High Immune Cell Infiltration Research Articles

A signature-based classification of lung adenocarcinoma that stratifies tumor immunity.

Immune-related subgroup classification in immune checkpoint blockade (ICB) therapy is largely inconclusive in lung adenocarcinoma (LUAD). First, the single-sample Gene Set Enrichment Analysis (ssGSEA) and K-means algorithms were used to identify immune-based subtypes for the LUAD cohort based on the immunogenomic profiling of 29 immune signatures from The Cancer Genome Atlas (TCGA) database (n = 504). Second, we examined the prognostic and predictive value of immune-based subtypes using bioinformatics analysis. Survival analysis and additional COX proportional hazards regression analysis were conducted for LUAD. Then, the immune score, tumor-infiltrating immune cells (TIICs), and immune checkpoint expression of the three subtypes were analyzed. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) of the differentially expressed genes (DEGs) between three immune-based subtypes were subsequently analyzed for functional enrichment pathways. A total of three immune-based subtypes with distinct immune signatures have been identified for LUAD and designated as cluster 1 (C1), cluster 2 (C2), and cluster 3 (C3). Patients in C3 had higher stromal, immune, and ESTIMATE scores, whereas those in C1 had the opposite. Patients in C1 had an enrichment of macrophages M0 and activation of dendritic cells, whereas tumors in C3 had an enrichment of CD8+ T cells, activation of CD4+ memory T cells, and macrophages M1. C3 had a higher immune cell infiltration and a better survival prognosis than other subtypes. Furthermore, patients in C3 had higher expression levels of immune checkpoint proteins such as PD-L1, PD1, CTLA4, LAG3, IDO1, and HAVCR2. No significant differences were found in cluster TMB scores. We also found that immune-related pathways were enriched in C3. LUAD subtypes based on immune signatures may aid in the development of novel treatment strategies for LUAD.

Construction of an Immunity and Ferroptosis-Related Risk Score Model to Predict Ovarian Cancer Clinical Outcomes and Immune Microenvironment.

Ovarian cancer (OV) is a severe and common gynecological disease. Ferroptosis can regulate the progression and invasion of tumors. The immune system is a decisive factor in cancer. The present study aimed to use gene expression data to establish an immunity and ferroptosis-related risk score model as a prognostic biomarker to predict clinical outcomes and the immune microenvironment of OV. Common gene expression data were searched from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Immunity-related genes and ferroptosis-related genes were searched and downloaded from the ImmPort and FerrDb databases, followed by the analysis of the overall survival of patients with OV and the identification of genes. Subsequently, the status of the infiltration of immune cells and the association between immune checkpoints and risk score were assessed. A total of 10 prognostic genes (C5AR1, GZMB, IGF2R, ISG20, PPP3CA, STAT1, TRIM27, TSHR, RB1, and EGFR) were included in the immunity and ferroptosis-related risk score model. The high-risk group had a higher infiltration of immune cells. The risk score, an independent prognostic feature of OV was negatively associated with each immune checkpoint. The risk score may thus help to predict the response to immunotherapy. The immunity and ferroptosis-related risk score model is an independent prognostic factor for OV. The established risk score may help to predict the response of patients to immunotherapy.

Comprehensive analysis of fatty acid metabolism-related gene signatures for predicting prognosis in patients with prostate cancer.

Fatty acid metabolism (FAM) is an important factor in tumorigenesis and development. However, whether fatty acid metabolism (FAM)-related genes are associated with prostate cancer (PCa) prognosis is not known. Therefore, we established a novel prognostic model based on FAM-related genes to predict biochemical recurrence in PCa patients. First, PCa sequencing data were acquired from TCGA as the training cohort and GSE21032 as the validation cohort. Second, a prostate cancer prognostic model containing 10 FAM-related genes was constructed using univariate Cox and LASSO. Principal component analysis and t-distributed stochastic neighbour embedding analysis showed that the model was highly effective. Third, PCa patients were divided into high- and low-risk groups according to the model risk score. Survival analysis, ROC curve analysis, and independent prognostic analysis showed that the high-risk group had short recurrence-free survival (RFS), and the risk score was an independent diagnostic factor with diagnostic value in PCa patients. External validation using GSE21032 also showed that the prognostic model had high reliability. A nomogram based on a prognostic model was constructed for clinical use. Fourth, tumor immune correlation analyses, such as the ESTIMATE, CIBERSORT algorithm, and ssGSEA, showed that the high-risk group had higher immune cell infiltration, lower tumour purity, and worse RFS. Various immune checkpoints were expressed at higher levels in high-risk patients. In summary, this prognostic model is a promising prognostic biomarker for PCa that should improve the prognosis of PCa patients. These data provide new ideas for antitumour immunotherapy and have good potential value for the development of targeted drugs.

Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA.

This study aimed to construct an immune-related prognostic model and a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) of breast cancer patients. We applied single-sample gene set enrichment analysis to classify 1,053 breast cancer samples from The Cancer Genome Atlas (TCGA) database into high and low immune cell infiltration clusters. In cluster construction and validation, the R packages "GSVA," "hclust," "ESTIMATE," and "CIBERSORT" and GSEA software were utilized. ImmPort, univariate Cox regression analysis, and Venn analysis were then used to identify 42 prognostic immune-related genes. Eventually, the genes TAPBPL, RAC2, IL27RA, ULBP2, PSMB8, SOCS3, NFKBIE, IGLV6-57, CXCL1, IGHD, AIMP1, and CXCL13 were chosen for model construction utilizing least absolute shrinkage and selection operator regression analysis. The Kaplan-Meier curves of both the training and validation sets indicated that the overall survival of patients in the low-risk group was superior to that of patients in the high-risk group (p < .05). The areas under curves (AUCs) of the model at 1, 3, and 5years were, respectively, .697, .710, and .675 for the training set and .930, .688, and .712 for the validation set. Regarding clinicopathologic characteristics, breast cancer-related genes, and tumor mutational burden, effective differentiation was achieved between high-risk and low-risk groups. A nomogram integrating the risk model and clinicopathologic factors was constructed using the "rms" R software package. The nomogram's 1-, 3-, and 5-year AUCs were .828, .783, and .751, respectively. Overall, our study developed an immune-related model and a nomogram that could reliably predict OS for breast cancer patients, and offered insights into tumor immune and pathological mechanisms.

Identification and Validation of Ferroptosis-Related Subtypes and a Predictive Signature in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with an immunosuppressive Tumor microenvironment (TME). Ferroptosis plays an essential role in tumor proliferation, invasion, and metastasis. However, the relationship between ferroptosis and TME of HCC has remained elusive. Differentially expressed ferroptosis-related genes (DE FRGs) between normal liver tissues and HCC tissues were obtained from The Cancer Genome Atlas (TCGA). On this basis, we identified the molecular subtypes mediated by DE FRGs and TME cell infiltration. Next, a predictive signature was established to quantity the ferroptosis-related characteristics by performing the least absolute shrinkage and selection operator Cox regression analyses. Univariate and multivariate COX analyses determined the independent prognostic factors. Finally, the expression stability of 3 ferroptosis-related signature genes was verified in cancer and paracancerous normal tissues of HCC. We identified three different molecular subtypes and found that the subtype with the better prognosis was associated with high enrichment of immune- and metabolic-related hallmark signaling pathways and high infiltration of immune cells in TME. The signature was considered to be an independent prognostic factor. We also found that the signature can reflect the infiltration characteristics of different immune cells in TME. Immunosuppressive cells such as myeloid-derived suppressor cells (MDSCs), regulatory T cells, and type 17 T helper cells were significantly enriched in the high-risk group. The analysis data of immune checkpoints and tumor mutation load indicated that the signature had great potential in predicting Immunotherapy response and chemotherapeutic sensitivity. In addition, the overexpression of 3 ferroptosis-related signature genes was confirmed in HCC tissues and HCC cell lines. Ferroptosis inducer RSL3 inhibited the proliferation of HCC cells and was a potential cancer immunotherapy agent. These findings enhanced our understanding of the regulatory mechanism of ferroptosis in HCC and provided new insights into evaluating prognosis and developing more effective Immunotherapy and chemotherapy strategies.

Abstract C111: Spatial transcriptome profiling of tumor and tumor microenvironment of triple negative breast cancer in patients of African American and European American descent

Abstract Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype that disproportionately affects women of African origin. AA women with TNBC have worse clinical outcomes than women of European descent as it presents with a more aggressive and progressive disease. Understanding the underlying biology of the tumor and its microenvironment is critical to address the disparity associated with racial differences in breast cancer. Bulk RNA-sequencing is the most common and useful method to analyze whole transcriptome. However, this method cannot deconvolute the individual components of the tumor and its associated stroma. The NanoString GeoMx digital spatial profiling is a multiplexed platform that spatially resolves and quantifies the abundance of genes present in tumor and stroma. In this study, we have utilized spatial RNA-seq technology to determine the differential gene expression in tumor epithelial cells and its associated stroma in AA and EA TNBC patients. Gene expression from spatially defined regions in tumor and stroma in each patient and in between races identified distinct molecular pathways. The tumor epithelial region of AA has high expression of fatty acid biosynthesis, nucleotide excision repair, RNA helicase like proteins and exhaustion signaling while EA tumor epithelial region had high expression of neuropilin 2, interferon signaling and TGF beta signaling. The pathways overexpressed in the AA stromal region included tryptophan metabolism, chemokine signaling pathway, interferon signaling and exhaustion signature while in the EA stromal region, the pathways pertinent to mTOR signaling, AURORA kinase, cysteine endopeptidases were more prominently expressed. AA tumors were significantly more inflamed than EA and exhibited high infiltration of immune cells. The stromal region of AA tumors has high population of macrophages, CD8 T memory, Treg’s and neutrophils more than EA tumors. The tumor epithelial region of AA has high infiltration of plasmacytoid dendritic cells (pDC), B memory, monocytes, natural killer cells (NK), CD4 memory, CD4 naïve, Treg’s and plasma cells. However, the exhaustion signature proteins, PDCD1 and LAG3 were also higher in tumor-infiltrated and stromal immune cells in AA tumor. The EA tumors were scarce in immune cells in the stroma as well as inside the tumor epithelial, though interferon signaling was substantially higher in EA tumor epithelial cells as compared to AA. Thus, using NanoString GeoMx digital spatial profiling platform, we have spatial profiled AA and EA TNBC and assessed the inter and intra-tumor heterogeneity within and in between races. This is one of the first study that describes not only expression variation of genes between races but also between stroma and tumor epithelial cells within the tissue of same patient and between different patients. These findings address the biological determinant of racial disparity by patient-centered investigation of factors associated with tumor and tumor microenvironment. Citation Format: Deepa Bedi, Balasubramanyam Karanam, Isra Elhussin, Chitra R. Nayak, Clayton Yates. Spatial transcriptome profiling of tumor and tumor microenvironment of triple negative breast cancer in patients of African American and European American descent [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C111.

Infiltrating immune cells in prostate cancer tissue after androgen deprivation and radiotherapy.

Androgen deprivation therapy (ADT) has long been a cornerstone in treatment of advanced prostate cancer (PCa), and is known to improve the results of radiotherapy (RT) for high-risk disease. The purpose of our study was to use a multiplexed immunohistochemical (mIHC) approach to investigate the infiltration of immune cells in PCa tissue after eight weeks of ADT and/or RT with 10Gy. From a cohort of 48 patients divided into two treatment arms, we obtained biopsies before and after treatment and used a mIHC method with multispectral imaging to analyze the infiltration of immune cells in tumor stroma and tumor epithelium, focusing on areas with high infiltration. Tumor stroma showed a significantly higher infiltration of immune cells compared to tumor epithelium. The most prominent immune cells were CD20+ B-lymphocytes, followed by CD68+ macrophages, CD8+ cytotoxic T-cells, FOXP3+ regulatory T-cells (Tregs), and T-bet+ Th1-cells. Neoadjuvant ADT followed by RT significantly increased the infiltration of all five immune cells. Numbers of Th1-cells and Tregs significantly increased after single treatment with ADT or RT. In addition, ADT alone increased the number of cytotoxic T-cells and RT increased the number of B-cells. Neoadjuvant ADT in combination with RT results in a higher inflammatory response compared to RT or ADT alone. The mIHC method may be a useful tool for investigating infiltrating immune cells in PCa biopsies to understand how immunotherapeutic approaches can be combined with current PCa therapies.

M6A RNA methylation modification and tumor immune microenvironment in lung adenocarcinoma.

Lung adenocarcinoma is one of the deadliest tumors. Studies have shown that N6-methyladenosine RNA methylation regulators, as a dynamic chemical modification, affect the occurrence and development of lung adenocarcinoma. To investigate the relationship between mutations and expression levels of m6A regulators in lung adenocarcinoma, we investigated the mutations and expression levels of 38 m6A regulators. We found that mutations in m6A regulatory factors did not affect the changes in expression levels, and 19 differentially expressed genes were identified. All tumor samples were classified into two subtypes based on the expression levels of 19 differentially expressed m6A-regulated genes. Survival analysis showed significant differences in survival between the two subtypes. To explore the relationship between immune cell infiltration and survival in both subtypes, we calculated the infiltration of 23 immune cells in both subtypes, and we found that the subtype with high immune cell infiltration had better survival. We found that subtypes with low tumor purity and high stromal and immune scores had better survival. The m6A-related immune genes were identified by taking the intersection of differentially expressed genes and immune genes in the two isoforms and calculating the Pearson correlation coefficients between the intersecting immune genes and the differentially expressed m6A-regulated genes. Finally, a prognostic model associated with m6A and associated with immunity was developed using prognostic genes screened from m6A-associated immune genes. The predictive power of the model was evaluated and our model was able to achieve good prediction.

Role of Single Nucleotide Polymorphism-Related Genes in Tumour Immune Cell Infiltration and Prognosis of Cutaneous Melanoma.

Advances in cancer research have allowed for early diagnosis and improved treatment of cutaneous melanoma (CM). However, its invasiveness and recurrent metastasis, along with rising resistance to newer therapies, have lent urgency to the search for novel biomarkers and the underlying molecular mechanisms of CM. Single nucleotide polymorphism- (SNP-) related genes were obtained from the sequencing data of 428 CM samples in The Cancer Genome Atlas. Functional enrichment of these genes was analysed in clusterProfiler. Additionally, a protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Gene (STRING) database. Gene Expression Profiling Interactive Analysis (GEPIA) was used to identify the expression and prognostic value of mutated genes. Finally, the Tumour Immune Estimation Resource (TIMER) analysed the relationship between gene expression and immune cell infiltration. We constructed a PPI network from the top 60 SNP-related genes. Mutated genes were mainly involved in calcium and oxytocin signalling pathways, as well as circadian entrainment. In addition, three SNP-related genes, BRAF, FLG, and SORL1, were significantly associated with patient prognosis. BRAF and SORL1 were positively associated with infiltration abundance of B cells, CD8+ T cells, CD4+ T cells, neutrophils, and dendritic cells, whereas FLG expression was negatively associated. Furthermore, higher immune cell infiltration was positively correlated with good prognosis. Our study provides vital bioinformatic data and a relevant theoretical basis to further explore the molecular pathogenesis of CM and improve patient prognosis.

Integrated analysis from multi-center studies identities m7G-derived modification pattern and risk stratification system in skin cutaneous melanoma.

The m7G modification has been proven to play an important role in RNA post-transcriptional modification and protein translation. However, the potential role of m7G modification patterns in assessing the prognosis of Skin cutaneous melanoma (SKCM) and tumor microenvironment (TME) has not been well studied. In this study, we investigated and finally identified 21 available m7G-related genes. We used hierarchical clustering (K-means) to classify 743 SKCM patients into three m7G-modified subtypes named m7G/gene cluster-A, B, C. We found that both m7G cluster B and gene cluster B exhibited higher prognosis and higher immune cell infiltration in TME compared to other subtypes. EIF4E3 and IFIT5, two m7G related genes, were both markedly elevated in Cluster B. Then, we constructed an m7G score system utilizing principal component analysis (PCA) in order to evaluate the patients' prognosis. High m7G score subtype was associated with better survival prognosis and active immune response. Overall, this article revealed that m7G modification patterns were involved in the development of the tumor microenvironment. Evaluating patients' m7G modification patterns will enhance our understanding of TME characteristics and help to guide personal treatment in clinics in the future.

Development of a polyamine gene expression score for predicting prognosis and treatment response in clear cell renal cell carcinoma.

Polyamine metabolism (PM) is closely related to the tumor microenvironment (TME) and is involved in antitumor immunity. Clear cell renal cell carcinoma (ccRCC) not only has high immunogenicity but also has significant metabolic changes. However, the role of PM in the immune microenvironment of ccRCC remains unclear. This study aimed to reveal the prognostic value of PM-related genes (PMRGs) expression in ccRCC and their correlation with the TME. The expression levels PMRGs in different cells were characterized with single-cell sequencing analysis. The PMRG expression pattern of 777 ccRCC patients was evaluated based on PMRGs. Unsupervised clustering analysis was used in identifying PMRG expression subtypes, and Lasso regression analysis was used in developing polyamine gene expression score (PGES), which was validated in external and internal data sets. The predictive value of PGES for immunotherapy was validated in the IMvigor210 cohort. Multiple algorithms were used in analyzing the correlation between PGES and immune cells. The sensitivity of PGES to chemotherapeutic drugs was analyzed with the "pRRophetic" package. We validated the genes that develop PGES in tissue samples. Finally, weighted gene co-expression network analysis was used in identifying the key PMRGs closely related to ccRCC, and cell function experiments were carried out. PMRGs were abundantly expressed on tumor cells, and PMRG expression was active in CD8+ T cells and fibroblasts. We identified three PMRG expression subtypes. Cancer and immune related pathways were active in PMRG expression cluster A, which had better prognosis. PGES exhibited excellent predictive value. The high-PGES group was characterized by high immune cell infiltration, high expression of T cell depletion markers, high tumor mutation burden and tumor immune dysfunction and exclusion, was insensitive to immunotherapy but sensitive to sunitinib, temsirolimus, and rapamycin, and had poor prognosis. Spermidine synthetase (SRM) has been identified as a key gene and is highly expressed in ccRCC at RNA and protein levels. SRM knockdown can inhibit ccRCC cell proliferation, migration, and invasion. We revealed the biological characteristics of PMRG expression subtypes and developed PGES to accurately predict the prognosis of patients and response to immunotherapy.

Establishment of lung adenocarcinoma classification and risk model based on necroptosis-related genes.

Lung adenocarcinoma (LUAD) is the most widely known histological subtype of lung cancer. Its classification is significant for the characteristic evaluation of patients. The aim of this research is to assess the categorization of LUAD and its risk model based on necroptosis and to investigate its potential regulatory mechanisms for diagnosing and treating LUAD. According to the expression profile data along with the clinical information related to LUAD from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we constructed a consistency matrix through consistency clustering, and used the ConsensusClusterPlus as the measurement distance to cluster and subtype the samples, and performed gene set enrichment analysis and immune infiltration analysis. Least absolute shrinkage and selection operator (Lasso) regression was utilized for obtaining prognostic significant necroptosis phenotype-related genes. Finally, we measured each patient's riskscore (RS) and build a risk model, and predicted the effect of immunotherapy for different groups of risk factors in the model. Three molecular subtypes of LUAD were obtained by cluster analysis of necroptosis-related genes in LUAD samples. Compared with C1, C3 had a better prognosis and higher immune cell infiltration. The prognosis of the C1 subtype was poor and had a high clinical grade. The proportion of Stage II, Stage III, and Stage IV was much more in comparison with that of the other two subtypes. TP53 gene had a high mutation frequency in the C1 subtype. Gene Set Enrichment Analysis (GSEA) indicated that the aberrant pathways in the C1 and C3 subtypes mainly included some cell cycle-related pathways. In addition, seven genes were identified as related genes of necroptosis phenotype affecting prognosis. High RS had a poor prognosis, while low RS had a good prognosis. The RS was verified to have a strong ability to predict survival. LUAD can be classified by the genes linked with cell necrosis and apoptosis. The difference among various types is helpful to deepen the understanding of LUAD. In addition, a risk model was constructed based. In conclusion, this study provides potential detection targets and treatment methods for LUAD from a new perspective.

STEM-13. GLIOMA STEM CELLS MIMIC REGULATORY T CELL FUNCTION TO SUPPRESS THE IMMUNE RESPONSE AND PROMOTE TUMOR PROPAGATION

Abstract The advent of immunotherapy has transformed our approach to cancer treatment; however, it is virtually ineffective in glioblastoma (GBM), the most common &amp; malignant brain cancer in adults, due in part to the inherently immunosuppressive tumor microenvironment (TME). Recent research has identified GBM cancer stem cells (GSCs) as key modulators of the TME, mainly through activation and recruitment of pro-tumor immune cells (e.g., TAMs, regulatory T-cells (Tregs), MDSCs). We identified a novel immunosuppressive phenotype whereby GSCs mimic Treg function via TGF-b type II receptor (TGFBR2) signaling to suppress the immune response with the potential to promote tumor propagation and resist immunotherapy. Preliminary experiments revealed that TGFBR2 drives GBM cells to a CD44-high, mesenchymal-like GSC state. Transgenic TGFBR2 was found to induce a Treg-like transcriptional profile defined by expression of canonical Treg effector genes (e.g., CD274, ENTPD1, NT5E, LGALS1, TGFB1, PDCD1LG2) in human GBM neurospheres. Single-cell RNA-Seq and bioinformatics analyses confirmed the presence of this TGFBR2-mediated transcriptional signature in both patient-derived GSCs and patient tumors. Notably, in silico deconvolution of bulk GBM sequencing predicts that high expression of a TGFBR2-induced transcriptome associates with an immunosuppressive TME characterized by high infiltration of pro-tumor immune cells (e.g., TAMs, neutrophils). To target this Treg-like, immunosuppressive GSC phenotype, we utilized ITD-1, a small molecule inhibitor of TGFBR2. In vitro ITD-1 treatment reduced GSC proliferation capacity and downregulated expression of CD44 and Treg effectors. In T-cell co-cultures, pre-treating GSCs with ITD-1 increased T-cell viability and T-cell-mediated tumor cell killing. Administration of ITD-1 in tumor-bearing, immunocompetent mice reduced tumor volume and increased anti-tumor immune cell infiltration relative to vehicle controls. In all, ITD-1-mediated TGFBR2 inhibition attenuates a unique, Treg-like immunosuppressive and potentially tumor-protective phenotype in GSCs. Future studies will combine ITD-1 and checkpoint inhibitor therapy in an effort to increase the efficacy of immunotherapy in GBM.

Identification of a New Prediction Model for Bladder Cancer Related to Immune Functions and Chemotherapy Using Gene Sets of Biological Processes.

Biological processes serve crucial functions in the initiation and development of cancer. Therefore, we constructed and validated a model for bladder cancer (BLCA) with good predictive power for immunity, prognosis, and therapy. Using the expression of the gene sets based on biological processes, BLCA patients were divided into three clusters by consensus cluster analysis. By performing LASSO regression analysis twice, key genes were selected, and the biological processes-related genes' (BPRG) score was calculated. Differences in immune infiltration, tumor microenvironment, tumor mutation burden, immunotherapy, and sensitivity towards chemotherapy were analyzed between two groups divided by BPRG score. Good accuracy was observed for the three clusters. They showed different prognoses and levels of immune cell infiltration. The selected key genes were mainly enriched in immune-related pathways. The high-BPRG score group was related to poor prognosis, higher immune cell infiltration, interstitial scores, and increased tumor mutation. Moreover, the effects of immunotherapy were good, and those of chemotherapy were poor. Overall, key genes may be involved in various complex immune regulation processes. Therefore, the quantification and verification of the BPRG score are expected to facilitate the understanding of the immunosuppressive microenvironment in BLCA and guide the choice of chemotherapeutic drugs and immunotherapeutic regimens and help predict the prognoses of patients with BLCA.

WIN Symposium 2022 - Abstract Poster Presentations.

WIN Symposium 2022 - Abstract Poster Presentations.

A novel signature based on pyroptosis-related genes for predicting prognosis and treatment response in prostate cancer patients.

Background: Pyroptosis is a form of programmed cell death accompanied by specific inflammatory and immune responses, and it is closely related to the occurrence and progression of various cancers. However, the roles of pyroptosis-related genes (PRGs) in the prognosis, treatment response, and tumor microenvironment (TME) of prostate cancer (PCa) remain to be investigated. Methods: The mRNA expression data and clinical information of PCa patients were obtained from the Cancer Genome Atlas database (TCGA) and the cBioPortal for Cancer Genomics website, and the 52 PRGs were obtained from the published papers. The univariate, multivariate, and LASSO Cox regression algorithms were used to obtain prognostic hub PRGs. Meanwhile, qRT-PCR was used to validate the expression of hub genes between PCa lines and normal prostate epithelial cell lines. We then constructed and validated a risk model associated with the patient's disease-free survival (DFS). Finally, the relationships between risk score and clinicopathological characteristics, tumor immune microenvironment, and drug treatment response of PCa were systematically analyzed. Results: A prognostic risk model was constructed with 6 hub PRGs (CHMP4C, GSDMB, NOD2, PLCG1, CYCS, GPX4), and patients were divided into high and low-risk groups by median risk score. The risk score was confirmed to be an independent prognostic factor for PCa in both the training and external validation sets. Patients in the high-risk group had a worse prognosis than those in the low-risk group, and they had more increased somatic mutations, higher immune cell infiltration and higher expression of immune checkpoint-related genes. Moreover, they were more sensitive to cell cycle-related chemotherapeutic drugs and might be more responsive to immunotherapy. Conclusion: In our study, pyroptosis played a significant role in the management of the prognosis and tumor microenvironment of PCa. Meanwhile, the established model might help to develop more effective individual treatment strategies.

Combination of Immune-Related Network and Molecular Typing Analysis Defines a Three-Gene Signature for Predicting Prognosis of Triple-Negative Breast Cancer

Recent breakthroughs in immune checkpoint inhibitors (ICIs) have shown promise in triple-negative breast cancer (TNBC). Due to the intrinsic heterogeneity among TNBC, clinical response to ICIs varies greatly among individuals. Thus, discovering rational biomarkers to select susceptible patients for ICIs treatment is warranted. A total of 422 TNBC patients derived from The Cancer Genome Atlas (TCGA) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were included in this study. High immunogenic gene modules were identified using weighted gene co-expression network analysis (WGCNA). Immune-related genes (IRGs) expression patterns were generated by consensus clustering. We developed a three-gene signature named immune-related gene panel (IRGP) by Cox regression method. Afterward, the associations of IRGP with survival outcomes, infiltration of immune cells, drug sensitivity, and the response to ICIs therapy were further explored. We found five high immunogenic gene modules. Two distinct IRGclusters and IRG-related genomic clusters were identified. The IRGP was constructed based on TAPBPL, FBP1, and GPRC5C genes. TNBC patients were then subdivided into high- and low-IRGriskscore subgroups. TNBC patients with low IRGriskscore had a better survival outcome, higher infiltration of immune cells, lower TP53 mutation rate, and more benefit from ICIs treatment than high IRGriskscore patients. These findings offer novel insights into molecular subtype of TNBC and provided potential indicators for guiding ICIs treatment.

Dickkopf-1 is a Prognostic Biomarker of Head and Neck Squamous Cell Carcinoma that Correlates with Immune Infiltrates

<h3>Purpose/Objective(s)</h3> Head and neck squamous cell carcinoma (HNSCC) is associated with high immune cell infiltration, and immunotherapy is currently one of the most promising treatments for HNSCC. Dickkopf-1 (DKK1), a Wnt-signaling inhibitor and mediator of immune activation, is highly associated with tumor progression and the tumor microenvironment and, thus, disease prognosis. However, the associations between DKK1 expression, HNSCC prognosis, and tumor-infiltrating lymphocytes remain unknown. <h3>Materials/Methods</h3> We investigated these relationships in this study by exploring DKK1-expression differences in multiple tumor tissues and normal tissues, using the TIMER, Oncomine, and UALCAN databases. We searched the Kaplan–Meier database to assess correlations between DKK1 mRNA-expression levels and clinical outcomes. Subsequently, we evaluate the correlation between DKK1 expression and tumor immune-cell infiltration using the TIMER platform and TISIDB website. In addition, we assessed DKK1, CD3, and CD4 expression in 27 head and neck tumor samples and DKK1 expression in 15 normal head and neck tissue samples by immunohistochemistry. Moreover, we used the cBioPortal and UCSC Xena databases to explore aberrant DKK1 expression during tumorigenesis. <h3>Results</h3> DKK1 significantly upregulated in HNSCC tissues versus normal tissues. DKK1 expression was related to the HNSCC tumor stage, tumor grade, histology, lymph node metastasis, and a poor clinical prognosis. DKK1 expression correlated negatively with CD3+ (P < 0.0001) and CD4+ (P < 0.0001) immune cell infiltration in HNSCC tumor tissues. Moreover, DKK1 closely correlated with genetic markers of various immune cells. The prognosis of HNSCC correlated negatively with DKK1 expression in immune cells. Copy-number variation and DNA methylation may promote abnormal DKK1 upregulation in HNSCC. These results help understand the role of DKK1 in HNSCC immunotherapy and provide a rationale for clinical research on treating HNSCC by targeting DKK1. <h3>Conclusion</h3> DKK1 may therefore serve as a prognostic indicator and a biomarker for immune infiltration in HNSCC, and potentially as a central target for improving the diagnosis and immunotherapy efficacy of HNSCC.

Amino acid metabolism-based molecular classification of colon adenocarcinomavia in silico analysis.

Amino acid metabolism is closely related to the occurrence and development of colon adenocarcinoma (COAD). Studies on the relationship between COAD and the expression of amino acid metabolism are still rare. Based on in silico analysis, we used 358 amino acid metabolism-related genes (AAMRGs) to determine the amino acid metabolism characteristics and then classified COAD into two distinct subtypes, namely AA1 and AA2. Then we analyzed the clinical characteristics, somatic mutation landscape, transcriptome profile, metabolism signatures, immune infiltration, and therapy sensitivity of these two subtypes. The AA1 subtype had inferior overall survival and was characterized by lower amino acid metabolic activity, higher tumor mutation burden, and higher immune cell infiltration, while AA2 displayed higher metabolic activity and relatively better survival. Furthermore, the AA1 subtype was likely to benefit from irinotecan in chemotherapy and immune checkpoint blockade therapy including programmed cell death protein-1 (PD-1) and cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) immune checkpoint inhibitor but was resistant to targeted therapy cetuximab. The AA2 subtype showed higher sensitivity to 5-fluorouracil and oxaliplatin. To provide perspectives on cell-specific metabolism for further investigation, we explored metabolic activity in different cell types including lymphocytes, mast cells, myeloid cells stromal cells, and epithelial cells via colorectal cancer single-cell data. Additionally, to assist in clinical decision-making and prognosis prediction, a 60-AAMRG-based classifier was generated and validated in an independent cohort.

Comprehensive analysis of cuproptosis-related genes and tumor microenvironment infiltration characterization in breast cancer.

Cuproptosis is a newly discovered programmed cell death dependent on overload copper-induced mitochondrial respiration dysregulation. The positive response to immunotherapy, one of the most important treatments for invasive breast cancer, depends on the dynamic balance between tumor cells and infiltrating lymphocytes in the tumor microenvironment (TME). However, cuproptosis-related genes (CRGs) in clinical prognosis, immune cell infiltration, and immunotherapy response remain unclear in breast cancer progression. The expression and mutation patterns of 12 cuproptosis-related genes were systematically evaluated in the BRCA training group. Through unsupervised clustering analysis and developing a cuproptosis-related scoring system, we further explored the relationship between cuproptosis and breast cancer progression, prognosis, immune cell infiltration, and immunotherapy. We identified two distinct CuproptosisClusters, which were correlated with the different patterns between clinicopathological features, prognosis, and immune cell infiltration. Moreover, the differences of the three cuproptosis-related gene subtypes were evaluated based on the CuproptosisCluster-related DEGs. Then, a cuproptosis-related gene signature (PGK1, SLC52A2, SEC14L2, RAD23B, SLC16A6, CCL5, and MAL2) and the scoring system were constructed to quantify the cuproptosis pattern of BRCA patients in the training cohort, and the testing cohorts validated them. Specifically, patients from the low-CRG_score group were characterized by higher immune cell infiltration, immune checkpoint expression, immune checkpoint inhibitor (ICI) scores, and greater sensitivity to immunotherapy. Finally, we screened out RAD23B as a favorable target and indicated its expression was associated with breast cancer progression, drug resistance, and poor prognosis in BRCA patients by performing real-time RT-PCR, cell viability, and IC50 assay. Our results confirmed the essential function of cuproptosis in regulating the progression, prognosis, immune cell infiltration, and response to breast cancer immunotherapy. Quantifying cuproptosis patterns and constructing a CRG_score could help explore the potential molecular mechanisms of cuproptosis regulating BRCA advancement and provide more effective immunotherapy and chemotherapy targets.