Transcriptomic analysis-guided assessment of precision-cut tumor slices (PCTS) as an ex-vivo tool in cancer research.

With cancer immunotherapy and precision medicine dynamically evolving, there is greater need for pre-clinical models that can better replicate the intact tumor and its complex tumor microenvironment (TME). Precision-cut tumor slices (PCTS) have recently emerged as an ex vivo human tumor model, offering the opportunity to study individual patient responses to targeted therapies, including immunotherapies. However, little is known about the physiologic status of PCTS and how culture conditions alter gene expression. In this study, we generated PCTS from head and neck cancers (HNC) and mesothelioma tumors (Meso) and undertook transcriptomic analyses to understand the changes that occur in the timeframe between PCTS generation and up to 72 h (hrs) in culture. Our findings showed major changes occurring during the first 24 h culture period of PCTS, involving genes related to wound healing, extracellular matrix, hypoxia, and IFNγ-dependent pathways in both tumor types, as well as tumor-specific changes. Collectively, our data provides an insight into PCTS physiology, which should be taken into consideration when designing PCTS studies, especially in the context of immunology and immunotherapy.

Comprehensive investigation into the influence of glycosylation on head and neck squamous cell carcinoma and development of a prognostic model for risk assessment and anticipating immunotherapy.

Background: It has been well established that glycosylation plays a pivotal role in initiation, progression, and therapy resistance of several cancers. However, the correlations between glycosylation and head and neck squamous cell carcinoma (HNSCC) have not been elucidated in detail. Methods: The paramount genes governing glycosylation were discerned via the utilization of the Protein-Protein Interaction (PPI) network and correlation analysis, coupled with single-cell RNA sequencing (scRNA-seq) analysis. To construct risk models exhibiting heightened predictive efficacy, cox- and lasso-regression methodologies were employed, and the veracity of these models was substantiated across both internal and external datasets. Subsequently, an exploration into the distinctions within the tumor microenvironment (TME), immunotherapy responses, and enriched pathways among disparate risk cohorts ensued. Ultimately, cell experiments were conducted to validate the consequential impact of SMS in Head and Neck Squamous Cell Carcinoma (HNSCC). Results: A total of 184 genes orchestrating glycosylation were delineated for subsequent scrutiny. Employing cox- and lasso-regression methodologies, we fashioned a 3-gene signature, proficient in prognosticating the outcomes for patients afflicted with HNSCC. Noteworthy observations encompassed distinctions in the Tumor Microenvironment (TME), levels of immune cell infiltration, and the presence of immune checkpoint markers among divergent risk cohorts, holding potentially consequential implications for the clinical management of HNSCC patients. Conclusion: The prognosis of HNSCC can be proficiently anticipated through risk signatures based on Glycosylation-related genes (GRGs). A thorough delineation of the GRGs signature in HNSCC holds the potential to facilitate the interpretation of HNSCC's responsiveness to immunotherapy and provide innovative strategies for cancer treatment.

Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration.

This study delves into the potential therapeutic benefits of Fufang Sanling Granules for kidney cancer, focusing on their active components and the underlying mechanisms of their interaction with cancer-related targets. By constructing a drug-active component-target network based on eight herbs, key active compounds such as kaempferol, quercetin, and linolenic acid were identified, suggesting their pivotal roles in modulating immune responses and cellular signaling pathways relevant to cancer progression. The research further identified 51 central drug-disease genes through comprehensive bioinformatics analyses, implicating their involvement in crucial biological processes and pathways. A novel risk score model, encompassing six genes with significant prognostic value for renal cancer, was established and validated, showcasing its effectiveness in predicting patient outcomes through mutation analysis and survival studies. The model's predictive power was further confirmed by its ability to stratify patients into distinct risk groups with significant survival differences, highlighting its potential as a prognostic tool. Additionally, the study explored the relationship between gene expression within the identified black module and the risk score, uncovering significant associations with the extracellular matrix and immune infiltration patterns. This reveals the complex interplay between the tumor microenvironment and cancer progression. The integration of the risk score with clinical parameters through a nomogram significantly improved the model's predictive accuracy, offering a more comprehensive tool for predicting kidney cancer prognosis. In summary, by combining detailed molecular analyses with clinical insights, this study presents a robust framework for understanding the therapeutic potential of Fufang Sanling Granules in kidney cancer. It not only sheds light on the active components and their interactions with cancer-related genes but also introduces a reliable risk score model, paving the way for personalized treatment strategies and improved patient management in the future.

A tale of two pathways: Review of immune checkpoint inhibitors in DNA mismatch repair-deficient and microsatellite instability-high endometrial cancers.

The DNA mismatch repair (MMR) pathway is critical for correcting DNA mismatches generated during DNA replication. MMR-deficiency (MMR-D) leads to microsatellite instability (MSI) associated with an increased mutation rate, driving cancer development. This is particularly relevant in endometrial cancer (EC) as 25%-30% of tumors are of MMR-D/MSI-high (MSI-H) phenotype. Comprehensive assessment using immunohistochemistry (IHC) and sequencing-based techniques are necessary to fully evaluate ECs given the importance of molecular subtyping in staging and prognosis. This also influences treatment selection as clinical trials have demonstrated survival benefits for immune checkpoint inhibitors (ICIs) alone and in combination with chemotherapy for MMR-D/MSI-H EC patients in various treatment settings. As a portion of MMR-D/MSI-H ECs are driven by Lynch syndrome, an inherited cancer predisposition syndrome that is also associated with colorectal cancer, this molecular subtype also prompts germline assessment that can affect at-risk family members. Additionally, heterogeneity in the tumor immune microenvironment and tumor mutation burden (TMB) have been described by MMR mechanism, meaning MLH1 promoter hypermethylation versus germline/somatic MMR gene mutation, and this may affect response to ICI therapies. Variations by ancestry in prevalence and mechanism of MMR-D/MSI-H tumors have also been reported and may influence health disparities given observed differences in tumors of Black compared to White patients which may affect ICI eligibility. These observations highlight the need for additional prospective studies to evaluate the nuances regarding MMR-D heterogeneity as well as markers of resistance to inform future trials of combination therapies to further improve outcomes for patients with EC.

A signature based on neutrophil extracellular trap-related genes for the assessment of prognosis, immunoinfiltration, mutation and therapeutic response in hepatocellular carcinoma.

BACKGROUND: Liver cancer is a highly lethal and aggressive form of cancer that poses a significant threat to patient survival. Within this category, liver hepatocellular carcinoma (LIHC) represents the most common subtype of liver cancer. Despite decades of research and treatment, the overall survival rate for LIHC has not significantly improved. Improved models are necessary to differentiate high-risk cases and predict possible treatment options for LIHC patients. Recent studies have identified a set of genes associated with neutrophil extracellular traps (NETs) that may contribute to tumor growth and metastasis; however, their prognostic value in LIHC has yet to be established. This study aims to construct a prognostic signature based on a set of NET-related genes (NRGs) for patients diagnosed with LIHC. METHODS: The transcriptomic data and clinical information concerning LIHC patients were procured from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium LIHC (ICLIHC) databases, respectively. To determine the NRG subtypes, the k-means algorithm was employed, along with consensus clustering. The aforementioned analysis aided the construction of a prognostic signature utilizing the last absolute shrinkage and selection operator Cox analysis. To validate the prognostic model, an external dataset, receiver operating characteristic curve, and principal component analysis were utilized. Moreover, the immune microenvironment and the proportion of immune cells between high- and low-risk cases were scrutinized by ESTIMATE and CIBERSORT algorithms. Finally, gene set enrichment analysis was executed to investigate the potential mechanism of NRGs in the pathogenesis and prognosis of LIHC. RESULTS: Two molecular subtypes of LIHC were identified based on the expression patterns of differentially expressed NRGs (DE-NRGs). The two subtypes demonstrated significant differences in survival rates and immune cell expression levels. The study results demonstrated the role of NRGs in antigen presentation, which led to the promotion of tumor immune escape. A risk model was developed and validated with strong overall survival prediction ability. The model, comprising 34 NRGs, showed a strong ability to predict prognosis. CONCLUSION: We built a dependable prognostic signature based on NRGs for LIHC. We identified that NRGs could have a significant interaction in LIHC's immune microenvironment and therapeutic response. This finding offers insight into the molecular mechanisms and targeted therapy for LIHC.

Identification of prognostic biomarkers for cervical cancer based on programmed cell death-related genes and assessment of their immune profile and response to drug therapy.

BACKGROUND: Programmed cell death (PCD) has been widely investigated in various human diseases. The present study aimed to identify a novel PCD-related genetic signature in cervical squamous cell carcinoma (CESC) to provide clues for survival, immunotherapy and drug sensitization prediction. METHODS: Single-sample gene set enrichment analysis (ssGSEA) was used to quantify the PCD score and assess the distribution of PCD in clinicopathological characteristics in The Cancer Genome Atlas (TCGA)-CESC samples. Then, the ConsensusClusterPlus method was used to identify molecular subtypes in the TCGA-CESC database. Genomic mutation analysis, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment, as well as tumor microenvironment (TME) infiltration analysis, were performed for each molecular subtype group. Finally, a prognostic model by Uni-Cox and least absolute shrinkage and selection operator-Cox analysis was established based on differentially expressed genes from molecular subtypes. ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) and ssGSEA were performed to assess the correlation between the model and TME. Drug sensitization prediction was carried out with the oncoPredict package. RESULTS: Preliminary analysis indicated that PCD had a potential association clinical characteristics of the TCGA-CESC cohort, and PCD-related genes mutated in 289 (70.59%) CESC patients. Next, four groups of CESC molecular typing were clustered based on 63 significantly prognostic PCD-related genes. Among four subtypes, C1 group displayed the worst prognosis combined with over expressed PCD genes and enriched cell cycle-related pathways. C4 group exhibited the best prognosis accompanied with high degree of immune infiltration. Finally, a five-gene (SERPINE1, TNF, CA9, CX3CL1 and JAK3) prognostic model was constructed. Patients in the high-risk group displayed unfavorable survival. Immune infiltration analysis found that the low-risk group had significantly higher levels of immune cell infiltration such as T cells, Macrophages_M1, relative to the high-risk group, and were significantly enriched in apoptosis-associated pathways, which predicted a higher level of immunity. Drug sensitivity correlation analysis revealed that the high-risk group was resistant to conventional chemotherapeutic drugs and sensitive to the Food and Drug Administration-approved drugs BI.2536_1086 and SCH772984_1564. CONCLUSIONS: In the present study, we first found that PCD-related gene expression patterns were correlated with clinical features of CESC patients, which predicts the feasibility of subsequent mining of prognostic features based on these genes. The five-PCD-associated-gene prognostic model showed good assessment ability in predicting patient prognosis, immune response and drug-sensitive response, and provided guidance for the elucidation of the mechanism by which PCD affects CESC, as well as for the clinical targeting of drugs.

Comprehensive assessment of base excision repair (BER)-related lncRNAs as prognostic and functional biomarkers in lung adenocarcinoma: implications for personalized therapeutics and immunomodulation.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer, and comprehending its molecular mechanisms is pivotal for advancing treatment efficacy. This study aims to explore the prognostic and functional significance of base excision repair (BER)-related long non-coding RNAs (BERLncs) in LUAD. METHODS: A risk score model for BERLncs was developed using the least absolute shrinkage and selection operator regression and Cox regression analysis. Model validation and prognostic evaluation were performed using Kaplan-Meier and receiver-operating characteristic curve analyses. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted to elucidate the potential biological functions of BERLncs. Comparative analyses were carried out to investigate disparities in tumor mutation burden (TMB), immune infiltration, tumor immune dysfunction and exclusion (TIDE) score, chemosensitivity, and immune checkpoint gene expression between the two risk groups. RESULTS: A predictive risk score model comprising 19 BERLncs was successfully developed. Patients were divided into high-risk and low-risk groups according to the median risk score. The high-risk subgroup exhibited significantly inferior overall survival. Functional enrichment analysis revealed pathways associated with lung cancer development, notably the neuroactive ligand-receptor interaction pathway. High-risk patients demonstrated elevated TMB, diminished TIDE scores, and an immunosuppressive tumor microenvironment, while low-risk patients displayed potential benefits from immunotherapy. Additionally, the risk model identified potential anticancer agents. CONCLUSION: The risk score model based on BERLncs shows promise as a prognostic biomarker for LUAD patients, providing valuable insights for clinical decision-making, therapeutic strategies, and understanding of underlying biological mechanisms.

Landmark Series: The Cancer Genome Atlas and the Study of Breast Cancer Disparities.

Race-related variation in breast cancer incidence and mortality are well-documented in the United States. The effect of genetic ancestry on disparities in tumor genomics, risk factors, treatment, and outcomes of breast cancer is less understood. The Cancer Genome Atlas (TCGA) is a publicly available resource that has allowed for the recent emergence of genome analysis research seeking to characterize tumor DNA and protein expression by ancestry as well as the social construction of race and ethnicity. Results from TCGA based studies support previous clinical evidence that demonstrates that American women with African ancestry are more likely to be afflicted with breast cancers featuring aggressive biology and poorer outcomes compared with women with other backgrounds. Data from TCGA based studies suggest that Asian women have tumors with favorable immune microenvironments and may experience better disease-free survival compared with white Americans. TCGA contains limited data on Hispanic/Latinx patients due to small sample size. Overall, TCGA provides important opportunities to define the molecular, biologic, and germline genetic factors that contribute to breast cancer disparities.

Colorectal Cancer Organoid-Stroma Biobank Allows Subtype-Specific Assessment of Individualized Therapy Responses.

In colorectal cancers, the tumor microenvironment plays a key role in prognosis and therapy efficacy. Patient-derived tumor organoids (PDTO) show enormous potential for preclinical testing; however, cultured tumor cells lose important characteristics, including the consensus molecular subtypes (CMS). To better reflect the cellular heterogeneity, we established the colorectal cancer organoid-stroma biobank of matched PDTOs and cancer-associated fibroblasts (CAF) from 30 patients. Context-specific phenotyping showed that xenotransplantation or coculture with CAFs improves the transcriptomic fidelity and instructs subtype-specific stromal gene expression. Furthermore, functional profiling in coculture exposed CMS4-specific therapeutic resistance to gefitinib and SN-38 and prognostic expression signatures. Chemogenomic library screening identified patient- and therapy-dependent mechanisms of stromal resistance including MET as a common target. Our results demonstrate that colorectal cancer phenotypes are encrypted in the cancer epithelium in a plastic fashion that strongly depends on the context. Consequently, CAFs are essential for a faithful representation of molecular subtypes and therapy responses ex vivo. SIGNIFICANCE: Systematic characterization of the organoid-stroma biobank provides a resource for context dependency in colorectal cancer. We demonstrate a colorectal cancer subtype memory of PDTOs that is independent of specific driver mutations. Our data underscore the importance of functional profiling in cocultures for improved preclinical testing and identification of stromal resistance mechanisms. This article is featured in Selected Articles from This Issue, p. 2109.

Multi-omics analysis revealing a senescence-relevant lncRNAs signature for the assessment of response to immunotherapy for breast cancer.

Breast cancer (BRCA) is the most fatal malignancy of women. Immunotherapy has greatly improved the prognosis of advanced BRCA. Cellular senescence contributes to tumorigenesis and suppresses anti-cancer immunity. Identification of senescence-relevant long noncoding RNAs (SRlncRNAs) signature may benefit the predictions of prognosis and response to immunotherapy of BRCA. RNA-seq, mutation, and clinical data of BRCA were acquired from public databases. SRlncRNAs were screened using univariate Cox regression analysis. Consensus clustering classified BRCA patients into 2 clusters, and the differences of overall survival (OS) and immune status between the 2 clusters were analyzed by survival analysis, CIBERSORT, and ESITIMATE. The SRlncRNAs signature was constructed by least absolute shrinkage and selection operator (LASSO) regression analysis, and BRCA patients were divided into 2 risk groups. Enrichment analyses were performed to explore the cancer- and immunotherapy-relevant pathways. Transcriptome analysis was performed to investigate the differences of OS, immune infiltration, and ESITIMATE score of the 2 groups. Genome analysis was applied to investigate the differences of somatic mutation, tumor mutation burden (TMB) and microsatellite instability (MSI) between the 2 risk groups. A nomogram combined with calibration curves and decision curve analysis (DCA) was established for better clinical decision. Tumor Immune Dysfunction and Exclusion (TIDE) score and IMvigor-210 were applied for the predicting of response to immunotherapy. Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) and the Cancer Therapeutics Response Portal resource (CTRP) databases were used for drug susceptibility analysis. Ten prognostic SRlncRNAs were identified and BRCA patients were divided into 2 clusters. Cluster 1 had better OS with anti-tumor immune microenvironment. The high-risk BRCA had poorer OS in the Cancer Genome Atlas (TCGA) training cohort, which was also verified by TCGA validation cohort and GSE20685 validation cohort. Low-risk patients also had anti-tumor immune microenvironment. Genome analysis demonstrated that the high-risk group had significant higher TMB. High-risk BRCA were more susceptive to immunotherapy according to the TIDE score and IMvigor-210. Finally, drug susceptibility analysis showed that 6 compounds were sensitive to high-risk BRCA patients. We developed and verified an original SRlncRNAs signature by multi-omics analysis, which could serve as a prognosis and immunotherapy predictor for BRCA.

Assessment of RACGAP1 as a Prognostic and Immunological Biomarker in Multiple Human Tumors: A Multiomics Analysis.

Several recent studies have pointed out that arc GTPase activating protein 1 (RACGAP1) is a putative oncogene in many human tumors. However, to date, no pan-cancer analysis has been performed to study the different aspects of this gene expression and behavior in tumor tissues. Here, we applied several bioinformatics tools to perform a comprehensive analysis for RACGAP1. First, we assessed the expression of RACGAP1 in several types of human tumors and tried to correlate that with the stage of the tumors analyzed. We then performed a survival analysis to study the correlation between RACGAP1 upregulation in tumors and the clinical outcome. Additionally, we investigated the mutation forms, the correlation with several immune cell infiltration, the phosphorylation status of the interested protein in normal and tumor tissues, and the potential molecular mechanisms of RACGAP1 in cancerous tissue. The results demonstrated that RACGAP1, a highly expressed gene across several types of tumors, correlated with a poor prognosis in several types of human cancers. Moreover, it was found that RACGAP1 affects the tumor immune microenvironment by influencing the infiltration level of several immune cells. Collectively, the current study provides a comprehensive overview of the oncogenic roles of RACGAP1, where our results nominate it as a potential prognostic biomarker and a target for antitumor therapy development.

An ion-channel-gene-based prediction model for head and neck squamous cell carcinoma: Prognostic assessment and treatment guidance.

Purpose: Head and neck squamous cell carcinoma (HNSCC) is a very diverse malignancy with a poor prognosis. The purpose of this study was to develop a new signature based on 12 ion channel genes to predict the outcome and immune status of HNSCC patients. Methods: Clinicopathological information and gene sequencing data of HNSCC patients were generated from the Cancer Genome Atlas and Gene Expression Omnibus databases. A set of 323 ion channel genes was obtained from the HUGO Gene Nomenclature Committee database and literature review. Using univariate Cox regression analysis, the ion channel genes related to HNSCC prognosis were identified. A prognostic signature and nomogram were then created using machine learning methods. Kaplan-Meier analysis was used to explore the relevance of the risk scores and overall survival (OS). We also investigated the association between risk scores, tumor immune infiltration, and gene mutational status. Finally, we detected the expression levels of the signature genes by quantitative real-time polymerase chain reaction, western blotting, and immunohistochemistry. Results: We separated the patients into high- and low-risk groups according to the risk scores computed based on these 12 ion channel genes, and the OS of the low-risk group was significantly longer (p<0.001). The area under the curve for predicting 3-year survival was 0.729. Univariate and multivariate analyses showed that the 12-ion-channel-gene risk model was an independent prognostic factor. We also developed a nomogram model based on risk scores and clinicopathological variables to forecast outcomes. Furthermore, immune cell infiltration, gene mutation status, immunotherapy response, and chemotherapeutic treatment sensitivity were all linked to risk scores. Moreover, high expression levels of ANO1, AQP9, and BEST2 were detected in HNSCC tissues, whereas AQP5, SCNN1G, and SCN4A expression was low in HNSCC tissues, as determined by experiments. Conclusion: The 12-ion-channel-gene prognostic signatures have been demonstrated to be highly efficient in predicting the prognosis, immune microenvironment, gene mutation status, immunotherapy response, and chemotherapeutic sensitivity of HNSCC patients.

Molecular subtyping based on TRP family and prognostic assessment for TRP-associated lncRNAs in pancreatic adenocarcinoma.

BACKGROUND: Transient receptor potential (TRP) channels have high permeability to Ca2+ ions because they are non-selective ion channels. TRP channels have been implicated in tumor onset and progression, proliferation, and migration in recent years. However, the prognostic value of genes related to TRP and their specific mechanism in pancreatic adenocarcinoma (PAAD) are yet to be understood. METHODS: Public databases such as TCGA and GEO were used to retrieve data on gene expression and clinical information of patients with pancreatic adenocarcinoma for our study. ConsensusClusterPlus package was used for unsupervised clustering analysis. The microenvironment cell population (MCP)-counter approach was employed to measure the immune cells infiltration status. The Pearson correlation was performed to identify TRP-associated lncRNAs. RESULTS: Initially, we separated PAAD patients into three clusters depending on TRP-related genes, and of the three clusters, cluster B showed the least immune cell infiltration, which was correlated with poor prognosis. Moreover, GSVA enrichment analysis further revealed that cluster A was subjected to a considerable enrichment in carcinogenic signaling pathways, whereas cluster C was enriched in immune-related pathways. Then, using TRP-associated lncRNAs as a starting point, we constructed a prognostic risk model for PAAD patients that could efficiently predict their prognosis. Further, GSEA revealed that cancer-related pathways, for instance, the cell cycle, p53 signaling pathway, etc. were considerably enriched in the high-risk group. In addition, we looked into the link between the prognostic model and the immunological microenvironment. Lower cytotoxic lymphocytes, NK cells, CD8 T cells, and endothelial cells infiltration were found to be associated with high risk using the MCP-counter algorithm. The expression of CD274, POLE2, MCM6, and LOXL2 was also found to be higher in the high-risk group. TMB was also considerably greater in high-risk individuals, indicating that immune checkpoint inhibitors (ICIs) therapy may benefit them more. Lastly, qRT-PCR further confirmed the differential expression of these prognostic TRP-associated lncRNAs, indicating that these lncRNAs play an imperative role in PAAD tumorigenesis. CONCLUSION: TRP family genes may represent a new class of candidate molecular markers of the occurrence and progression of PAAD. Risk models based on TRP-associated lncRNAs could provide important new references for immunotargeted therapy of pancreatic adenocarcinoma.

Immune-Related LncRNAs to Construct a Prognosis Risk-Assessment Model for Gastric Cancer.

BACKGROUND: Gastric cancer is a prevalent cause of tumor death. Tumor immunotherapy aims to reshape the specific immunity to tumors in order to kill the tumor. LncRNAs play a pivotal role in regulating the tumor immune microenvironment. Herein, immune-related lncRNAs were used to establish a prognosis risk-assessment model for gastric cancer and provide personalized predictions while providing insights and targets for gastric cancer treatment to enhance patient prognosis. METHODS: Gastric adenocarcinoma transcriptome and clinical data were acquired from the The Cancer Genome Atlas (TCGA) database to screen the immune-related lncRNAs. Then, LASSO COX regression was utilized to construct the prognosis risk-assessment model. Afterward, the reliability of the model was evaluated the relationship between immune infiltration, clinical characteristics, and the model was analyzed. RESULTS: We identified 13 lncRNAs and constructed the prognosis assessment model. According to the median risk score of the training set, the patients were assigned to different risk groups. Overall survival time was shorter in the high-risk group. In the high-risk group, higher infiltration of mono-macrophages, dendritic cells, CD4+ T cells, and CD8+ T cells was observed. Moreover, the model was positively related to tumor metastasis. CONCLUSION: The prognosis risk-assessment model developed in this research can effectively predict the prognosis of gastric cancer patients. This tool is expected to be further applied to clinics in the future, thus providing a novel target for immunotherapy in gastric cancer patients.

Tumor mutational burden presents limiting effects on predicting the efficacy of immune checkpoint inhibitors and prognostic assessment in adrenocortical carcinoma.

BACKGROUND: Adrenocortical carcinoma (ACC) is a highly malignant urologic cancer and tends to metastasize. Although immune checkpoint inhibitors (ICIs) bring a glimmer of light to conquer ACC, only a fraction of patients have benefit from ICIs treatment. It is well known that tumor mutational burden (TMB) is closely associated with the efficacy and response rate of immunotherapy. However, its roles in ACC were not investigated. METHODS: Using somatic mutations data of 92 ACC samples in TCGA database, we calculated their TMB values by the 'maftools' package in R software (Ver 3.6.3). To explore the roles of TMB in ICIs therapy, we have addressed this issue from three perspectives. First, the effects of TMB levels on tumor immune microenvironment (TIM) were analyzed through CIBERSORT algorithm, ssGSEA method and TIMER web server. Second, we investigated the expressive correlations between TMB level and five pivotal immune checkpoints based on Pearson coefficient. Third, the difference in TIDE score between high- and low-TMB groups was compared. The prognostic value of TMB was also evaluated. Besides, GSEA was performed to determine the changes in the activities of signaling pathways caused by TMB. RESULTS: TMB values in ACC samples were not high. The average of total mutation counts in each sample was only 21.5. High TMB could lead metabolic reprogramming and poor survival outcomes. However, it was unable to affect the infiltration levels of lymphocytes, and failed to facilitate the activities of immune-related pathways. Regarding immune checkpoints (ICs), only PD-L1 upregulation could result in a good prognosis, and TMB level did not correlate with the expressions of other ICs except for LAG3. There was no significant difference in TIDE score between high- and low-TMB groups. Combining the present results and previous study, we speculated that inadequate stimulation for neoantigens formation, intrinsic immune-resistance and special genomic alterations were three possible reasons for TMB limiting functions in TIM and ICIs. Besides, TMB was toughly applied in clinical practice due to its high cost of determination and non-universal definition of high TMB. CONCLUSIONS: TMB presents limiting effects on prediction for ICIs efficacy and prognostic assessment for ACC patients.

Construction of an algorithm based on oncosis-related LncRNAs comprising the molecular subtypes and a risk assessment model in lung adenocarcinoma.

BACKGROUND: As an important non-apoptotic cell death method, oncosis has been reported to be closely associated with tumors in recent years. However, few research reported the relationship between oncosis and lung cancer. METHODS: In this study, we established an oncosis-based algorithm comprised of cluster grouping and a risk assessment model to predict the survival outcomes and related tumor immunity of patients with lung adenocarcinomas (LUAD). We selected 11 oncosis-related lncRNAs associated with the prognosis (CARD8-AS1, LINC00941, LINC01137, LINC01116, AC010980.2, LINC00324, AL365203.2, AL606489.1, AC004687.1, HLA-DQB1-AS1, and AL590226.1) to divide the LUAD patients into different clusters and different risk groups. Compared with patients in clsuter1, patients in cluster2 had a survival advantage and had a relatively more active tumor immunity. Subsequently, we constructed a risk assessment model to distinguish between patients into different risk groups, in which low-risk patients tend to have a better prognosis. GO enrichment analysis revealed that the risk assessment model was closely related to immune activities. In addition, low-risk patients tended to have a higher content of immune cells and stromal cells in tumor microenvironment, higher expression of PD-1, CTLA-4, HAVCR2, and were more sensitive to immune checkpoint inhibitors (ICIs), including PD-1/CTLA-4 inhibitors. The risk score had a significantly positive correlation with tumor mutation burden (TMB). The survival curve of the novel oncosis-based algorithm suggested that low-risk patients in cluster2 have the most obvious survival advantage. CONCLUSION: The novel oncosis-based algorithm investigated the prognosis and the related tumor immunity of patients with LUAD, which could provide theoretical support for customized individual treatment for LUAD patients.

Comprehensive assessment of cellular senescence in the tumor microenvironment.

Cellular senescence (CS), a state of permanent growth arrest, is intertwined with tumorigenesis. Due to the absence of specific markers, characterizing senescence levels and senescence-related phenotypes across cancer types remain unexplored. Here, we defined computational metrics of senescence levels as CS scores to delineate CS landscape across 33 cancer types and 29 normal tissues and explored CS-associated phenotypes by integrating multiplatform data from ~20 000 patients and ~212 000 single-cell profiles. CS scores showed cancer type-specific associations with genomic and immune characteristics and significantly predicted immunotherapy responses and patient prognosis in multiple cancers. Single-cell CS quantification revealed intra-tumor heterogeneity and activated immune microenvironment in senescent prostate cancer. Using machine learning algorithms, we identified three CS genes as potential prognostic predictors in prostate cancer and verified them by immunohistochemical assays in 72 patients. Our study provides a comprehensive framework for evaluating senescence levels and clinical relevance, gaining insights into CS roles in cancer- and senescence-related biomarker discovery.

Assessment of the targeted effect of Sijunzi decoction on the colorectal cancer microenvironment via the ESTIMATE algorithm.

OBJECTIVE: Sijunzi decoction (SJZD) was used to treat patients with colorectal cancer (CRC) as an adjuvant method. The aim of the study was to investigate the therapeutic targets and pathways of SJZD towards the tumor microenvironment of CRC via network pharmacology and the ESTIMATE algorithm. METHODS: The ESTIMATE algorithm was used to calculate immune and stromal scores to predict the level of infiltrating immune and stromal cells. The active targets of SJZD were searched in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and UniProt database. The core targets were obtained by matching the differentially expressed genes in CRC tissues and the targets of SJZD. Then, GO, KEGG and validation in TCGA were carried out. RESULTS: According to the ESTIMATE algorithm and survival analysis, the median survival time of the low stromal score group was significantly higher than that of the high stromal score group (P = 0.018), while the patients showed no significant difference of OS between different immune groups (P = 0.19). A total of 929 genes were upregulated and 115 genes were downregulated between the stromal score groups (|logFC| > 2, adjusted P < 0.05); 357 genes were upregulated and 472 genes were downregulated between the immune score groups. The component-target network included 139 active components and 52 related targets. The core targets were HSPB1, SPP1, IGFBP3, and TGFB1, which were significantly associated with poor prognosis in TCGA validation. GO terms included the response to hypoxia, the extracellular space, protein binding and the TNF signaling pathway. Immunoreaction was the main enriched pathway identified by KEGG analysis. CONCLUSION: The core genes (HSPB1, SPP1, IGFBP3 and TGFB1) affected CRC development and prognosis by regulating hypoxia, protein binding and epithelial-mesenchymal transition in the extracellular matrix.

Clinical and molecular assessment of an onco-immune signature with prognostic significance in patients with colorectal cancer.

Understanding the complex tumor microenvironment is key to the development of personalized therapies for the treatment of cancer including colorectal cancer (CRC). In the past decade, significant advances in the field of immunotherapy have changed the paradigm of cancer treatment. Despite significant improvements, tumor heterogeneity and lack of appropriate classification tools for CRC have prevented accurate risk stratification and identification of a wider patient population that may potentially benefit from targeted therapies. To identify novel signatures for accurate prognostication of CRC, we quantified gene expression of 12 immune-related genes using a medium-throughput NanoString quantification platform in 93 CRC patients. Multivariate prognostic analysis identified a combined four-gene prognostic signature (TGFB1, PTK2, RORC, and SOCS1) (HR: 1.76, 95% CI: 1.05-2.95, *p < 0.02). The survival trend was captured in an independent gene expression data set: GSE17536 (177 patients; HR: 3.31, 95% CI: 1.99-5.55, *p < 0.01) and GSE14333 (226 patients; HR: 2.47, 95% CI: 1.35-4.53, *p < 0.01). Further, gene set enrichment analysis of the TCGA data set associated higher prognostic scores with epithelial-mesenchymal transition (EMT) and inflammatory pathways. Comparatively, a lower prognostic score was correlated with oxidative phosphorylation and MYC and E2F targets. Analysis of immune parameters identified infiltration of T-reg cells, CD8+ T cells, M2 macrophages, and B cells in high-risk patient groups along with upregulation of immune exhaustion genes. This molecular study has identified a novel prognostic gene signature with clinical utility in CRC. Therefore, along with prognostic features, characterization of immune cell infiltrates and immunosuppression provides actionable information that should be considered while employing personalized medicine.

Identification and characterization of nucleotide metabolism and neuroendocrine regulation-associated modification patterns in stomach adenocarcinoma with auxiliary prognostic assessment and immunotherapy response prediction.

Background: The significance of nucleotide metabolism and neuroendocrine in cellular immune response and cancer is becoming more well-established. However, the mechanisms underlying nucleotide metabolism and neuroendocrine involvement in stomach adenocarcinoma (STAD) remain unclear. Methods: First, a pan-cancer overview of nucleotide metabolism and neuroendocrine-related genes (NMNGs) was explored through the integration of expression profiles, prognostic values, mutation information, methylation levels, and pathway-regulation relationships. We next extensively assessed variations in prognosis and tumor microenvironment (TME) features across the various modification patterns, based on an extensive analysis of the NMNG modification patterns of 808 STAD samples based on 46 NMNGs. Utilizing principal component analysis methodologies, the NMNGscore was developed to measure NMNG alteration patterns of individual tumors. Results: Pan-cancer analysis shows that NMNGs mostly act as risk genes in multiple cancer types, especially in STAD. Based on the NMNGs we detected two different NMNG modification patterns in STAD. Both patterns showed distinct immune cell infiltration features and biological behavior, with NMNGcluster A exhibiting a worse prognosis and a larger amount of immune infiltration. Differentially expressed genes with prognostic relevance were used to classify the STAD samples into three genomic subgroups. Analysis of survival rates revealed that cluster B genes were associated with longer life expectancy than clusters A and C. Individual STAD patients' NMNG alteration patterns were analyzed by analyzing their NMNGscore signatures. NMNGscore and immune cells showed a statistically significant adverse correlation with each other. Increased longevity, a higher incidence of mutations, and a better response to immunotherapy were associated with patients' NMNG scores. Conclusions: Our findings provide a personalized prediction tool for prognosis and immunotherapy sensitivity in patients, as well as a promising knowledge of nucleotide metabolism and neuroendocrine in STAD.