Tumor Immune Status Research Articles

Construction and validation of a prognostic model for predicting clear cell renal cell carcinoma based on complement-related genes.

Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous tumor and is the most common subtype of renal cell carcinoma (RCC). Surgery is used to cure most early ccRCC, but the 5-year overall survival (OS) of ccRCC patients is far from satisfactory. Thus, new prognostic features and therapeutic targets for ccRCC need to be identified. Since complement factors can influence tumor development, we aimed to develop a model to predict the prognosis of ccRCC through complement-related genes. Differentially expressed genes were screened from an International Cancer Genome Consortium (ICGC) data set, and the genes associated with prognosis were screened by univariate regression and least absolute shrinkage and selection operator-Cox regression, and column line plots were generated using the rms R package to predict OS. The C-index was used to show the accuracy of the survival prediction and the prediction effects were verified using a data set from The Cancer Genome Atlas (TCGA). An immuno-infiltration analysis was performed with CIBERSORT analysis, and a drug sensitivity analysis was performed using the Gene Set Cancer Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA/#/) database. We identified 5 complement-related genes (i.e., A2M, APOBEC3G, COL4A2, DOCK4, and NOTCH4) for risk-score modeling to predict OS at 1, 2, 3, and 5 years, and the C-index of the prediction mode was 0.795. In addition, the model was successfully validated in TCGA data set. The CIBERSORT analysis showed that M1 macrophages were downregulated in the high-risk group. The GSCA database analysis showed that DOCK4, COL4A2, and A2M were positively correlated with the half maximal inhibitory concentration (IC50) of 10 drugs and small molecules, and COL4A2, NOTCH4, A2M, and APOBEC3G were negatively correlated with the IC50 of dozens of different drugs and small molecules. We developed and validated a survival prognostic model based on 5 complement-related genes for ccRCC. We also elucidated the relationship with tumor immune status and developed a new predictive tool for clinical purposes. In addition, our results showed that A2M, APOBEC3G, COL4A2, DOCK4, and NOTCH4 may be potential targets for the treatment of ccRCC in the future.

Identification of a Novel Cuproptosis-Related Gene Signature and Integrative Analyses in Thyroid Cancer

Cuproptosis is a novel programmed cell death that depends on copper. The role and potential mechanism of cuproptosis-related genes (CRGs) in thyroid cancer (THCA) are still unclear. In our study, we randomly divided THCA patients from the TCGA database into a training set and a testing set. A cuproptosis-related signature consisting of six genes (SLC31A1, LIAS, DLD, MTF1, CDKN2A, and GCSH) was constructed using the training set to predict the prognosis of THCA and was verified with the testing set. All patients were classified into low- and high-risk groups according to risk score. Patients in the high-risk group had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) values for 5 years, 8 years, and 10 years were 0.845, 0.885, and 0.898, respectively. The tumor immune cell infiltration and immune status were significantly higher in the low-risk group, which indicated a better response to immune checkpoint inhibitors (ICIs). The expression of six cuproptosis-related genes in our prognostic signature were verified by qRT-PCR in our THCA tissues, and the results were consistent with TCGA database. In summary, our cuproptosis-related risk signature has a good predictive ability regarding the prognosis of THCA patients. Targeting cuproptosis may be a better alternative for THCA patients.

Abstract P1-05-17: Radiomic models based on dynamic contrast-enhanced magnetic resonance imaging predict the immunophenotype reflecting spatial distribution of CD8+ tumor-infiltrating lymphocytes in breast cancer

Abstract Purpose: This study aimed to develop noninvasive magnetic resonance imaging (MRI)-based radiomic models to predict immunophenotypes of breast cancer. Methods: A total of 182 breast cancer patients who underwent upfront surgery were analyzed, divided into training (n = 137) and validation (n = 45) cohorts. Dynamic contrast-enhanced (DCE)-MRI was acquired, and immunophenotype was determined on the surgical tumor sections: immune-inflamed (high degree of CD8+ T cells infiltrated), -excluded (CD8+ T cells accumulated at invasive margin but not efficiently infiltrated in tumor bed), and -desert (CD8+ T cells absent within tumor and at margins). Based on 833 radiomic features extracted after manual delineation, the least absolute shrinkage and selection operator method was used to build radiomic models. Results: Our radiomic models from the whole tumor sections showed moderate performance in predicting the immune-inflamed versus non-inflamed tumors, showing the highest AUC values, of 0.659 and 0.671 for training and validation cohorts, respectively. Combining the models, much improved predictability was observed, with AUC values of 0.973 and 0.985 for training and validation cohorts, respectively. Other radiomic features from tumor periphery data discriminated immune-excluded versus immune-desert status, with AUC values of 0.993 and 0.984 for training and validation cohorts, respectively. The combined models were also applicable to predicting immunophenotype for different molecular subtypes, with AUC values ≥ 0.867. Conclusions: By integrating the immunohistochemistry profiles, we established MRI-derived radiomic models to predict the detailed immunophenotype of breast cancer. This study suggests the feasibility of noninvasive assessment of tumor immune status in real-world clinics. Citation Format: Seung Hyuck Jeon, So-Woon Kim, Mirinae Seo, Yu Jin Lim. Radiomic models based on dynamic contrast-enhanced magnetic resonance imaging predict the immunophenotype reflecting spatial distribution of CD8+ tumor-infiltrating lymphocytes in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-17.

TIMEAS, a promising method for the stratification of testicular germ cell tumor patients with distinct immune microenvironment, clinical outcome and sensitivity to frontline therapies.

With the heterogeneous genetic background, prognosis prediction and therapeutic targets for testicular germ cell tumors (TGCTs) are still unclear. We defined the tumor immune microenvironment activation status (TIMEAS). We collected a total of 314 TGCT patients from four cohorts, including a 48-case microarray. A nonnegative matrix factorization algorithm was applied to identify the "immune factor", derived the top 150 weighted genes to divide patients into immune and non-immune classes, and further separated the immune class into activated and exhausted subgroups by nearest template prediction. Tumor mutant burden, gene mutation, and copy number alteration were compared with our recently developed package "MOVICS". A random forest algorithm was performed to establish a prediction model with fewer genes. Immunohistochemistry staining was performed to identify TIMEAS in the microarray. We constructed the TIMEAS in the TCGA-TGCT cohort and further validated it in the GSE3218 and GSE99420 cohorts. The immune class contained the activated status of T-lymphocytes, B-lymphocytes, and macrophages, while Treg cells and the WNT/TGFβ signature were more activated in the immune-suppressed subgroup. Patients in the immune-exhausted subgroup had the worst prognosis, and 22.9% of patients in the immune-activated subgroup had KRAS mutations, which might stimulate the response of the immune system and lead to a favorable prognosis. The immune-exhausted group benefited more from chemotherapy, while the immune-activated subgroup responded well to anti-PD-1/PD-L1 therapy. FSCN1 was validated as the target of the immune-exhausted microenvironment by immunohistochemistry. TIMEAS classification can separate TGCT patients; patients in the immune-activated subgroup could benefit more from anti-PD-L1 immunotherapy, and those in the immune-exhausted subgroup are more suitable for chemotherapy.

Prognostic 7-SLC-Gene Signature Identified via Weighted Gene Co-Expression Network Analysis for Patients with Hepatocellular Carcinoma.

Solute carrier (SLC) proteins play an important role in tumor metabolism. But SLC-associated genes' prognostic significance in hepatocellular carcinoma (HCC) remained elusive. We identified SLC-related factors and developed an SLC-related classifier to predict and improve HCC prognosis and treatment. From the TCGA database, corresponding clinical data and mRNA expression profiles of 371 HCC patients were acquired, and those of 231 tumor samples were derived from the ICGC database. Genes associated with clinical features were filtered using weighted gene correlation network analysis (WGCNA). Next, univariate LASSO Cox regression studies developed SLC risk profiles, with the ICGC cohort data being used in validation. Univariate Cox regression analysis revealed that 31 SLC genes (P < 0.05) were related to HCC prognosis. 7 (SLC22A25, SLC2A2, SLC41A3, SLC44A1, SLC48A1, SLC4A2, and SLC9A3R1) of these genes were applied in developing a SLC gene prognosis model. Samples were classified into the low-andhigh-risk groups by the prognostic signature, with those in the high-risk group showing a significantly worse prognosis (P < 0.001 in the TCGA cohort and P=0.0068 in the ICGC cohort). ROC analysis validated the signature's prediction power. In addition, functional analyses showed enrichment of immune-related pathways and different immune status between the two risk groups. The 7-SLC-gene prognostic signature established in this study helped predict the prognosis, and was also correlated with the tumor immune status and infiltration of different immune cells in the tumor microenvironment. The current findings may provide important clinical indications for proposing a novel combination therapy consists of targeted anti-SLC therapy and immunotherapy for HCC patients.

Construction of a pyroptosis-related lncRNAs signature for predicting prognosis and immunotherapy response in glioma.

Recent studies have proved that pyroptosis-related long non-coding RNAs (PRlncRNAs) are closely linked to tumor progression, prognosis, and immunity. Here, we systematically evaluated the correlation of PRlncRNAs with glioma prognosis. This study included 3 glioma cohorts (The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Gravendeel). Through Pearson correlation analysis, PRlncRNAs were screened from these 3 cohorts. Univariate Cox regression analysis was then carried out to determine the prognostic PRlncRNAs. A pyroptosis-related lncRNAs signature (PRLS) was then built by least absolute shrinkage and selection operator and multivariate Cox analyses. We systematically evaluated the correlation of the PRLS with the prognosis, immune features, and tumor mutation burden in glioma. A total of 14 prognostic PRlncRNAs overlapped in all cohorts and were selected as candidate lncRNAs. Based on The Cancer Genome Atlas cohort, a PRLS containing 7 PRlncRNAs was built. In all cohorts, the PRLS was proved to be a good predictor of glioma prognosis, with a higher risk score related to a poorer prognosis. We observed obvious differences in the immune microenvironment, immune cell infiltration level, and immune checkpoint expression in low- and high-risk subgroups. Compared with low-risk cases, high-risk cases had lower Tumor Immune Dysfunction and Exclusion scores and greater tumor mutation burden, indicating that high-risk cases can be more sensitive to immunotherapy. A nomogram combining PRLS and clinical parameters was constructed, which showed more robust and accurate predictive power. In conclusion, the PRLS is a potentially useful indicator for predicting prognosis and response to immunotherapy in glioma. Our findings may provide a useful insight into clinically individualized treatment strategies for patients.

Combination Analysis of Ferroptosis and Immune Status Predicts Patients Survival in Breast Invasive Ductal Carcinoma

Ferroptosis is a new form of iron-dependent cell death and plays an important role during the occurrence and development of various tumors. Increasingly, evidence shows a convincing interaction between ferroptosis and tumor immunity, which affects cancer patients' prognoses. These two processes cooperatively regulate different developmental stages of tumors and could be considered important tumor therapeutic targets. However, reliable prognostic markers screened based on the combination of ferroptosis and tumor immune status have not been well characterized. Here, we chose the ssGSEA and ESTIMATE algorithms to evaluate the ferroptosis and immune status of a TCGA breast invasive ductal carcinoma (IDC) cohort, which revealed their correlation characteristics as well as patients' prognoses. The WGCNA algorithm was used to identify genes related to both ferroptosis and immunity. Univariate COX, LASSO regression, and multivariate Cox regression models were used to screen prognostic-related genes and construct prognostic risk models. Based on the ferroptosis and immune scores, the cohort was divided into three groups: a high-ferroptosis/low-immune group, a low-ferroptosis/high-immune group, and a mixed group. These three groups exhibited distinctive survival characteristics, as well as unique clinical phenotypes, immune characteristics, and activated signaling pathways. Among them, low-ferroptosis and high-immune statuses were favorable factors for the survival rates of patients. A total of 34 differentially expressed genes related to ferroptosis-immunity were identified among the three groups. After univariate, Lasso regression, and multivariate stepwise screening, two key prognostic genes (GNAI2, PSME1) were identified. Meanwhile, a risk prognosis model was constructed, which can predict the overall survival rate in the validation set. Lastly, we verified the importance of model genes in three independent GEO cohorts. In short, we constructed a prognostic model that assists in patient risk stratification based on ferroptosis-immune-related genes in IDC. This model helps assess patients' prognoses and guide individualized treatment, which also further eelucidatesthe molecular mechanisms of IDC.

Alternative splicing event associated with immunological features in bladder cancer.

Bladder cancer (BLCA) is the most prevalent urinary tumor with few treatments. Alternative splicing (AS) is closely related to tumor development and tumor immune microenvironment. However, the comprehensive analysis of AS and prognosis and immunological features in BLCA is still lacking. In this study, we downloaded RNA-Seq data and clinical information from The Cancer Genome Atlas (TCGA) database, and AS events were acquired from the TCGA Splice-seq. A total of eight prognostic AS events (C19orf57|47943|ES, ANK3|11845|AP, AK9|77203|AT, GRIK2|77096|AT, DYM|45472|ES, PTGER3|3415|AT, ACTG1|44120|RI, and TRMU|62711|AA) were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression analysis to construct a risk score model. The Kaplan-Meier analysis revealed that the high-risk group had a worse prognosis compared with the low-risk group. The area under the receiver operating characteristic (ROC) curves (AUCs) for this risk score model in 1, 3, and 5 years were 0.698, 0.742, and 0.772, respectively. One of the prognostic AS event-related genes, TRMU, was differentially expressed between tumor and normal tissues in BLCA. The single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithm showed that both the risk score model and TRMU were significantly associated with tumor immune microenvironment and immune status (immune cells, immune-related pathway, and immune checkpoint) in BLCA patients. The TIMER database confirmed the relationship between the expression of TRMU and immune cells and checkpoint genes. Furthermore, Cytoscape software 3.8.0 was used to construct the regulatory network between AS and splicing factors (SFs). Our study demonstrated that AS events were powerful biomarkers to predict the prognosis and immune status in BLCA, which may be potential therapeutic targets in BLCA.

Diets Differently Regulate Tumorigenesis in Young E0771 Syngeneic Breast Cancer Mouse Model.

Breast cancer (BC) is the most diagnosed cancer type, accounting for one in eight cancer diagnoses worldwide. Epidemiological studies have shown that obesity is associated with increased risk of BC in post-menopausal women, whereas adiposity reduces the risk of BC in premenopausal women. The mechanistic link between obesity and BC has been examined by combining murine BC models with high-fat diet (HFD) induced obesity. However, the effect of adiposity (not obesity) induced by a short period of HFD consumption on BC pathogenesis is not well understood. In the current study, we examined the effects of different diet compositions on BC pathogenesis using a young E0771 syngeneic BC mouse model fed on either an HFD or regular diet (RD: a low-fat high-carbohydrate diet) for a short period (4 weeks) before implanting mammary tumors in mice. We analyzed the effect of diet composition on the onset of tumor growth, metastasis, and metabolic and immune status in the tumor microenvironment (TME) using various methods including in vivo bioluminescence imaging and immunoblotting analyses. We showed for the first time that a short-term HFD delays the onset of tumorigenesis by altering the immune and metabolic signaling and energy mechanism in the TME. However, RD may increase the risk of tumorigenesis and metastasis by increasing pro-inflammatory factors in the TME in young mice. Our data suggest that diet composition, adipogenesis, and loss of body fat likely regulate the pathogenesis of BC in a manner that differs between young and post-menopausal subjects.

Glycolytic Genes Predict Immune Status and Prognosis Non‐Small‐Cell Lung Cancer Patients with Radiotherapy and Chemotherapy

Non-small-cell lung cancer (NSCLC) is a major health problem that endangers human health. The prognosis of radiotherapy or chemotherapy is still unsatisfactory. This study is aimed at investigating the predictive value of glycolysis-related genes (GRGs) on the prognosis of NSCLC patients with radiotherapy or chemotherapy. Download the clinical information and RNA data of NSCLC patients receiving radiotherapy or chemotherapy from TCGA and geo databases and obtain GRGs from MsigDB. The two clusters were identified by consistent cluster analysis, the potential mechanism was explored by KEGG and GO enrichment analyses, and the immune status was evaluated by estimate, TIMER, and quanTIseq algorithms. Lasso algorithm is used to build the corresponding prognostic risk model. Two clusters with different GRG expression were identified. The high-expression subgroup had poor overall survival. The results of KEGG and GO enrichment analyses suggest that the differential genes of the two clusters are mainly reflected in metabolic and immune-related pathways. The risk model constructed with GRGs can effectively predict the prognosis. The nomogram combined with the model and clinical characteristics has good clinical application potential. In this study, we found that GRGs are associated with tumor immune status and can assess the prognosis of NSCLC patients receiving radiotherapy or chemotherapy.

Mass Spectrometry-Based Tissue Imaging of the Tumor Microenvironment.

Multiplex ion beam imaging (MIBI) and imaging mass cytometry (IMC) enable highly multiplexed antibody (40+) staining of frozen or formalin fixed, paraffin-embedded (FFPE) human or murine tissues through detection of metal ions liberated from primary antibodies by time-of-flight mass spectrometry (TOF). These methods make detection of more than 50 targets theoretically possible while maintaining spatial orientation. As such, they are ideal tools to identify the multiple immune, epithelial, and stromal cell subsets in the tumor microenvironment and to characterize spatial relationships and tumor-immune status in either murine models or human samples. This chapter summarizes methods for antibody conjugation and validation, staining, and preliminary data collection using IMC or MIBI in both human and mouse pancreatic adenocarcinoma samples. These protocols are intended to facilitate use of these complex platforms in not only tissue-based tumor immunology studies but also tissue-based oncology or immunology studies more broadly.

Establishment and validation of an aging-related risk signature associated with prognosis and tumor immune microenvironment in breast cancer

BackgroundBreast cancer (BC) is a highly malignant and heterogeneous tumor which is currently the cancer with the highest incidence and seriously endangers the survival and prognosis of patients. Aging, as a research hotspot in recent years, is widely considered to be involved in the occurrence and development of a variety of tumors. However, the relationship between aging-related genes (ARGs) and BC has not yet been fully elucidated.Materials and methodsThe expression profiles and clinicopathological data were acquired in the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) database. Firstly, the differentially expressed ARGs in BC and normal breast tissues were investigated. Based on these differential genes, a risk model was constructed composed of 11 ARGs via univariate and multivariate Cox analysis. Subsequently, survival analysis, independent prognostic analysis, time-dependent receiver operating characteristic (ROC) analysis and nomogram were performed to assess its ability to sensitively and specifically predict the survival and prognosis of patients, which was also verified in the validation set. In addition, functional enrichment analysis and immune infiltration analysis were applied to reveal the relationship between the risk scores and tumor immune microenvironment, immune status and immunotherapy. Finally, multiple datasets and real‐time polymerase chain reaction (RT-PCR) were utilized to verify the expression level of the key genes.ResultsAn 11-gene signature (including FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57, CPLX2 and CCL19) was established to predict the survival of BC patients, which was validated by the GEO cohort. Based on the risk model, the BC patients were divided into high- and low-risk groups, and the high-risk patients showed worse survival. Stepwise ROC analysis and Cox analyses demonstrated the good performance and independence of the model. Moreover, a nomogram combined with the risk score and clinical parameters was built for prognostic prediction. Functional enrichment analysis revealed the robust relationship between the risk model with immune-related functions and pathways. Subsequent immune microenvironment analysis, immunotherapy, etc., indicated that the immune status of patients in the high-risk group decreased, and the anti-tumor immune function was impaired, which was significantly different with those in the low-risk group. Eventually, the expression level of FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57 and CCL19 was identified as down-regulated in tumor cell line, while CPLX2 up-regulated, which was mostly similar with the results in TCGA and Human Protein Atlas (HPA) via RT-PCR.ConclusionsIn summary, our study constructed a risk model composed of ARGs, which could be used as a solid model for predicting the survival and prognosis of BC patients. Moreover, this model also played an important role in tumor immunity, providing a new direction for patient immune status assessment and immunotherapy selection.

Development and validation of a cuproptosis-related lncRNA model correlated to the cancer-associated fibroblasts enable the prediction prognosis of patients with osteosarcoma

BackgroundOsteosarcoma is the most common primary pediatric and adolescent bone malignancy. An imbalance in copper homeostasis caused by copper ion accumulation could increase intracellular toxicity and regulate cancer cell growth. This study aimed to identify long non-coding RNAs (lncRNAs) associated with cuproptosis to predict prognosis and target drug use to improve patient survival. MethodsRNA sequencing and relevant clinical information of ninety-three osteosarcoma patients were obtained from the TARGET database. We then identified thirteen prognostic cuproptosis-related lncRNAs(CRLncs) using coexpression and univariate Cox analyses. The prognostic risk model with three CRLncs was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. Patients were divided into low-risk and high-risk subgroups using the median risk score. The tumor microenvironment (TME) and immune status of identified subgroups were analyzed using ESTIMATE, CIBERSORT, MCP-counter, xCELL, EPIC, and ssGSEA analyses. Functional analyses were conducted to elucidate the underlying mechanisms, including GO, KEGG, GSVA, and GSEA analyses. Also, the relationships between the model, tumor immunity, and drug sensitivity were explored. Lastly, the expression level of ZNF37BP, AL353759.1, and AC005034.5 was validated in vitro. ResultsWe constructed a model containing three CRLncs (ZNF37BP, AL353759.1, and AC005034.5) and validated its excellent prognostic and predictive power. The AUC curves for 1-year, 3-year, and 5-year survival probabilities were 0.76, 0.84, and 0.89, respectively. Patients in the high-risk group had a shorter overall survival (OS) time than those in the low-risk. The stroma score and cancer-associated fibroblasts (CAFs) were significantly higher in the low-risk group. Immune cells such as T cells CD4 naive, T cells gamma delta, NK cells resting, dendritic cells resting, and mast cells activated were significantly upregulated in the high-risk group. Based on functional analyses, the PI3K-Akt pathway was identified as a critical metabolic pathway in osteosarcoma. Additionally, we obtained three potentially effective drugs for OS: erlotinib, MP470, and WH-4-023 targeting the PI3K-Akt pathway. The expression level of ZNF37BP was significantly elevated in OS cell lines than in normal osteoblast hFOB1.19 cells, and that of ATP7A, LIPT1, AL353759.1, and AC005034.5 were decreased considerably in OS cell lines. ConclusionCuproptosis-related lncRNAs are correlated with the CAFs of osteosarcoma, and this could serve as a foundation for OS survival prediction and treatment.

Construction of a novel cuproptosis-related gene signature for predicting prognosis and estimating tumor immune microenvironment status in papillary thyroid carcinoma

BackgroundCuproptosis, a new form of programmed cell death, has been recently reported to be closely related to tumor progression. However, the significance of cuproptosis-related genes (CRGs) in papillary thyroid carcinoma (PTC) is still unclear. Therefore, this study aimed to investigate the role of the CRG signature in prognosis prediction and immunotherapeutic effect estimation in patients with PTC.MethodsRNA-seq data and the corresponding clinical information of patients with PTC were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Comprehensive analyses, namely, consensus clustering, immune analyses, functional enrichment, least absolute shrinkage and selection operator-multivariate Cox regression, and nomogram analysis, were performed to identify new molecular subgroups, determine the tumor immune microenvironment (TIME) status of the identified subgroups, and construct a clinical model. Independent verification cohort data and quantitative real-time polymerase chain reaction (qPCR) was performed to validate the expression of specific prognosis-related and differentially expressed CRGs (P-DECRGs).ResultsIn the TCGA database, 476 patients with PTC who had complete clinical and follow-up information were included. Among 135 CRGs, 21 were identified as P-DECRGs. Two molecular subgroups with significantly different disease-free survival and TIME statuses were identified based on these 21 P-DECRGs. The differentially expressed genes between the two subgroups were mainly associated with immune regulation. The risk model and nomogram were constructed based on four specific P-DECRGs and validated as accurate prognostic predictions and TIME status estimation for PTC by TCGA and GEO verification cohorts. Finally, the qPCR results of 20 PTC and paracancerous thyroid tissues validated those in the TCGA database.ConclusionsFour specific P-DECRGs in PTC were identified, and a clinical model based on them was established, which may be helpful for individualized immunotherapeutic strategies and prognostic prediction in patients with PTC.

Breast cancer cell-derived microRNA-155 suppresses tumor progression via enhancing immune cell recruitment and antitumor function.

Evidence suggests that increased microRNA-155 (miR-155) expression in immune cells enhances antitumor immune responses. However, given the reported association of miR-155 with tumorigenesis in various cancers, a debate is provoked on whether miR-155 is oncogenic or tumor suppressive. We aimed to interrogate the impact of tumor miR-155 expression, particularly that of cancer cell–derived miR-155, on antitumor immunity in breast cancer. We performed bioinformatic analysis of human breast cancer databases, murine experiments, and human specimen examination. We revealed that higher tumor miR-155 levels correlate with a favorable antitumor immune profile and better patient outcomes. Murine experiments demonstrated that miR-155 overexpression in breast cancer cells enhanced T cell influx, delayed tumor growth, and sensitized the tumors to immune checkpoint blockade (ICB) therapy. Mechanistically, miR-155 overexpression in breast cancer cells upregulated their CXCL9/10/11 production, which was mediated by SOCS1 inhibition and increased phosphorylated STAT1 (p-STAT1)/p-STAT3 ratios. We further found that serum miR-155 levels in breast cancer patients correlated with tumor miR-155 levels and tumor immune status. Our findings suggest that high serum and tumor miR-155 levels may be a favorable prognostic marker for breast cancer patients and that therapeutic elevation of miR-155 in breast tumors may improve the efficacy of ICB therapy via remodeling the antitumor immune landscape.

Identification of key snoRNAs serves as biomarkers for hepatocellular carcinoma by bioinformatics methods.

Background:Hepatocellular carcinoma (HCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. However, research on small nuclear RNAs (snoRNAs) in HCC were very little. This study aimed to identify a potential diagnostic and prognostic snoRNA signature for HCC.Methods:HCC datasets from the cancer genome atlas (TCGA) and international cancer genome consortium (ICGC) cohorts were used. Differentially expressed snoRNA (DEs) were identified using the limma package. Based on the DEs, diagnostic and prognostic models were established by the least absolute shrinkage and selection operator (LASSO) regression and COX analysis, and Kaplan–Meier (K–M) survival analysis and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the efficiency of signatures. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to analyze the risk score and further explore the potential correlation between the risk groups and tumor immune status in TCGA. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine the functions of key snoRNAs.Results:We constructed a 6-snoRNAs signature which could classify patients into high- or low-risk groups and found that patients in the high-risk group had a worse prognosis than those in the low-risk group and were significantly involved in p53 processes. Tumor immune status analysis revealed that CTLA4 and PDCD1 (PD1) were highly expressed in the high-risk group, which responded to PD1 inhibitor therapy. Additionally, a 25-snoRNAs diagnostic signature was constructed with an area under the curve (AUC) of 0.933 for distinguishing HCCs from normal controls. Finally, 3 key snoRNAs (SNORA11, SNORD124, and SNORD46) were identified with both diagnostic and prognostic efficacy, some of which were closely related to the spliceosome and Notch signaling pathways.Conclusions:Our study identified 6 snoRNAs that may serve as novel prognostic models and 3 key snoRNAs with both diagnostic and prognostic efficacy for HCC.

A novel neutrophil extracellular trap signature to predict prognosis and immunotherapy response in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant cancers, and patients with HNSCC possess early metastases and poor prognosis. Systematic therapies (including chemotherapy, targeted therapy, and immunotherapy) are generally applied in the advanced/late stages of HNSCC, but primary and acquired resistance eventually occurs. At present, reliable biomarkers to predict the prognosis of HNSCC have not been completely identified. Recent studies have shown that neutrophil extracellular traps (NETs) are implicated in cancer progression, metastasis and cancer immune response, and NET-related gene signatures are associated with the prognosis of patients with several human cancers. To explore whether NET-related genes play crucial roles in HNSCC, we have performed systematic analysis and reported several findings in the current study. Firstly, we identified seven novel NET-related genes and developed a NET-score signature, which was highly associated with the clinicopathological and immune traits of the HNSCC patients. Then, we, for the first time, found that NIFK was significantly upregulated in HNSCC patient samples, and its levels were significantly linked to tumor malignancy and immune status. Moreover, functional experiments confirmed that NIFK was required for HNSCC cell proliferation and metastasis. Altogether, this study has identified a novel NET-score signature based on seven novel NET-related genes to predict the prognosis of HNSCC and NIFK has also explored a new method for personalized chemo-/immuno-therapy of HNSCC.

DNA methylation regulator-mediated modification patterns and tumor microenvironment characterization in glioma.

Growing evidences indicate DNA methylation plays a crucial regulatory role in inflammation, innate immunity, and immunotherapy. However, the overall landscape of various DNA methylation regulatory genes and their relationship with the infiltration of immune cells into the tumor microenvironment (TME) as well as the response to immunotherapy in gliomas is still not clear. Therefore, we comprehensively analyzed the correlation between DNA methylation regulator patterns, infiltration of immune cell-types, and tumor immune response status in gather glioma cohorts. Furthermore, we calculated the DNA methylation score (DMS) for individual glioma samples, then evaluated the relationship between DMS, clinicopathological characteristics, and overall survival (OS) in patients with gliomas. Our results showed three distinct DNA methylation regulator patterns among the glioma patients which correlated with three distinct tumor immune response phenotypes, namely, immune-inflamed, immune-excluded, and immune desert. We then calculated DMS for individual glioma samples based on the expression of DNA methylation-related gene clusters. Furthermore, DMS, tumor mutation burden (TMB), programmed death 1 (PD-1) expression, immune cell infiltration status in the TME, and Tumor Immune Dysfunction and Exclusion (TIDE) scores were associated with survival outcomes and clinical responses to immune checkpoint blockade therapy. We also validated the predictive value of DMS in two independent immunotherapy cohorts. In conclusion, our results demonstrated that three DNA methylation regulator patterns that correlated with three tumor immune response phenotypes. Moreover, we demonstrated that DMS was an independent predictive biomarker that correlated with survival outcomes of glioma patients and their responses to immunotherapy therapeutic regimens.

Discovery of Lipid Metabolism-Related Genes for Predicting Tumor Immune Microenvironment Status and Prognosis in Prostate Cancer.

Background Reprogramming of lipid metabolism is closely associated with tumor development, serving as a common and critical metabolic feature that emerges during tumor evolution. Meanwhile, immune cells in the tumor microenvironment also undergo aberrant lipid metabolism, and altered lipid metabolism also has an impact on the function and status of immune cells, further promoting malignant biological behavior. Consequently, we focused on lipid metabolism-related genes for constructing a novel prognostic marker and evaluating immune status in prostate cancer. Methods Information about prostate cancer patients was obtained from TCGA and GEO databases. The NMF algorithm was conducted to identify the molecular subtypes. The least absolute shrinkage and selection operator (Lasso) regression analysis was applied to establish a prognostic risk signature. CIBERSORT algorithm was used to calculate immune cell infiltration levels in prostate cancer. External clinical validation data were used to validate the results. Results Prostate cancer samples were divided into two subtypes according to the NMF algorithm. A six-gene risk signature (PTGS2, SGPP2, ALB, PLA2G2A, SRD5A2, and SLC2A4) was independent of prognosis and showed good stability. There were significant differences between risk groups of patients with respect to the infiltration of immune cells and clinical variables. Response to immunotherapy also differed between different risk groups. Furthermore, the mRNA expression levels of the signature genes were verified in tissue samples by qRT-PCR. Conclusion We constructed a six-gene signature with lipid metabolism in prostate cancer to effectively predict prognosis and reflect immune microenvironment status.

A somatic mutation-derived LncRNA signatures of genomic instability predicts the prognosis and tumor microenvironment immune characters in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is an aggressive carcinoma with genome instability. Long non-coding RNAs (LncRNAs) have been functionally associated with genomic instability in cancers. However, the identification and prognostic value of lncRNAs related to genome instability have not been explored in hepatocellular carcinoma. In this study, we aim to identify a genomic instability-related lncRNA signature for predicting prognosis and theefficacyofimmunotherapy in HCC patients. According to the somatic mutation and transcript data of 364 patients with HCC, we determined differentially expressed genome instability-related lncRNAs (GInLncRNAs). Gene ontology (GO) enrichment analyses and Kyoto Encyclopedia of genes and genomes enrichment analyses revealed the potential functions of genes co-expressed with those lncRNAs involved in cancer development and immune function. We further determined a genome instability-related lncRNA signature (GInLncSig) through Cox regression analysis and LASSO regression analysis. Thereafter, we performed correlation analyses with mutations, clinical stratification analyses, andsurvival analyses to evaluate GInLncSig predictive function. Subsequently, we construct a nomogram model for prognostic assessments of patients with HCC. Finally, we performed Immunocytes infiltration analysis, gene set enrichment analysis (ssGSEA) of immunity circle-associated pathways, and T cell-inflamed score to explore GInLncSig's potential value in guiding immunotherapy. We identified 11 independent prognosis-associated GInLncRNAs (AC002511.2, LINC00501, LINC02055, LINC02714, LINC01508, LOC105371967, RP11_96A15.1, RP11_305F18.1, RP11_342M1.3, RP11_432J24.3, U95743.1) to construct a GInLncSig. According to the risk score calculated by GInLncSig, the high-risk group was characterized by a higher somatic mutation count, significantly poorer clinical prognosis, higher T cell-inflamed score, and specific tumor immune infiltration status compared to the low-risk group. Furthermore, we constructed a nomogram model to improve the reliability and clinical utility of predicting the prognosis of patients with HCC. Our study established a reliable prognostic prediction signature that could be a tool for prognosis prediction and a promising predictive biomarker of immunotherapy in hepatocellular carcinoma.