score was identified as a risk index and correlated with poor overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), and recurrence-free survival (RFS). High CAFGs scores were observed in patients with advanced stages, CMS4, as well as lymphatic invasion. Furthermore, elevated CAFG scores in patients signified a suppressive tumor microenvironment characterized by the upregulation of programmed death-ligand 1 (PD-L1), T-cell dysfunction, exclusion, and TIDE score. And high CAFGs scores can differentiate patients with lower response rates and poor prognosis under ICB therapy. Notably, single-cell transcriptomics and proteomics analyses identified several molecules related to CAF identity and function, such as FSTL1, IGFBP7, and FBN1.ConclusionWe constructed a robust CAFGs score system with clinical significance using multiple CRC cohorts. In addition, we identified several molecules related to CAF identity and function that could be potential intervention targets for CRC patients.

Novel prognostic biomarker TBC1D1 is associated with immunotherapy resistance in gliomas.

Glioma, an aggressive brain tumor, poses a challenge in understanding the mechanisms of treatment resistance, despite promising results from immunotherapy. We identified genes associated with immunotherapy resistance through an analysis of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) databases. Subsequently, qRT-PCR and western blot analyses were conducted to measure the mRNA and protein levels of TBC1 Domain Family Member 1 (TBC1D1), respectively. Additionally, Gene Set Enrichment Analysis (GSEA) was employed to reveal relevant signaling pathways, and the expression of TBC1D1 in immune cells was analyzed using single-cell RNA sequencing (scRNA-seq) data from GEO database. Tumor Immune Dysfunction and Exclusion (TIDE) database was utilized to assess T-cell function, while Tumor Immunotherapy Gene Expression Resource (TIGER) database was employed to evaluate immunotherapy resistance in relation to TBC1D1. Furthermore, the predictive performance of molecules on prognosis was assessed using Kaplan-Meier plots, nomograms, and ROC curves. The levels of TBC1D1 were significantly elevated in tumor tissue from glioma patients. Furthermore, high TBC1D1 expression was observed in macrophages compared to other cells, which negatively impacted T cell function, impaired immunotherapy response, promoted treatment tolerance, and led to poor prognosis. Inhibition of TBC1D1 was found to potentially synergistically enhance the efficacy of immunotherapy and prolong the survival of cancer patients with gliomas. Heightened expression of TBC1D1 may facilitate an immunosuppressive microenvironment and predict a poor prognosis. BlockingTBC1D1 could minimize immunotherapy resistance in cancer patients with gliomas.