TCGA Cohort Research Articles

An Innovative Telomere-associated Prognosis Model in AML: Predicting Immune Infiltration and Treatment Responsiveness.

To build an innovative telomere-associated scoring model to predict prognosis and treatment responsiveness in acute myeloid leukemia (AML). AML is a highly heterogeneous malignant hematologic disorder with a poor prognosis. While telomere maintenance is frequently observed in tumors, investigations into telomere-related genes (TRGs) in AML remain limited. This study aimed to identify prognostic TRGs using the least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression, evaluate their predictive value, explore the association between TRG scores and immune cell infiltration, and assess the sensitivity of high-scoring AML patients to chemotherapeutic agents. Univariate Cox regression analysis was conducted on the TCGA cohort to identify prognostic TRGs and to develop the TRG scoring model using LASSO-Cox and multivariate Cox regression. Validation was performed on the GSE37642 cohort. Immune cell infiltration patterns were assessed through computational analysis, and the sensitivity to chemotherapeutic agents was evaluated. Thirteen prognostic TRGs were identified, and a seven-TRG scoring model (including NOP10, OBFC1, PINX1, RPA2, SMG5, MAPKAPK5, and SMN1) was developed. Higher TRG scores were associated with a poorer prognosis, as confirmed in the GSE37642 cohort, and remained an independent prognostic factor even after adjusting for other clinical characteristics. The high-score group was characterized by elevated infiltration of B cells, T helper cells, natural killer cells, tumor-infiltrating lymphocytes, regulatory T (Treg) cells, M2 macrophages, neutrophils, and monocytes, along with reduced infiltration of gamma delta T cells, CD4- T cells, and resting mast cells. Moreover, high infiltration of M2 macrophages and Tregs was associated with poor overall survival compared to low infiltration. Notably, high-risk AML patients were resistant to Erlotinib, Parthenolide, and Nutlin-3a, but sensitive to AC220, Midostaurin, and Tipifarnib. Additionally, using RT-qPCR, we observed significantly higher expression of two model genes, OBFC1 and SMN1, in AML tissues compared to control tissues. This innovative TRG scoring model demonstrates considerable predictive value for AML patient prognosis, offering valuable insights for optimizing treatment strategies and personalized medicine approaches. The identified TRGs and associated scoring models could aid in risk stratification and guide tailored therapeutic interventions in AML patients.

Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.

Glioblastoma (GBM), one of the most common brain tumors, is known for its low survival rates and poor treatment responses. This study aims to create a robust predictive model that integrates multiple feature types, including clinical data, RNA expression, and tumor microenvironment data, using fusion techniques to enhance model performance. We obtained data from the SEER database to assess the impact of nine demographic and clinical features on the survival of 58,495 GBM patients and built predictive machine learning models. Additionally, mRNA expression data from 600 GBM patients from TCGA, CGGA, and GEO were analyzed. We used Cox regression and LASSO to create a gene signature, which was compared against 13 published signatures for accuracy. Twenty-one machine learning models were applied to predict survival at multiple time points. Finally, we integrated multiple feature types using fusion techniques and developed a Shiny app to provide survival predictions for GBM patients. Using the SEER database, we constructed machine learning models based on nine clinical variables: age, gender, marital status, race, tumor site, laterality, surgery, chemotherapy, and radiation therapy. The best-performing model achieved AUC values of 0.775, 0.728, 0.692, and 0.683 for predicting survival at 6, 12, 18, and 24months in the testing cohort. In the merged TCGA, CGGA, and GEO cohorts, we identified 11 genes to develop predictive models. These 11 genes outperformed 13 other published gene signatures in predicting the prognosis of GBM. When incorporating mRNA features, tumor microenvironment features, and clinical variables into the fusion models, the AUC values for predicting survival at 6, 12, 18, and 24months were 0.641, 0.624, 0.655, and 0.637, respectively. A user-friendly tool for predicting the survival curve of individual GBM patients is available at https://zzubioinfo.shinyapps.io/mlGBM/ . Our study provides a web-based tool that includes two modules: one for predicting survival curves using only clinical variables, and another that integrates multiple feature types for more comprehensive predictions.

Identification and validation of a prognostic model based on three TLS-Related genes in oral squamous cell carcinoma

BackgroundThe tertiary lymphoid structures (TLSs) have an immunomodulatory function and have a positive impact on the survival outcomes of patients with oral squamous cell carcinoma (OSCC). However, there is a lack of standard approaches for quantifying TLSs and prognostic models using TLS-related genes (TLSRGs). These limitations limit the widespread use of TLSs in clinical practice.MethodsA convolutional neural network was used to automatically detect and quantify TLSs in HE-stained whole slide images. By employing bioinformatics and diverse statistical methods, this research created a prognostic model using TCGA cohorts and explored the connection between this model and immune infiltration. The expression levels of three TLSRGs in clinical specimens were detected by immunohistochemistry. To facilitate the assessment of individual prognostic outcomes, we further constructed a nomogram based on the risk score and other clinical factors.ResultsTLSs were found to be an independent predictor of both overall survival (OS) and disease-free survival in OSCC patients. A larger proportion of the TLS area represented a better prognosis. After analysis, we identified 69 differentially expressed TLSRGs and selected three pivotal TLSRGs to construct the risk score model. This model emerged as a standalone predictor for OS and exhibited close associations with CD4 + T cells, CD8 + T cells, and macrophages. Immunohistochemistry revealed high expression levels of CCR7 and CXCR5 in TLS + OSCC samples, while CD86 was highly expressed in TLS- OSCC samples. The nomogram demonstrates excellent predictive ability for overall survival in OSCC patients.ConclusionsThis is the first prognostic nomogram based on TLSRGs, that can effectively predict survival outcomes and contribute to individual treatment strategies for OSCC patients.

Multi-omics analysis identifies PTTG1 as a prognostic biomarker associated with immunotherapy and chemotherapy resistance

BackgroundPituitary tumor-transforming gene 1 (PTTG1) is an important gene in tumour development. However, the relevance of PTTG1 in tumour prognosis, immunotherapy response, and medication sensitivity in human pan-cancer has to be determined.MethodsTIMER, GEPIA, the human protein atlas, GEPIA, TISCH2, and cBioportal examined the gene expression, protein expression, prognostic value, and genetic modification landscape of PTTG1 in 33 malignancies based on the TCGA cohort. The association between PTTG1 and tumour immunity, tumour microenvironment, immunotherapy response, and anticancer drug sensitivity was investigated using GSCA, TIDE, and CellMiner CDB. Molecular docking was used to validate the possible chemotherapeutic medicines for PTTG1. Additionally, siRNA-mediated knockdown was employed to confirm the probable role of PTTG1 in paclitaxel-resistant cells.ResultsPTTG1 is overexpressed and associated with poor survival in most tumors. Functional enrichment study revealed that PTTG1 is involved in the cell cycle and DNA replication. A substantial connection between PTTG1 expression and immune cell infiltration points to PTTG1’s possible role in the tumour microenvironment. High PTTG1 expression is associated with tumour immunotherapy resistance. The process could be connected to PTTG1, which mediates T cell exhaustion and promotes cytotoxic T lymphocyte malfunction. Furthermore, PTTG1 was found to be substantially linked with sensitivity to several anticancer medications. Suppressing PTTG1 with siRNA reduced clone formation and migration, implying that PTTG1 may play a role in paclitaxel resistance.ConclusionPTTG1 shows potential as a cancer diagnostic, prognostic, and chemosensitivity marker. Increased PTTG1 expression is linked to resistance to cancer treatment. The mechanism could be linked to PTTG1’s role in promoting cytotoxic T lymphocyte dysfunction and mediating T cell exhaustion. It is feasible to consider PTTG1, which is expressed on Treg and Tprolif cells, as a new therapeutic target for overcoming immunotherapy resistance.

Bioinformatics investigation of the prognostic value and mechanistic role of CD9 in glioma

In recent years, CD9 has been extensively studied as a potential biomarker for cancer. However, the biological role of CD9 in gliomas remains unclear. This study investigates the function of CD9 in gliomas and its molecular mechanisms. Utilizing pan-cancer analysis with TCGA, CGGA, and GEO databases, differential expression of CD9 was observed in 11 tumor types within the TCGA cohort, and it was associated with patient survival rates. Analysis of the CGGA glioma database revealed that patients with high CD9 expression had lower survival rates. The area under the ROC curve (AUC) for GSE16011 was greater than 0.7, indicating a high discriminative ability. Through gene set enrichment analysis (GSEA), immune-related analysis, and CD9 mutation detection, CD9 was found to have the strongest correlation with neutrophil involvement (cor = 0.30, P < 0.05), and the high CD9 expression group exhibited higher rejection responses and TIDE scores, suggesting a lower likelihood of successful immunotherapy. The high CD9 expression group was more sensitive to 81 drugs, indicating potential therapeutic effects for gliomas. Furthermore, overexpression of CD9 in gliomas may be associated with gene mutations. Down-regulation or up-regulation of CD9 expression in the glioblastoma cell line LN229 showed that CD9 could positively regulate the migratory ability of LN229 cells. Further, several marker genes, such as VEGFR-2, TGF-β1, CASP1 and PI3K, were down regulated in CD9 knockdown cell lines and up regulated in CD9 overexpression cell lines, compared with control cell line. This study preliminarily explores the role of CD9 in gliomas and its prognostic value, providing new insights for personalized treatment strategies in glioma therapy.

Comparative analysis of tertiary lymphoid structures for predicting survival of colorectal cancer: A whole-slide images-based study

Abstract Background Tertiary lymphoid structures (TLS) are major component in immune microenvironment, correlating with a favorable prognosis in colorectal cancer. However, the methods used to define and characterize TLS were not united, hindering its's clinical application. This study aims to seek a more stable method to characterize TLS and clarify its prognostic value in larger multicenter cohorts. Methods A total of 1609 patients from four hospitals and the TCGA database were analyzed. We quantified the number and maximum length of TLS along the invasive margin of tumor using hematoxylin and eosin (HE)-stained whole-slide images (WSIs). Additionally, the length of the invasive margin was determined to calculate the TLS density. The prognostic value of the TLS for overall survival was evaluated. In addition, we examined the association between TLS density and immune cell infiltration using immunohistochemistry-stained WSIs. The performance for predicting overall survival was measured using hazard ratios (HR) with 95% confidence intervals (CI). Results Among the three TLS quantification methods, TLS density has the strongest discriminative performance. Survival analysis indicated that higher TLS density correlated with better overall survival (HR for high vs. low 0.57 [95% CI 0.42–0.78] in primary cohort; 0.49 [0.35–0.69] in validation cohort; 0.35 [0.18–0.67] in TCGA cohort). A high TLS density was associated with a high level of CD3+ T cells infiltration. Conclusions Based on this comparative multicenter analysis, TLS density was identified as a simple, robust, and effective immune prognostic index for colorectal cancer.

Application of a risk score model based on glycosylation-related genes in the prognosis and treatment of patients with low-grade glioma.

Low-grade gliomas (LGG) represent a heterogeneous and complex group of brain tumors. Despite significant progress in understanding and managing these tumors, there are still many challenges that need to be addressed. Glycosylation, a common post-translational modification of proteins, plays a significant role in tumor transformation. Numerous studies have demonstrated a close relationship between glycosylation modifications and tumor progression. However, the biological function of glycosylation-related genes in LGG remains largely unexplored. Their potential roles within the LGG microenvironment are also not well understood. We collected RNA-seq data and scRNA-seq data from patients with LGG from TCGA and GEO databases. The glycosylation pathway activity scores of each cluster and each patient were calculated by irGSEA and GSVA algorithms, and the differential genes between the high and low glycosylation pathway activity score groups were identified. Prognostic risk profiles of glycosylation-related genes were constructed using univariate Cox and LASSO regression analyses and validated in the CGGA database. An 8 genes risk score signature including ASPM, CHI3L1, LILRA4, MSN, OCIAD2, PTGER4, SERPING1 and TNFRSF12A was constructed based on the analysis of glycosylation-related genes. Patients with LGG were divided into high risk and low risk groups according to the median risk score. Significant differences in immunological characteristics, TIDE scores, drug sensitivity, and immunotherapy response were observed between these groups. Additionally, survival analysis of clinical medication information in the TCGA cohort indicated that high risk and low risk groups have different sensitivities to drug therapy. The risk score characteristics can thus guide clinical medication decisions for LGG patients. Our study established glycosylation-related gene risk score signatures, providing new perspectives and approaches for prognostic prediction and treatment of LGG.

Machine learning based intratumor heterogeneity signature for predicting prognosis and immunotherapy benefit in stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a prevalent malignancy that is highly aggressive and heterogeneous. Intratumor heterogeneity (ITH) showed strong link to tumor progression and metastasis. High ITH may promote tumor evolution. An ITH-related signature (IRS) was created using as integrative technique including 10 machine learning methods based on TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets. The relevance of IRS in predicting the advantages of immunotherapy was assessed using a number of prediction scores and three immunotherapy datasets (GSE78220, IMvigor210 and GSE91061). Vitro experiments were performed to verify the biological functions of AKR1B1. The RSF + Enet (alpha = 0.1) projected model was proposed as the ideal IRS because it had the highest average C-index. The IRS demonstrated a strong performance in serving as an independent risk factor for the clinical outcome of STAD patients. It performed exceptionally well in predicting the overall survival rate of STAD patients, as seen by the TCGA cohort’s AUC of 1-, 3-, and 5-year ROC curves, which were 0.689, 0.683, and 0.669, respectively. A low IRS score demonstrated a superior response to immunotherapy, as seen by a lower TIDE score, lower immune escape score, greater TMB score, higher PD1&CTLA4 immunophenoscore, higher response rate, and improved prognosis. Common chemotherapeutic and targeted treatment regimens had lower IC50 values in the group with higher IRS scores. Vitro experiment showed that AKR1B1 was upregulated in STAD and knockdown of AKR1B1 obviously suppressed tumor cell proliferation and migration. The present investigation produced the best IRS for STAD, which may be applied to prognostication, risk stratification, and therapy planning for STAD patients.

Prognostic value of a lactate metabolism gene signature in lung adenocarcinoma and its associations with immune checkpoint blockade therapy response.

Lung adenocarcinoma (LUAD) is a study that examines the prognostic value of lactate metabolism genes in tumor cells, which are associated with clinical prognosis. We analyzed the expression and clinical data for LUAD from The Cancer Genome Atlas database, using the GSE68465 dataset from the Gene Expression Omnibus and the MSigDB database. LASSO Cox regression and stepwise Cox regression were used to identify the optimal lactate metabolism gene signature. Differences in immune infiltration, tumor mutation burden (TMB), and response to immune checkpoint blockade (ICB) therapy were evaluated between groups. LASSO and Cox regression analyses showed an eight-lactate metabolism gene signature for model construction in both TCGA cohort and GSE68465 data, with higher survival outcomes in high-risk groups. The lactate metabolism risk score had an independent prognostic value (hazard ratio: 2.279 [1.652-3.146], P < .001). Immune cell infiltration differed between the risk groups, such as CD8+ T cells, macrophages, dendritic cells, mast cells, and neutrophils. The high-risk group had higher tumor purity, lower immune and stromal scores, and higher TMB. High-risk samples had high tumor immune dysfunction and exclusion (TIDE) scores and low cytolytic activity (CYT) scores, indicating a poor response to ICB therapy. Similarly, most immune checkpoint molecules, immune inhibitors/stimulators, and major histocompatibility complex (MHC) molecules were highly expressed in the high-risk group. The 8-lactate metabolism gene-based prognostic model predicts patient survival, immune infiltration, and ICB response in patients with LUAD, driving the development of therapeutic strategies to target lactate metabolism.

Leveraging programmed cell death signature to predict clinical outcome and immunotherapy benefits in postoperative bladder cancer

Bladder cancer is the fourth most common malignancy in men with poor prognosis. Programmed cell death (PCD) exerts crucial functions in many biological processes and immunotherapy responses of cancers. Cell death signature (CDS) is novel gene signature comprehensively considering the characteristics of 15 patterns of programmed cell death, which could affect the prognosis and immunotherapy benefits of cancer patients. Integrative machine learning procedure including 10 algorithms was conducted to construct a prognostic CDS using TCGA, GSE13507, GSE31684, GSE32984 and GSE48276 datasets. Immunophenoscore, intratumor heterogeneity (ITH), tumor immune dysfunction and exclusion (TIDE) score and five immunotherapy cohorts were used to evaluate the predictive value of CDS in immunotherapy response. The prognostic CDS constructed by StepCox[backward] + Ridge algorithms was regarded as the optimal prognostic model. The CDS had a stable and powerful performance in predicting overall survival of bladder cancer patients with the AUCs at 3-year, 5-year, and 7-year ROC of 0.740, 0.763 and 0.820 in TCGA cohort. Moreover, CDS score acted as an independent risk factor for overall survival rate of bladder cancer patients. Low CDS score had a higher abundance of immuno-activated cells, higher PD1&CTLA4 immunophenoscore, higher TMB score, lower TIDE score, lower immune escape score, lower ITH score, lower cancer-related hallmarks score in bladder cancer. The CDS score was higher in non-responders in pan-cancer patients receiving immunotherapy. Our study constructed a novel prognostic CDS, which could serve as an indicator for predicting the prognosis in postoperative bladder cancer cases and immunotherapy benefits in pan-cancer. Low CDS score indicated a better prognosis and immunotherapy benefits.

MHC Hammer reveals genetic and non-genetic HLA disruption in cancer evolution.

Disruption of the class I human leukocyte antigen (HLA) molecules has important implications for immune evasion and tumor evolution. We developed major histocompatibility complex loss of heterozygosity (LOH), allele-specific mutation and measurement of expression and repression (MHC Hammer). We identified extensive variability in HLA allelic expression and pervasive HLA alternative splicing in normal lung and breast tissue. In lung TRACERx and lung and breast TCGA cohorts, 61% of lung adenocarcinoma (LUAD), 76% of lung squamous cell carcinoma (LUSC) and 35% of estrogen receptor-positive (ER+) cancers harbored class I HLA transcriptional repression, while HLA tumor-enriched alternative splicing occurred in 31%, 11% and 15% of LUAD, LUSC and ER+ cancers. Consistent with the importance of HLA dysfunction in tumor evolution, in LUADs, HLA LOH was associated with metastasis and LUAD primary tumor regions seeding a metastasis had a lower effective neoantigen burden than non-seeding regions. These data highlight the extent and importance of HLA transcriptomic disruption, including repression and alternative splicing in cancer evolution.

Development and validation of a novel liquid-liquid phase separation gene signature for bladder cancer

Bladder carcinoma (BLCA) represents a common urinary tract malignancy, characterized by aggressive behavior and high recurrence rates. The biological response regulation during tumor proliferation and metastasis is intimately associated with liquid-liquid phase separation (LLPS). For the purpose of enhancing early detection and treatment, this study employed transcriptomic data to examine the prognostic implications of LLPS-associated genes and formulate a predictive model. Clinical and transcriptomic data of bladder cancer patients were sourced from the GEO and TCGA databases. This study applied a clustering algorithm using non-negative matrix factorization (NMF) to classify samples, which were systematically compared based on their liquid-liquid phase separation characteristics. Prognostic models were developed using multivariate Cox regression and the Least Absolute Shrinkage Selection Operator (LASSO) algorithm to establish risk formulas for nine genes. The gene signature’s validity was tested across the entire TCGA cohort (406 cases), the TCGA testing cohort (120 cases), and the external validation dataset GSE13507. The predictive accuracy of the signature system was assessed using receiver operating characteristic (ROC) and Kaplan-Meier curves. Additionally, decision curve analysis incorporating clinicopathological parameters and the genetic signature was employed to predict individual survival. This study identified two distinct molecular subtypes, C1 and C2. Patients with the C1 subtype exhibited significantly better prognoses than those with the C2 subtype. Low-risk group patients consistently showed superior prognoses compared to high-risk groups across the entire TCGA, GEO, and TCGA training cohorts. Furthermore, the LLPS-related gene model demonstrated prognostic value independent of other clinical traits. This study identifies LLPS-associated gene clusters and establishes an independent, accurate prognostic model for BLCA. The model holds potential for clinical application in BLCA prognosis assessment.

Precision molecular insights for prostate cancer prognosis: tumor immune microenvironment and cell death analysis of senescence-related genes by machine learning and single-cell analysis

BackgroundProstate cancer (PCa) is a prevalent malignancy among men, primarily originating from the prostate epithelium. It ranks first in global cancer incidence and second in mortality rates, with a rising trend in China. PCa's subtle initial symptoms, such as urinary issues, necessitate diagnostic measures like digital rectal examination, prostate-specific antigen (PSA) testing, and tissue biopsy. Advanced PCa management typically involves a multifaceted approach encompassing surgery, radiation, chemotherapy, and hormonal therapy. The involvement of aging genes in PCa development and progression, particularly through the mTOR pathway, has garnered increasing attention.MethodsThis study aimed to explore the association between aging genes and biochemical PCa recurrence and construct predictive models. Utilizing public gene expression datasets (GSE70768, GSE116918, and TCGA), we conducted extensive analyses, including Cox regression, functional enrichment, immune cell infiltration estimation, and drug sensitivity assessments. The constructed risk score model, based on aging-related genes (ARGs), demonstrated superior predictive capability for PCa prognosis compared to conventional clinical features. High-risk genes positively correlated with risk, while low-risk genes displayed a negative correlation.ResultsAn ARGs-based risk score model was developed and validated for predicting prognosis in prostate adenocarcinoma (PRAD) patients. LASSO regression analysis and cross-validation plots were employed to select ARGs with prognostic significance. The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787). The model demonstrated good sensitivity and specificity, with AUC values of 0.67, 0.675, 0.696, and 0.696 at 1, 3, 5, and 8 years, respectively, in the GEO cohort. Similar AUC values were observed in the TCGA cohort at 1, 3, and 5 years (0.67, 0.659, 0.667, and 0.743). The model included 12 genes, with high-risk genes positively correlated with risk and low-risk genes negatively correlated.ConclusionsThis study presents a robust ARGs-based risk score model for predicting biochemical recurrence in PCa patients, highlighting the potential significance of aging genes in PCa prognosis and offering enhanced predictive accuracy compared to traditional clinical parameters. These findings open new avenues for research on PCa recurrence prediction and therapeutic strategies.

Age- and ethnic-driven molecular and clinical disparity of East Asian breast cancers

BackgroundBreast cancer (BC) is a complex disease with profound genomic aberrations. However, the underlying molecular disparity influenced by age and ethnicity remains elusive.MethodsIn this study, we aimed to investigate the molecular properties of 843 primary and metastatic BC patients enrolled in the K-MASTER program. By categorizing patients into two distinct age subgroups, we explored their unique molecular properties. Additionally, we leveraged large-scale genomic data from the TCGA and MSK-IMPACT studies to examine the ethnic-driven molecular and clinical disparities.ResultsWe observed a high prevalence of PI3KCA mutations in K-MASTER HER2 + tumors, particularly in older patients. Moreover, we identified increased mutation rates in DNA damage response molecules, including ARID1A, MSH6, and MLH1. The K-MASTER patients were mainly comprised of triple-negative breast cancer (TNBC) and HER2-positive tumors, while the TCGA and MSK-IMPACT cohorts exhibited a predominance of hormone receptor-positive (HR +) subtype tumors. Importantly, GATA3 mutations were less frequently observed in East Asian patients, which correlated with poor clinical outcomes. In addition to characterizing the molecular disparities, we developed a gradient-boosting multivariable model to identify a new molecular signature that could predict the therapeutic response to platinum-based chemotherapy.ConclusionsOur findings collectively provide unprecedented insights into the significance of age and ethnicity on the molecular and clinical characteristics of BC patients.

Establish a novel immune-related gene prognostic risk index (IRGPRI) associated with CD8+ cytotoxic T lymphocytes in non-small-cell lung cancer (NSCLC)

BackgroundThe aim of this study is to create an index called IRGPRI (immune-related gene prognostic risk index) that can be utilized for predicting the prognosis and assessing the efficacy of immune checkpoint inhibitors (ICIs) therapy in patients with non-small-cell lung cancer (NSCLC). MethodsDistinguishing gene expression patterns (DEGs) were detected in CD8+ cytotoxic T lymphocytes (CTLs) compared to other cellular types such as CD4 T cells, B cells, plasma cells, and CD8 Tex using the advanced technology of Single-cell RNA Sequencing (scRNA-seq). The construction of IRGPRI was accomplished by employing LASSO Cox regression analysis. We conducted a comparative analysis on clinical characteristics and molecular features, such as pathway enrichment and gene mutation, among the distinct subgroups of IRGPRI. Furthermore, we explored the correlation between immunological characteristics and IRGPRI subgroups to comprehensively assess the effectiveness of ICIs in NSCLC patients. ResultsA total of 109 genes were identified by intersecting immune-related genes with DEGs obtained from single-cell RNA sequencing data (GSE131907), specifically comparing CTLs to other cell types. From these, we selected 7 prognosis-related genes, namely TRBC1, HLA-DMA, CTSH, RAC1, CTSL, ANXA2, and CEBPB. These genes were used to construct the IRGPRI. The prognosis of patients diagnosed with NSCLC was found to be significantly better in the low-risk group compared to the high-risk group, as demonstrated by Kaplan-Meier (K-M) survival analysis. This observation was further confirmed through the utilization of data from the GEO cohort. The low-risk group demonstrated an increase in pathways linked with immune response, whereas the high-risk group exhibited a higher prevalence of pathways related to cancer. Furthermore, it was noted in the TCGA cohort that there existed a significant rise in the mutation frequency of every gene within the high-risk group as opposed to the low-risk group. Missense variation emerged as the most prevalent form of mutation. According to the analysis of immune cell infiltration and function, the comprehensive findings suggest that the group with a low risk is characterized by an increased presence of plasma cells, CTLs, T cells follicular helper, Tregs, and Dendritic cell resting. Additionally, they exhibit a higher score in terms of immune function for B cells, CD8+ T cells, checkpoint activity, T cell inhibition and stimulation. Moreover, this low-risk group demonstrates greater efficacy when treated with ICIs therapy compared to the high-risk group. ConclusionsOur research effectively developed and verified a unique IRGPRI, showcasing its association with immune-related characteristics. As a result, the potential of IRGPRI as a valuable biomarker for predicting prognosis and evaluating the effectiveness of ICIs treatment in cancer is evident.

Abstract A007: Beyond continental ancestry categories: trans-population genomic similarity continuum reveals overlooked somatic-germline interactions across cancer types

Abstract A growing body of research highlighted a strong presence of continuums that describe human genomic diversity. Historical scholarship has also extensively investigated the use of discrete classification systems in human genomics further questioning their validity. To investigate the limiting effect of pre-defined stratification, we performed a wide comparative driver GWAS analysis at the trans-population (i.e. without stratification) vs continent-based population levels across 16 cancer types. Results demonstrate that in the MSK-IMPACT cohort (n = 56,826 samples), the analysis of the trans-population genomic similarity continuum captures significant somatic-germline interactions (p &amp;lt; 5x10-8) in cancer type-specific and pancancer cohorts, with a set of interactions overlooked by the discrete continent-based paradigm. We demonstrate that overlooked germline SNPs have similar allele frequencies and directional relationships with somatic alterations across pre-defined discrete categories, and that earlier stratification prevents their detection due to lowered statistical power. PheWAS analysis of detected germline variants revealed associations with cancer and non-cancer phenotypes reported in previous studies as well as novel hits. To expand the scope of the trans-population lens, we carried out a comparative analysis on 17 whole-exome TCGA cohorts to identify potential cancer drivers based on mutational frequencies. Results similarly revealed known and potential cancer drivers at the trans-population level that would be overlooked when pre-defined continent-based stratification is applied. The lens we adopt further highlights the necessity for new quantitative perspectives to comprehensively detect genomic patterns and to leverage entire genomic datasets without pre-defined stratification schemes. Citation Format: Hussein Mohsen, Tomin Perea-Chamblee, Jian Carrot-Zhang. Beyond continental ancestry categories: trans-population genomic similarity continuum reveals overlooked somatic-germline interactions across cancer types [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A007.

Prognostic impact of caspase-8 mutation in oral cavity squamous cell carcinoma.

Identifying the drive genes and inhibiting their significant signals were persistently the main concepts in cancer treatment. However, for oral cavity squamous cell carcinoma (OCSCC), the most influential genes for overall survival (OS) remain unclear. A total of 120 OCSCC patients with corresponding pathologic specimens were collected in Taiwan. Whole-exome sequencing was done and the prognostic impact of each gene was analyzed. TCGA database was used to validate. The incidences of caspase-8 mutation were 22.1% and 10.9% in the Taiwan and TCGA cohort, respectively. In the Taiwan cohort, caspase-8 mutation was the most significant independent for OS with an adjusted hazard ratio (HR) ([95% CI]: 3.83 [1.84-7.99]). It was validated by the TCGA database (HR [95% CI]: 1.51 [1.00-2.29]). The 5-year OSs of the patients with or without caspase-8 mutation were 38.1% vs. 75.3% (p < 0.001) (HR [95% CI]: 3.264 [1.645-6.438]) in the Taiwan cohort, and 26.1% vs. 49.0% (p = 0.048) (1.513 [1.001-2.288]) in the TCGA cohorts, respectively. Caspase-8 mutation was also individually associated with poor prognosis for TNM stage I/II/III/IV, respectively. CASP8 R127* and R494*, defined as pathogenic mutations in ClinVar, were presented in both cohorts. Caspase-8 mutation was the most significant genetic alteration impacting prognosis.

Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment

PurposeWe aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment.MethodsThe clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What’s more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results.ResultsAt multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868–0.931), 0.854(0.819–0.899) and 0.831(0.813–0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05).ConclusionThe combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.

PPARγ and C/EBPα enable adipocyte differentiation upon inhibition of histone methyltransferase PRC2 in malignant tumors

Loss of terminal differentiation is a hallmark of cancer and offers a potential mechanism for differentiation therapy. Polycomb Repressive Complex 2 (PRC2) serves as the methyltransferase for K27 of histone H3 that is crucial in development. While PRC2 inhibitors show promise in treating various cancers, the underlying mechanisms remain incompletely understood. Here, we demonstrated that the inhibition or depletion of PRC2 enhanced adipocyte differentiation in malignant rhabdoid tumors (MRTs) and mesenchymal stem cells (MSCs), through upregulation of PPARG and CEBPA. Mechanistically, PRC2 directly represses their transcription through H3K27 methylation, as both genes exhibit a bivalent state in MSCs. Knockout of PPARG compromised C/EBPα expression and impeded the PRC2 inhibitor-induced differentiation into adipocytes. Furthermore, the combination of the PPARγ agonist rosiglitazone and the PRC2 inhibitor MAK683 exhibited a higher inhibition on Ki67 positivity in tumor xenograft compared to MAK683 alone. High CEBPA, PLIN1 and FABP4 levels positively correlated with favorable prognosis in sarcoma patients in TCGA cohort. Together, these findings unveil an epigenetic regulatory mechanism for PPARG and highlight the essential role of PPARγ and C/EBPα in the adipocyte differentiation of MRTs and sarcomas with a potential clinical implication.

Molecular Classification of HNSCC Based on Inflammatory Response-Related Genes - Integrated Single-Cell and Bulk RNA-Seq Analysis.

Tumor cells, inflammatory cells, and chemical factors collaboratively orchestrate a sophisticated signaling network, culminating in the formation of the inflammatory tumor microenvironment (TME). The present study sought to explore the nature of the inflammatory response in HNSCC and to decipher its influence on immunotherapeutic. A thorough analysis was performed utilizing the TCGA cohort along with two GEO cohorts. Unsupervised clustering of 200 inflammatory response-related genes (IRGs) was applied using the k-means algorithm to explore the heterogeneity of HNSCC. Additionally, a prognostic signature based on IRGs genes was constructed using Lasso regression. Meanwhiles, the expression of IRGs were identified in tumors and paracancerous tissues at the single-cell level. The crosstalk between IRGs was explored using CellChat and the patterns of incoming and outgoing signals were identified. Finally, qPCR was used to verify the expression of hub genes. There were significant differences in immune-cell function and immune-cell infiltration among three inflammatory response clusters. Additionally, we also constructed a prognostic model which could predicted the responses of common chemotherapeutic drugs and immunotherapy. Furthermore, qPCR and sc-RNA seq corroborated that the expression profiles of the prognostic genes were largely in alignment with the findings from the bioinformatics analysis. Ultimately, the molecular docking demonstrated favorable binding affinities between the pivotal gene-SCC7 and four chemotherapeutic drugs. This research has uniquely shed light on the intricate connection between the inflammatory response profiles and the immune infiltration patterns in HNSCC.