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OBJECTIVES: To provide molecular and immunological attributes mechanistic insights for the management of radiologically distinctive multiple primary lung cancer (MPLC). METHODS: The Bulk RNA-seq data of MPLC were obtained from our center. The Bulk RNA-seq data and CT images of patients with single primary lung cancer (SPLC) were obtained from GSE103584. Immune infiltration algorithms were performed to investigate the disparities in the immunological microenvironment between the two groups. Single-cell gene analysis was used to explore immune cells composition and communication relationships between cells in MPLC. RESULTS: In MPLC, 11 pure ground-glass opacity nodules (pGGN) and 10 mixed GGN (mGGN) were identified, while in SPLC, the numbers were 18 pGGN and 22 mGGN, respectively. In MPLC, compared to pGGN, mGGN demonstrated a significantly elevated infiltration of CD8+ T cells. Single-cell gene analysis demonstrated that CD8+ T cells play a central role in the signaling among immune cells in MPLC. The transcription factors including MAFG, RUNX3, and TBX21 may play pivotal roles in regulation of CD8+ T cells. Notably, compared to SPLC nodules for both mGGN and pGGN, MPLC nodules demonstrated a significantly elevated degree of tumor-infiltrating immune cells, with this difference being particularly pronounced in mGGN. There was a positive correlation between the proportion of immune cells and consolidation/tumor ratio (CTR). CONCLUSIONS: Our findings provided a comprehensive description about the difference in the immune microenvironment between pGGN and mGGN in early-stage MPLC, as well as between MPLC and SPLC for both mGGN and pGGN. The findings may provide evidence for the design of immunotherapeutic strategies for MPLC.

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Inflammatory age (iAge) is a vital concept for understanding the intricate interplay between chronic inflammation and aging in the context of cancer. However, the importance of iAge-clock-related genes (iAge-CRGs) across cancers remains unexplored. This study aimed to explore the mechanisms and applications of these genes across diverse cancer types. We analyzed profiling data from over 10,000 individuals, covering 33 cancer types, 750 small molecule drugs, and 24 immune cell types. We focused on DCBLD2's function at the single-cell level and computed an iAge-CRG score using GSVA. This score was correlated with cancer pathways, immune infiltration, and survival. A signature was then derived using univariate Cox and LASSO regression, followed by ROC curve analysis, nomogram construction, decision curve analysis, and immunocytochemistry. Our comprehensive analysis revealed epigenetic, genomic, and immunogenomic alterations in iAge-CRGs, especially DCBLD2, leading to abnormal expression. Aberrant DCBLD2 expression strongly correlated with cancer-associated fibroblast infiltration and prognosis in multiple cancers. Based on GSVA results, we developed a risk model using five iAge-CRGs, which proved to be an independent prognostic index for uveal melanoma (UVM) patients. We also systematically evaluated the correlation between the iAge-related signature risk score and immune cell infiltration. iAge-CRGs, particularly DCBLD2, emerge as potential targets for enhancing immunotherapy outcomes. The strong correlation between abnormal DCBLD2 expression, cancer-associated fibroblast infiltration, and patient survival across various cancers underscores their significance. Our five-gene risk signature offers an independent prognostic tool for UVM patients, highlighting the crucial role of these genes in suppressing the immune response in UVM.Kindly check and confirm whether the corresponding affiliation is correctly identified.I identified the affiliation is correctly.thank you.Per style, a structured abstract is not allowed so we have changed the structured abstract to an unstructured abstract. Please check and confirm.I confirm the abstract is correctly ,thank you.

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The role of aberrantly expressed proteins in tumors in driving immune-mediated control of cancer has been well documented for more than five decades. Today, we know that both aberrantly expressed normal proteins as well as mutant proteins (neoantigens) can function as tumor antigens in both humans and mice. Next-generation sequencing (NGS) and high-resolution mass spectrometry (MS) technologies have made significant advances since the early 2010s, enabling detection of rare but clinically relevant neoantigens recognized by T cells. MS profiling of tumor-specific immunopeptidomes remains the most direct method to identify mutant peptides bound to cellular MHC. However, the need for use of large numbers of cells or significant amounts of tumor tissue to achieve neoantigen detection has historically limited the application of MS. Newer, more sensitive MS technologies have recently demonstrated the capacities to detect neoantigens from fewer cells. Here, we highlight recent advancements in immunopeptidomics-based characterization of tumor-specific neoantigens. Various tumor antigen categories and neoantigen identification approaches are also discussed. Furthermore, we summarize recent reports that achieved successful tumor neoantigen detection by MS using a variety of starting materials, MS acquisition modes, and novel ion mobility devices.

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PURPOSE: The aged microenvironment plays a crucial role in tumor onset and progression. However, it remains unclear whether and how the aging of the extracellular matrix (ECM) influences cancer onset and progression. Furthermore, the mechanisms and implications of extracellular matrix senescence-related genes (ECM-SRGs) in pan-cancer have not been investigated. METHODS: We collected profiling data from over 10,000 individuals, covering 33 cancer types, 750 small molecule drugs, and 24 immune cell types, for a thorough and systematic analysis of ECM-SRGs in cancer. RESULTS: We observed a significant correlation between immune cell infiltrates and Gene Set Variation Analysis enrichment scores of ECM-SRGs in 33 cancer types. Moreover, our results revealed significant differences in immune cell infiltration among patients with copy number variations (CNV) and single nucleotide variations (SNV) in ECM-SRGs across various malignancies. Aberrant hypomethylation led to increased ECM-SRGs expression, and in specific malignancies, a connection between ECM-SRGs hypomethylation and adverse patient survival was established. The frequency of CNV and SNV in ECM-SRGs was elevated. We observed a positive correlation between CNV, SNV, and ECM-SRGs expression. Furthermore, a correlation was found between the high frequency of CNV and SNV in ECM-SRGs and poor patient survival in several cancer types. Additionally, the results demonstrated that ECM-SRGs expression could serve as a predictor of patient survival in diverse cancers. Pathway analysis unveiled the role of ECM-SRGs in activating EMT, apoptosis, and the RAS/MAPK signaling pathway while suppressing the cell cycle, hormone AR, and the response to DNA damage signaling pathway. Finally, we conducted searches in the "Genomics of Drug Sensitivity in Cancer" and "Genomics of Therapeutics Response Portal" databases, identifying several drugs that target ECM-SRGs. CONCLUSIONS: We conducted a comprehensive evaluation of the genomes and immunogenomics of ECM-SRGs, along with their clinical features in 33 solid tumors. This may provide insights into the relationship between ECM-SRGs and tumorigenesis. Consequently, targeting these ECM-SRGs holds promise as a clinical approach for cancer treatment.

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BACKGROUND: Immune checkpoint inhibitors (ICIs) have demonstrated promising outcomes in small cell lung cancer (SCLC), but not all patients benefit from it. Thus, developing precise treatments for SCLC is a particularly urgent need. In our study, we constructed a novel phenotype for SCLC based on immune signatures. METHODS: We clustered patients with SCLC hierarchically in 3 publicly available datasets according to the immune signatures. ESTIMATE and CIBERSORT algorithm were used to evaluate the components of the tumor microenvironment. Moreover, we identified potential mRNA vaccine antigens for patients with SCLC, and qRT-PCR were performed to detect the gene expression. RESULTS: We identified 2 SCLC subtypes and named Immunity High (Immunity_H) and Immunity Low (Immunity_L). Meanwhile, we obtained generally consistent results by analyzing different datasets, suggesting that this classification was reliable. Immunity_H contained the higher number of immune cells and a better prognosis compared to Immunity_L. Gene-set enrichment analysis revealed that several immune-related pathways such as cytokine-cytokine receptor interaction, programmed cell death-Ligand 1 expression and programmed cell death-1 checkpoint pathway in cancer were hyperactivated in the Immunity_H. However, most of the pathways enriched in the Immunity_L were not associated with immunity. Furthermore, we identified 5 potential mRNA vaccine antigens of SCLC (NEK2, NOL4, RALYL, SH3GL2, and ZIC2), and they were expressed higher in Immunity_L, it indicated that Immunity_L maybe more suitable for tumor vaccine development. CONCLUSIONS: SCLC can be divided into Immunity_H and Immunity_L subtypes. Immunity_H may be more suitable for treatment with ICIs. NEK2, NOL4, RALYL, SH3GL2, and ZIC2 may be act as potential antigens for SCLC.

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OBJECTIVE: To facilitate immunotherapy and prognostic assessment of non-small cell lung cancer (NSCLC), we established a novel immunogenomic classification to provide valid identification criteria. METHODS: The immune enrichment scores were calculated by single sample gene set enrichment analysis (ssGSEA) and clustered into Immunity_L and Immunity_H, and the reliability of this classification was demonstrated. Immune microenvironment score and immune cell infiltration analysis of NSCLC were also performed. Randomly divided into training group and test group, a prognosis-related immune profile was developed using least absolute shrinkage and selection operator (LASSO) and stepwise COX proportional hazards model to construct a prognostic mode. RESULTS: The risk score for this immune profile was identified as an independent prognostic factor and can be used as a powerful prognostic tool to refine tumor immunotherapy. Our study identified two NSCLC classifications based on immunomic profiling, Immunity_H and Immunity_L. CONCLUSION: In conclusion, Immunogenomic classification can distinguish the immune status of different types of NSCLC patients and contribute to the immunotherapy of NSCLC patients.

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Lynch syndrome (LS) is an inherited disorder in which affected individuals have a significantly higher-than-average risk of developing colorectal and non-colorectal cancers, often before the age of 50 years. In LS, mutations in DNA repair genes lead to a dysfunctional post-replication repair system. As a result, the unrepaired errors in coding regions of the genome produce novel proteins, called neoantigens. Neoantigens are recognised by the immune system as foreign and trigger an immune response. Due to the invasive nature of cancer screening tests, universal cancer screening guidelines unique for LS (primarily colonoscopy) are poorly adhered to by LS variant heterozygotes (LSVH). Currently, it is unclear whether immunogenomic components produced as a result of neoantigen formation can be used as novel biomarkers in LS. We hypothesise that: (i) LSVH produce measurable and dynamic immunogenomic components in blood, and (ii) these quantifiable immunogenomic components correlate with cancer onset and stage. Here, we discuss the feasibility to: (a) identify personalised novel immunogenomic biomarkers and (b) validate these biomarkers in various clinical scenarios in LSVH.

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Background: The tumor microenvironment (TME) performs a crucial function in the tumorigenesis and response to immunotherapies of clear cell renal cell carcinoma (ccRCC). However, a lack of recognized pre-clinical TME-based risk models poses a great challenge to investigating the risk factors correlated with prognosis and treatment responses for patients with ccRCC. Methods: Stromal and immune contexture were assessed to calculate the TMErisk score of a large sample of patients with ccRCC from public and real-world cohorts using machine-learning algorithms. Next, analyses for prognostic efficacy, correlations with clinicopathological features, functional enrichment, immune cell distributions, DNA variations, immune response, and heterogeneity were performed and validated. Results: Clinical hub genes, including INAFM2, SRPX, DPYSL3, VSIG4, APLNR, FHL5, A2M, SLFN11, ADAMTS4, IFITM1, NOD2, CCR4, HLA-DQB2, and PLAUR, were identified and incorporated to develop the TMErisk signature. Patients in the TMEhigh risk group (category) exhibited a considerably grim prognosis, and the TMErisk model was shown to independently function as a risk indicator for the overall survival (OS) of ccRCC patients. Expression levels of immune checkpoint genes were substantially increased in TMEhigh risk group, while those of the human leukocyte antigen (HLA) family genes were prominently decreased. In addition, tumors in the TMEhigh group showed significantly high infiltration levels of tumor-infiltrated lymphocytes, including M2 macrophages, CD8+ T cells, B cells, and CD4+ T cells. In heterogeneity analysis, more frequent somatic mutations, including pro-tumorigenic BAP1 and PBRM1, were observed in the TMEhigh group. Importantly, 19.3% of patients receiving immunotherapies in the TMEhigh group achieved complete or partial response compared with those with immune tolerance in the TMElow group, suggesting that TMErisk prominently differentiates prognosis and responses to immunotherapy for patients with ccRCC. Conclusions: We first established the TMErisk score of ccRCC using machine-learning algorithms based on a large-scale population. The TMErisk score can be utilized as an innovative independent prognosis predictive marker with high sensitivity and accuracy. Our discovery also predicted the efficacy of immunotherapy in ccRCC patients, indicating the intimate link between tumor immune microenvironment and intratumoral heterogeneity.

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Messenger RNA vaccines are considered to be a promising strategy in cancer immunotherapy, while their application on mesothelioma is still largely uncharacterized. This study aimed to identify potential antigens in mesothelioma for anti-mesothelioma mRNA vaccine development, and further determine the immune subtypes of mesothelioma for selection of suitable candidates from an extremely heterogeneous population. Gene expression data and corresponding clinicopathological information were obtained from the TCGA and gene expression omnibus, respectively. Then, the genetic alterations were compared and visualized using cBioPortal, and differentially expressed genes and their prognostic signatures were identified by GEPIA. The relationship between tumor-infiltrating immune cells and the expression of tumor antigens was systematically evaluated by TIMER online. Finally, the immune subtypes and immune landscape of mesothelioma were separately analyzed using consensus cluster and graph learning-based dimensional reduction. A total of five potential tumor antigens correlated with prognosis and infiltration of antigen-presenting cells, including AUNIP, FANCI, LASP1, PSMD8, and XPO5 were identified. Based on the expression of immune-related genes, patients with mesothelioma were divided into two immune subtypes (IS1 and IS2). Each subtype exhibited differential molecular, cellular and clinical properties. Patients with the IS1 subtype were characterized by an immune "cold" phenotype, displaying superior survival outcomes, whereas those with the IS2 subtype were characterized by an immune "hot" and immunosuppressive phenotype. Furthermore, immune checkpoints and immunogenic cell death modulators were differentially expressed between the IS1 and IS2 immune subtype tumors. The immunogenomic landscape of mesothelioma revealed a complex tumor immune microenvironment between individual patients. AUNIP, FANCI, LASP1, PSMD8, and XPO5 are putative antigens for the development of anti-mesothelioma mRNA vaccine and patients with the IS1 subtype may be considered for vaccination.

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Background: Thyroid nodules are an extremely common entity, and surgery is considered the ultimate diagnostic strategy in those with unclear malignant potential. Unfortunately, strategies aiming to predict the risk of malignancy have inadequate specificity. Our group recently found that the microenvironment of thyroid cancer is characterized by an enhanced immune invasion and activated immune response mediated by double-negative T lymphocytes (DN T) (CD3+CD4-CD8-), which are believed to enable or promote tumorigenesis. In the present work, we try to use the DN T cells' proportion in thyroid fine-needle aspiration (FNA) material as a predictor of the risk of malignancy. Methods: We recruited 127 patients and obtained ultrasound-guided FNA samples from subjects with cytology-positive or suspicious for malignancy and from those with benign nodular goiter associated with compressive symptoms (such as dysphagia, shortness of breath, or hoarseness), Hashimoto thyroiditis, and Graves' disease. Out of 127, we investigated 46 FNA samples of patients who underwent total thyroidectomy and for which postoperative histological diagnosis by the academic pathologists was available. We specifically measured the number of cells expressing CD3+CD4-CD8- (DN T) as a function of total CD3+ cells in FNA samples using flow cytometry. We correlated their FNA DN T-cell proportions with the pathological findings. Results: The DN T cells were significantly more abundant in lymphocytic infiltrates of thyroid cancer cases compared to benign nodule controls (p < 0.0001). When the DN T-cell population exceeded a threshold of 9.14%, of total CD3+ cells, the negative likelihood ratio of being cancer-free was 0.034 (96.6% sensitivity, 95% CI, 0.915-1.000, p < 0.0001). DN T cells at <9.14% were not found in any subject with benign disease (specificity 100%). The high specificity of the test is promising, since it abolishes a false-positive diagnosis and in turn unnecessary surgical procedures. Conclusion: The present study proposes DN T cells' proportion as a preoperative diagnostic signature for thyroid cancer that with integration of RNA transcriptomics can provide a simplified technology based on the PCR assay for the ease of operation.

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HER2 positive BC is heterogeneous. But few studies discussed the classification of HER2-positive BC based on immune-related signatures. Using three publicly BC genomics datasets, we classified HER2 positive BC based on 33 immune-related signatures and used unsupervised machine learning methods to predict and perform the classification. We grouped three HER2-positive BC subtypes that we called Immune-High (IM-H), Immune-Medium (IM-M), and Immune-Low (IM-L), and manifested this categorization was predictable, duplicable and reliable by analyzing another dataset. Compared to other subtypes, IM-H had a higher immune cell infiltration level and stronger anti-tumor immune activities, as well as better clinical survival outcome. Besides these signatures, there were some cancer-related pathways which were hyperactivated in IM-H, including cytokine-cytokine receptor interactions, antigen processing and presentation pathways, natural killer cell-mediated cytotoxicity, Th1 and Th2 cell differentiation, chemokine signaling pathway, Th17 cell differentiation, B and T cell receptor signaling, NF-kappa B signaling, PD-L1 expression and PD-1 checkpoint pathway in cancer, TNF signaling, IL-17 signaling, NOD-like receptor signaling and Toll-like receptor signaling. By contrast, IM-L showed depressed immune-related signatures and enhanced activation of lycosylphosphatidylinositol-anchor biosynthesis and mismatch repair. Moreover, we discovered a gene co-expression network focused on eight transcription factor genes (EOMES, TBX21, GFI1, IRF4, POU2AF1, CIITA, FOXP3 and TOX) and one tumor suppress gene (PRF1), which were closely related with tumor immune. We identified three HER2-positive BC subtypes based on immune-related signatures, which had potential clinical implications and promoted the optimal stratification of HER2-positive BC responsive to immunotherapy.

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BACKGROUND: Bladder cancer (BC) is a common urinary tract system tumor with high recurrence rate and different populations show distinct response to immunotherapy. Novel biomarkers that can accurately predict prognosis and therapeutic responses are urgently needed. Here, we aim to identify a novel prognostic and therapeutic responses immune-related gene signature of BC through a comprehensive bioinformatics analysis. METHODS: The robust rank aggregation was conducted to integrate differently expressed genes (DEGs) in datasets of the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO). Lasso and Cox regression analyses were performed to formulate a novel mRNA signature that could predict prognosis of BC patients. Subsequently, the prognostic value and predictive value of the signature was validated with two independent cohorts GSE13507 and IMvigor210. Finally, quantitative Real-time PCR (qRT-PCR) analysis was conducted to determine the expression of mRNAs in BC cell lines (UM-UC-3, EJ-1, SW780 and T24). RESULTS: We built a signature comprised the eight mRNAs: CNKSR1, COPZ2, CXorf57, FASN, PCOLCE2, RGS1, SPINT1 and TPST1. Our prognostic signature could be used to stratify BC population into two risk groups with distinct immune profile and responsiveness to immunotherapy. The results of qRT-PCR demonstrated that the eight mRNAs exhibited different expression levels in BC cell lines. CONCLUSION: Our study constructed a convenient and reliable 8-mRNA gene signature, which might provide prognostic prediction and aid treatment decision making of BC patients in clinical practice.

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BACKGROUND: Multiple molecular subtypes with distinct clinical outcomes in gastric cancer have been identified. Nonetheless, the immunogenomic subtypes of gastric cancer and its mediated tumor microenvironment (TME) characterizations have not been fully understood. METHODS: Six gastric cancer cohorts with 1506 samples were obtained. Unsupervised methods were used to perform immunogenomic phenotype clustering. The least absolute shrinkage and selection operator regression method was used to construct immunogenomic characterization score (IGCS). RESULTS: Three distinct immunogenomic phenotypes were determined. We observed a prominent survival difference between three phenotypes. The TME cell-infiltrating characteristics under these three phenotypes were highly consistent with three immune subtypes of tumors. Cluster 1, was characterized by the "immune-desert" phenotype, with relatively lower cell infiltration level (type 1 "cold tumor"); Cluster 2, characterized by "immune-inflamed" phenotype, with abundant innate and adaptive immune cell infiltration ("hot tumor"); Cluster 3, characterized by "immune-excluded" phenotype, with significant stromal activation and inactivated immune cell infiltration (type 2 "cold tumor"). We demonstrated IGCS signature was significantly correlated with TME inflammation and stroma activity, molecular subtypes, genetic variation, microsatellite instability, immune checkpoint molecules, and patient prognosis. High IGCS subtype, with poorer survival and enhanced stromal activity, presented an immune-exclusion and non-inflamed TME characterization. Low IGCS, related to increased mutation/neoantigen load and microsatellite instability, showed enhanced responses to anti-checkpoint immunotherapy. Four immunotherapy cohorts confirmed patients with low IGCS exhibited prominently enhanced clinical responses and treatment advantages. CONCLUSIONS: This study demonstrated the immunogenomic characterizations could play a crucial role in shaping the complexity and diversity of tumor microenvironment. Targeting tumor immunogenomic characteristic in order for changing adverse phenotypes may contribute to exploiting the novel immunotherapy combination strategies or novel immunotherapeutic drugs, and promoting the advance of tumor personalized immunotherapy.

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Head and neck squamous cell carcinoma (HNSCC) is an urgent public health issue due to its poor prognosis and resistance to anti-cancer agents. However, the role of cuproptosis, a newly identified form cell death, in applications of HNSCC is still not a known. In this study, single-cell RNA sequencing data was used to explore cuproptosis-related gene expression in the tumour microenvironment. A prognostic model was constructed based on the cuproptosis-related lncRNA. Various methods were performed to predict the overall survival (OS) of different risk score patients and explore difference in enrichment function and pathways between the risk score patients. Finally, a series of immunogenomic landscape analyses were performed and evaluated the immune function, immune infiltration and sensitivity to chemotherapeutic agents. Cancer cell cluster expressed the essential cuproptosis-related gene. As the risk score increased of HNSCC patients, a significant decrease in survival status and time occurred for patients in the high-risk score patient. The AUC for predicting 1-, 3-, and 5-years OS were 0.679, 0.713 and 0.656, indicating that the model regarded as an independent prognostic signature in comparison with the clinical-pathological characteristics. As a results of GO, the immune function and immune infiltration of different risk score patients were assessed, revealing significant differences in T cell function and abundance of different types of T cells. Low-risk score patients are relatively insensitive to chemotherapy agents such as docetaxel and cisplatin, and easily resistant to immunotherapy. A cuproptosis-related lncRNA prognostic model was constructed to predict OS of HNSCC patients and provided the newly therapeutic strategies.

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This study was designed to construct a prognostic risk model to predict prognosis and immunotherapy response of bladder cancer (BCa) patinets. 350 differential expressed immune-related genes (DEIRGs) were obtained according to the transcriptome profiling and immune-related genes from the Cancer Genome Atlas (TCGA) database and ImmPort database, respectively. A prognostic risk model was constructed based on 15 hub genes through univariate, multivariate, and LASSO Cox regression analyses. The area under the receiver operating characteristic (ROC) curve was 0.743, indicating the superiority of the model. The scatter plot showed that as the risk score increased, the overall survival decreased significantly. In addition, all results were internally verified by the TCGA cohort. The model showed that the higher the grade, clinical stage, and TNM stage of BCa, the higher the risk score of patients. The tumor mutation burden of the low-risk group was generally higher than that of the high-risk group. Immune cell infiltration analysis showed that CD8 T cells, naive CD4 T cells, follicular helper T cells and M0 Macrophage were significantly different between the two groups. Several key immune checkpoint genes were found to be significantly different between the two groups, such as CTLA4, PD-L1, CD47, CD276, CXCL8, and HAVCR2/TIM3. Finally, the analysis of immunotherapy revealed that the efficacy of CTLA4 or PD1 blockers alone was better in the low-risk group than in the high-risk group. Taken together, we developed and validated a prognostic risk model based on 15 hub genes, which performed well in predicting prognosis and immunotherapy response of BCa patients.

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BACKGROUND: Advanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling. METHODS: Single-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro. RESULTS: Three immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis. CONCLUSIONS: We identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.

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The tumor microenvironment (TME) exerts a high impact on tumor biology and immunotherapy. The heterogeneous phenotypes and the clinical significance of CD8+ T cells in TME have not been fully elucidated. Here, a comprehensive immunogenomic analysis based on multi-omics data was performed to investigate the clinical significance and tumor heterogeneity between CD8+ T cell-related molecular clusters. We identified two distinct molecular clusters of ccRCC (C1 and C2) in TCGA and validated in E-MTAB-1980 cohorts. The C1 cluster was characterized by unfavorable prognosis, increased expression levels of CD8+ T cell exhaustion markers, high immune infiltration levels as well as more immune escape mechanisms. The C2 cluster was featured by favorable prognosis, elevated expression levels of CD8+ T cell effector markers, low load of copy number loss and low frequency of 9p21.3 deletion. Moreover, the effect of molecular classifications on Nivolumab therapeutic efficacy in the CheckMate 025 cohort was examined, and the C2 cluster exhibited a better prognosis. Taken together, we determine two CD8+ T cell-related molecular clusters in ccRCC, and provide new insights for evaluating the functions of CD8+ T cells. Our molecular classification is a potential strategy for prognostic prediction and immunotherapeutic guidance for ccRCC patients.

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The tumorigenesis of skin cutaneous melanoma (SKCM) remains unclear. The tumor microenvironment (TME) is well known to play a vital role in the onset and progression of SKCM. However, the dynamic mechanisms of immune regulation are insufficient. We conducted a comprehensive analysis of immune cell infiltration in the TME. Based on the differentially expressed genes (DEGs) in clusters grouped by immune infiltration status, a set of hub genes related to the clinical prognosis of SKCM and tumor immune infiltration was explored. Methods: We analyzed immune cell infiltration in two independent cohorts and assessed the relationship between the internal pattern of immune cell infiltration and SKCM characteristics, including clinicopathological features, potential biological pathways, and gene mutations. Genes related to the infiltration pattern of TME immune cells were determined. Furthermore, the unsupervised clustering method (k-means) was used to divide samples into three different categories according to TME, which were defined as TME cluster-A, -B, and -C. DEGs among three groups of samples were analyzed as signature genes. We further distinguished common DEGs between three groups of samples according to whether differences were significant and divided DEGs into the Signature gene-A group with significant differences and the Signature gene-B group with insignificant differences. The Signature gene-A gene set mainly had exon skipping in SKCM, while the Signature gene-B gene set had no obvious alternative splicing form. Subsequently, we analyzed genetic variations of the two signatures and constructed a competing endogenous RNA (ceRNA) regulatory network. LASSO Cox regression was used to determine the immune infiltration signature and risk score of SKCM. Finally, we obtained 13 hub genes and calculated the risk score based on the coefficient of each gene to explore the impact of the high- and low-risk scores on biologically related functions and prognosis of SKCM patients further. The correlation between the risk score and clinicopathological characteristics of SKCM patients indicated that a low-risk score was associated with TME cluster-A classification (p < 0.001) and metastatic SKCM (p < 0.001). Thirteen hub genes also showed different prognostic effects in pan-cancer. The results of univariate and multivariate Cox analyses revealed that risk score could be used as an independent risk factor for predicting the prognosis of SKCM patients. The nomogram that integrated clinicopathological characteristics and immune characteristics to predict survival probability was based on multivariate Cox regression. Finally, 13 hub genes that showed different prognostic effects in pan-cancers were obtained. According to immunohistochemistry staining results, Ube2L6, SRPX2, and IFIT2 were expressed at higher levels, while CLEC4E, END3, and KIR2DL4 were expressed at lower levels in 25 melanoma specimens. Conclusion: We performed a comprehensive assessment of the immune-associated TME. To elucidate the potential development of immune-genomic features in SKCM, we constructed an unprecedented set of immune characteristic genes (EDN3, CLEC4E, SRPX2, KIR2DL4, UBE2L6, and IFIT2) related to the immune landscape of TME. These genes are related to different prognoses and drug responses of SKCM. The immune gene signature constructed can be used as a robust prognostic biomarker of SKCM and a predictor of an immunotherapy effect.

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Pancreatic cancer is a highly aggressive disease with poor prognosis. N6-methyladenosine (m6A) is critical for post-transcriptional modification of messenger RNA (mRNA) and long non-coding RNA (lncRNA). However, the m6A-associated lncRNAs (m6A-lncRNA) and their values in predicting clinical outcomes and immune microenvironmental status in pancreatic cancer patients remain largely unexplored. This study aimed to evaluate the importance of m6A-lncRNA and established a m6A-lncRNA signature for predicting immunotherapeutic response and prognosis of pancreatic cancer. The m6A-lncRNA co-expression networks were constructed using data from the TCGA and GTEx database. Based on the least absolute shrinkage and selection operator (LASSO) analysis, we constructed an 8 m6A-lncRNA signature risk model, and selection operator (LASSO) analysis, and stratified patients into the high- and low-risk groups with significant difference in overall survival (OS) (HR = 2.68, 95% CI = 1.74-4.14, P < 0.0001). Patients in the high-risk group showed significantly reduced OS compared to patients in the low-risk group (P < 0.001). The clinical characteristics and m6A-lncRNA risk scores were used to construct a nomogram which accurately predicted the OS in pancreatic cancer. TIMER 2.0 were used to investigate tumor immune infiltrating cells and its relationship with pancreatic cancer. CIBERSORT analysis revealed increased higher infiltration proportions of M0 and M2 macrophages, and lower infiltration of naive B cell, CD8+ T cell and Treg cells in the high-risk group. Compared to the low-risk group, functional annotation using ssGSEA showed that T cell infiltration and the differential immune-related check-point genes are expressed at low level in the high-risk group (P < 0.05). In summary, our study constructed a novel m6A-associated lncRNAs signature to predict immunotherapeutic responses and provided a novel nomogram for the prognosis prediction of pancreatic cancer.

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The immunosuppressive mechanisms of the surrounding microenvironment and distinct immunogenomic features in glioblastoma (GBM) have not been elucidated to date. To fill this gap, useful data were extracted from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, GSE43378, GSE23806, and GSE12907. With the ssGSEA method and the ESTIMATE and CIBERSORT algorithms, four microenvironmental signatures were used to identify glioma microenvironment genes, and the samples were reasonably classified into three immune phenotypes. The molecular and clinical features of these phenotypes were characterized via key gene set expression, tumor mutation burden, fraction of immune cell infiltration, and functional enrichment. Exhausted CD8+ T cell (GET) signature construction with the predictive response to commonly used antitumor drugs and peritumoral edema assisted in further characterizing the immune phenotype features. A total of 2,466 glioma samples with gene expression profiles were enrolled. Tumor purity, ESTIMATE, and immune and stromal scores served as the 4 microenvironment signatures used to classify gliomas into immune-high, immune-middle and immune-low groups, which had distinct immune heterogeneity and clinicopathological characteristics. The immune-H phenotype had higher expression of four immune signatures; however, most checkpoint molecules exhibited poor survival. Enriched pathways among the subtypes were related to immunity. The GET score was similar among the three phenotypes, while immune-L was more sensitive to bortezomib, cisplatin, docetaxel, lapatinib, and rapamycin prescriptions and displayed mild peritumor edema. The three novel immune phenotypes with distinct immunogenetic features could have utility for understanding glioma microenvironment regulation and determining prognosis. These results contribute to classifying glioma subtypes, remodeling the immunosuppressive microenvironment and informing novel cancer immunotherapy in the era of precision immuno-oncology.

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