Low Tumor Mutation Burden Group Research Articles

A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma.

Identifying precise biomarkers of immunotherapy response for non-small cell lung carcinoma (NSCLC) before treatment is challenging. This study aimed to construct and investigate the potential performance of a sub-regional radiomics model (SRRM) as a novel tumor biomarker in predicting the response of patients with NSCLC treated with immune checkpoint inhibitors, and test whether its predictive performance is superior to that of conventional radiomics, tumor mutational burden (TMB) score and programmed death ligand-1 (PD-L1) expression. We categorized 264 patients from retrospective databases of two centers into training (n = 159) and validation (n = 105) cohorts. Radiomic features were extracted from three sub-regions of the tumor region of interest using the K-means method. We extracted 1,896 features from each sub-region, resulting in 5688 features per sample. The least absolute shrinkage and selection operator regression method was used to select sub-regional radiomic features. The SRRM was constructed and validated using the support vector machine algorithm. We used next-generation sequencing to classify patients from the two cohorts into high TMB (≥ 10 muts/Mb) and low TMB (< 10 muts/Mb) groups; immunohistochemistry was performed to assess PD-L1 expression in formalin-fixed, paraffin-embedded tumor sections, with high expression defined as ≥ 50% of tumor cells being positive. Associations between the SRRM and progression-free survival (PFS) and variant genes were assessed. Eleven sub-regional radiomic features were employed to develop the SRRM. The areas under the receiver operating characteristic curve (AUCs) of the proposed SRRM were 0.90 (95% confidence interval [CI] 0.84-0.96) and 0.86 (95% CI 0.76-0.95) in the training and validation cohorts, respectively. The SRRM (low vs. high; cutoff value = 0.936) was significantly associated with PFS in the training (hazard ratio [HR] = 0.35 [0.24-0.50], P < 0.001) and validation (HR = 0.42 [0.26-0.67], P = 0.001) cohorts. A significant correlation between the SRRM and three variant genes (H3C4, PAX5, and EGFR) was observed. In the validation cohort, the SRRM demonstrated a higher AUC (0.86, P < 0.001) than that for PD-L1 expression (0.66, P = 0.034) and TMB score (0.54, P = 0.552). The SRRM had better predictive performance and was superior to conventional radiomics, PD-L1 expression, and TMB score. The SRRM effectively stratified the progression-free survival (PFS) risk among patients with NSCLC receiving immunotherapy.

Exploration of Key Genes Combining with Immune Infiltration Level and Tumor Mutational Burden in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a lethal malignancy due to its heterogeneity and aggressive behavior. Recently, somatic mutations and tumor cell interactions with the surrounding tumor immune microenvironment (TIME) have been reported to participate in HCC carcinogenesis and predict HCC progression. In this study, we aimed to investigate the association between tumor mutational burden (TMB) and TIME in HCC. Additionally, we sought to identify differentially expressed genes (DEGs) associated with HCC prognosis and progression. The expression, clinical, and mutational data were downloaded from the cancer genome atlas (TCGA) database. The immune infiltration levels and TMB levels of the HCC samples were estimated and the samples were divided into immune cluster (ICR)-1 and 2 based on immune infiltration score and high and low TMB groups based on TMB score. Thereafter, differential gene expression analysis was conducted to identify the DEGs in the ICR1/2 and high/low TMB groups, and the intersecting DEGs were selected. Thereafter, Cox regression analysis was performed on 89 significant DEGs, among which 19 were associated with prognosis. These 19 DEGs were then used to construct a prognostic model based on their expression levels and regression coefficients. Thereafter, we analyzed the DEGs in mutant and wildtype TP53 HCC samples and identified high BCL10 and TRAF3 expression in the mutant TP53 samples. BCL10 and TRAF3 expression was detected by real-time quantitative reverse transcription PCR and immunohistochemistry, and their clinical correlation, biological function, and immune infiltration levels were analyzed by chi-square analyses, Gene Set Enrichment Analysis (GSEA), and “ssGSEA”, respectively. The results of our study revealed that immune infiltration level was correlated with TMB and that they synergistically predicted poor prognosis of HCC patients. DEGs enriched in immune-related pathways could serve as indicators of immunotherapy response in HCC. Among these DEGs, BCL10 and TRAF3 were highly expressed in HCC tissues, especially in the mutant TP53 group, and they co-operatively exhibited immunological function, thereby affecting HCC progression and prognosis. In this study, we identified BCL10 and TRAF3 as potential prognostic indicators in HCC patients. Additionally, we found that BCL10 and TRAF3 influence TMB and TIME in HCC patients and can be used for the development of immune-based therapies for improving the long-term survival of HCC patients.

Perspectives for next generation sequencing in patients with follicular lymphoma

Aim. To study the prognostic significance of gene mutations and intracellular signaling pathways involved in lymphomagenesis in patients with follicular lymphoma using next generation sequencing (NGS).Materials and methods. The prospective study included 26 patients with a median age of 51.5 years. Mutational screening was performed for cohort using custom NGS Panel of 118 genes. Gene set enrichment analysis (GSEA) was performed using Metascape. The data was analyzed in SPSS Statistics 26 and R 4.2.2.Results. The highest mutation frequency was noted in the genes: KMT2C – 50 %, KMT2D – 50 %, CREBBP – 31 %, NOTCH2 – 31 %, GNAS – 23 %. Missense mutations occurred with a frequency of 84.3 %. ARID1A gene mutation is an unfavorable prognostic factor according to progressive-free (p = 0.014) and event-free (p = 0.029) survival analysis. Tumor mutation burden (TMB) was defined as the number of mutations per megabase (Mb) of the coding sequence, the median TMB was 5.0 (3.3–8.3) mutations/Mb. The TMB threshold of 6 mutations/Mb divided patients into groups with high (44 %) and low (56 %) TMB. In the high TMB group, 2-year event-free survival was 27.3 % (95 % confidence interval 6.0–61.0), which was significantly lower than in low TMB group – 72.7 % (95 % confidence interval 41.9–91.6; p = 0.037). The most enriched cellular pathways according to GSEA results were regulation of cell activation (–log10(q-value) = 6.357), chromatin remodeling (–log10(q-value) = 5.707), histone modification (–log10(q-value) = 4.569). We have also demonstrated other possibilities of GSEA using follicular lymphoma as an example.Conclusion. TMB is a significant prognostic factor in patients with follicular lymphoma. We have shown that mutations in the MYC, CREBBP, EZH2, KMT2D genes lead to dysregulation in several intracellular processes, mediating complex molecular changes. The most enriched intracellular pathways in follicular lymphoma are those of chromatin remodeling, regulation of cell activation and histone modification.

Predictive value of tumor mutational burden for PD-1/PD-L1 inhibitors in NSCLC: A meta-analysis.

To investigate the association between tumor mutational burden (TMB) and the therapeutic effect of Programmed Death 1/Programmed Death Ligand 1 inhibitors in non-small cell lung cancer. Four electronic databases, PubMed, Embase, Web of Science, and Cochrane Library, were searched on May 10, 2023, and no time limitation was applied. Analyses were performed using STATA17.0. We assessed the methodological quality of each randomized controlled trial using the Newcastle-Ottawa scale. After exhaustive database search and rigorous screening, 10 studies were included in the meta-analysis. Our findings indicate that high TMB significantly improves progression-free survival but reduces overall response rate. The overall survival was not significantly different between the high and low TMB groups. No significant publication bias was observed. High TMB serves as a potential predictive biomarker for improved progression-free survival and reduced overall response rate in patients with non-small cell lung cancer treated with programmed death 1/programmed death ligand 1 inhibitors. However, its predictive value in overall survival requires further investigation.

Screening and validation of potential markers associated with uterine corpus endometrial carcinoma and polycystic ovary syndrome based on bioinformatics methods

Background: Endometrial cancer (UCEC) is a commonly occurring tumor in females, and polycystic ovary syndrome (PCOS) is closely related to UCEC, but the molecular mechanisms remain unclear. This article aims to explore potential molecular mechanisms in UCEC and PCOS, as well as identify prognostic genes for UCEC.Methods: Bioinformatics methods were employed to screen for DEGs in UCEC and PCOS. The shared DEGs were analyzed by constructing a protein-protein interaction (PPI) network using the String database and Cytoscape software. The enrichment analysis was performed using Metascape. The shared DEGs associated with the prognosis of UCEC were identified through univariate and lasso Cox regression methods. A multivariate Cox regression model was constructed and internally validated. The expression and test efficiency of the key prognostic genes were verified using external datasets for UCEC and PCOS. Furthermore, the Gepia database was utilized to analyze the expression of key prognostic genes and their correlation with the disease-free survival (RFS) of UCEC. Tumor mutation burden (TMB), immune infiltration, and the correlation of immune cells were assessed for the prognostic genes of UCEC.Results: There were 151 shared DEGs identified between UCEC and PCOS through bioinformatics screening. These shared DEGs were primarily enriched in leukocyte activation. Following model construction and verification, nine genes were determined to be prognostic for UCEC from the shared DEGs. Among them, TSPYL5, KCNJ15, RTN1, HMOX1, DCAF12L1, VNN2, and ANXA1 were confirmed as prognostic genes in UCEC through external validation. Additionally, RTN1 was identified as a key gene in both UCEC and PCOS. Gepia analysis revealed that higher expression of RTN1 was associated with RFS in UCEC. Immune infiltration analysis of the shared DEGs demonstrated significant differences in the expression of various immune cells between UCEC high and low TMB groups. The seven key prognostic genes in UCEC exhibited regulatory relationships with immune cells.Conclusion: This study identified TSPYL5, KCNJ15, RTN1, HMOX1, DCAF12L1, VNN2, and ANXA1 as the key prognostic DEGs of UCEC. These genes are associated with UCEC survival, TMB, immune cell infiltration, and immune cell regulation. Among them, RTN1 may serve as a potential biomarker for both UCEC and PCOS.

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.

The prognostic role of tumor mutation burden on survival of breast cancer: a systematic review and meta-analysis

BackgroundAs a potential genetic biomarker, tumor mutation burden (TMB) has made progress in numerous tumors. There are limited data regarding TMB and its prognostic role is controversial in breast cancer. This systematic review and meta-analysis were conducted to assess the prognostic value of TMB on survival of breast cancer.MethodsThe databases PubMed, Embase, Web of Science, and Cochrane Library were searched for articles published through May 31, 2022. Moreover, effective data were extracted from included studies and calculated pooled effects of hazard ratio (HR) for overall survival (OS) and progression-free survival (PFS) by STATA 16.0. Heterogeneity was conducted by the I2 statistic and p-value. Using publication bias evaluation, sensitivity analysis, and subgroup analysis, the origin of heterogeneity was further investigated.ResultsThey were up to 1,722 patients collected from sixteen cohorts for this analysis. The pooled effects of HR for both OS (HR: 1.14, 95% CI: 0.83,1.58, p > 0.01) and PFS (HR: 0.96, 95% CI: 0.53,1.71, p > 0.01) indicated no statistically significant difference in the high TMB and low TMB group. In immune checkpoint inhibitors (ICIs) subgroup, high TMB patients demonstrated benefit of OS (HR: 0.72, 95% CI: 0.59,0.87, p = 0.001) and PFS (HR: 0.52, 95% CI: 0.35,0.77, p < 0.001), whereas difference was not statistically significant in the non-ICIs subgroup (OS, HR:1.76, 95% CI: 0.97,3.20, p = 0.062; PFS, HR:2.31, 95% CI: 0.89,5.97, p = 0.086). In addition, sensitivity analysis revealed that the pooled effects were stable. The funnel plot and Begg's test suggested the absence of publication bias.ConclusionMeta-analysis revealed that the prognostic relevance of TMB in breast cancer is limited in scope. High TMB may be associated with longer survival only in ICIs-based treatment, but the association is not evident in non-ICIs-based treatment.Trial registration[https://www.crd.york.ac.uk/PROSPERO], Prospective Register of Systematic Reviews (PROSPERO), identifier: CRD42022342488.

Cuproptosis-related long non-coding RNAs model that effectively predicts prognosis in hepatocellular carcinoma.

BACKGROUNDCuproptosis has recently been considered a novel form of programmed cell death. To date, long-chain non-coding RNAs (lncRNAs) crucial to the regulation of this process remain unelucidated.AIMTo identify lncRNAs linked to cuproptosis in order to estimate patients' prognoses for hepatocellular carcinoma (HCC).METHODSUsing RNA sequence data from The Cancer Genome Atlas Live Hepatocellular Carcinoma (TCGA-LIHC), a co-expression network of cuproptosis-related genes and lncRNAs was constructed. For HCC prognosis, we developed a cuproptosis-related lncRNA signature (CupRLSig) using univariate Cox, lasso, and multivariate Cox regression analyses. Kaplan-Meier analysis was used to compare overall survival among high- and low-risk groups stratified by median CupRLSig risk score. Furthermore, comparisons of functional annotation, immune infiltration, somatic mutation, tumor mutation burden (TMB), and pharmacologic options were made between high- and low-risk groups.RESULTSThree hundred and forty-three patients with complete follow-up data were recruited in the analysis. Pearson correlation analysis identified 157 cuproptosis-related lncRNAs related to 14 cuproptosis genes. Next, we divided the TCGA-LIHC sample into a training set and a validation set. In univariate Cox regression analysis, 27 LncRNAs with prognostic value were identified in the training set. After lasso regression, the multivariate Cox regression model determined the identified risk equation as follows: Risk score = (0.2659 × PICSAR expression) + (0.4374 × FOXD2-AS1 expression) + (-0.3467 × AP001065.1 expression). The CupRLSig high-risk group was associated with poor overall survival (hazard ratio = 1.162, 95%CI = 1.063-1.270; P < 0.001) after the patients were divided into two groups depending upon their median risk score. Model accuracy was further supported by receiver operating characteristic and principal component analysis as well as the validation set. The area under the curve of 0.741 was found to be a better predictor of HCC prognosis as compared to other clinicopathological variables. Mutation analysis revealed that high-risk combinations with high TMB carried worse prognoses (median survival of 30 mo vs 102 mo of low-risk combinations with low TMB group). The low-risk group had more activated natural killer cells (NK cells, P = 0.032 by Wilcoxon rank sum test) and fewer regulatory T cells (Tregs, P = 0.021) infiltration than the high-risk group. This finding could explain why the low-risk group has a better prognosis. Interestingly, when checkpoint gene expression (CD276, CTLA-4, and PDCD-1) and tumor immune dysfunction and rejection (TIDE) scores are considered, high-risk patients may respond better to immunotherapy. Finally, most drugs commonly used in preclinical and clinical systemic therapy for HCC, such as 5-fluorouracil, gemcitabine, paclitaxel, imatinib, sunitinib, rapamycin, and XL-184 (cabozantinib), were found to be more efficacious in the low-risk group; erlotinib, an exception, was more efficacious in the high-risk group.CONCLUSIONThe lncRNA signature, CupRLSig, constructed in this study is valuable in prognostic estimation of HCC. Importantly, CupRLSig also predicts the level of immune infiltration and potential efficacy of tumor immunotherapy.

Comparative Analysis of Mutation Status and Immune Landscape for Squamous Cell Carcinomas at Different Anatomical sites.

ObjectiveIt has been controversial whether tumor mutation burden (TMB) affects the prognosis and the efficacy of immunotherapy in different tumor types. We provided a comprehensive analysis of mutation status and immune landscape of squamous cell carcinomas (SCCs) from four sites in order to investigate the relationship of TMB with prognosis and immune cell infiltration in different SCCs.MethodsThe transcriptome profiles and somatic mutation data of SCCs downloaded from the Cancer Genome Atlas (the Cancer Genome Atlas) database were analyzed and visualized. Then, TMB was calculated to analyze its correlations with prognosis and clinical features. Differentially expressed genes (DEGs) between the high and low TMB groups were screened for functional enrichment analysis. CIBERSORT algorithm was used to compare differences of immune cell infiltration between two groups in different SCCs. In addition, immune DEGs associated with prognosis were identified and risk prediction model was constructed via Cox regression analysis.ResultsMissense mutation was the most dominant mutation type in SCCs. The difference was that the top10 mutated genes varied widely among different SCCs. High TMB group had better prognosis in lung squamous cell carcinoma (LUSC) and cervical squamous cell carcinoma (CESC), while the result was reverse in head and neck squamous cell carcinoma (HNSCC) and esophageal squamous cell carcinoma (ESCC). In addition, patients with older age, smoking history, earlier pathological stage and no lymphatic invasion had higher TMB. The identified DEGs were mainly enriched in the regulation of immune system, muscular system and the activity of epidermal cells. The proportions of CD8+T cells, CD4+ memory T cells, follicular helper T cells, macrophages were distinct between two groups. The prognosis-related hub genes (CHGB, INHBA, LCN1 and VEGFC) screened were associated with poor prognosis.ConclusionThis study reveals the mutation status and immune cell infiltration of SCCs at different anatomical sites. TMB is closely related to the prognosis of SCCs, and its effects on prognosis are diverse in different SCCs, which might result from the situation of immune cell infiltration. These findings contribute to the exploration of biomarkers for predicting the efficacy of immunotherapy in SCCs and providing innovative insights for accurate application of immunotherapy.

Tumor mutational burden (TMB) in immune checkpoint inhibitor (ICI)-naïve and -experienced patients with metastatic melanoma treated with lifileucel, a tumor-infiltrating lymphocyte (TIL) cell therapy.

9524 Background: Cutaneous melanoma is characterized by high TMB, which is associated with increased tumor-specific neoantigen expression (Schumacher Science 2015) and an increased response rate to ICI (Yarchoan NEJM 2019). The TMB in tumors that recur/progress after ICI is not well defined. Lifileucel is a one-time, autologous TIL cell therapy under investigation for treatment of patients (pts) with advanced melanoma. We conducted a matched case-control study of prospectively enrolled pts with advanced melanoma treated with lifileucel in the ICI-naïve (IOV-COM-202 trial, Cohort 1A [C1A]) and post-ICI (C-144-01 trial, Cohort 2 [C2]) setting to investigate the potential association between prior ICI therapy, TMB, and response to lifileucel. Methods: All pts had unresectable or metastatic melanoma. Available cases from C1A (ICI-naïve pts receiving lifileucel + pembrolizumab [pembro]) were matched to controls from C2 (pts receiving lifileucel alone after progression on anti–PD-1/PD-L1 therapy); 3 controls per case were matched at least for BRAF status and disease stage at study entry, and if possible, for anatomic site of tumor harvest. Lifileucel regimen was similar in C1A and C2. In C1A, 1 dose of pembro was given after tumor harvest and before nonmyeloablative lymphodepletion and resumed after lifileucel per standard treatment, for up to 2 y. Objective response rate (ORR) was assessed by investigators per RECIST v1.1. TMB of the resected tumor was measured using the ImmunoID NeXT (C1A) or PGDx elio (C2) platform; a validated conversion factor was used to compare TMB between platforms (Vega Ann Oncol 2021). High TMB was defined as ≥10 mut/MB. Results: Seven pts in C1A and 21 in C2 were included in the case-control study and had ORR of 71.4% and 38.1%, respectively. The percentage of pts with high TMB was 57.1% in C1A and 19.0% in C2 ( P = 0.1). ORR in the low and high TMB groups was 66.7% and 75.0%, respectively, in C1A and 41.1% and 25.0% in C2; 60% of responders in C1A and 12.5% in C2 had high TMB. In logistic regression analysis adjusted for cohort, TMB was not associated with response to lifileucel (odds ratio, 1.0; 95% CI, 0.9–1.1; P = 0.8). Data on tumor mutations and neoantigens, T-cell receptor repertoire, and tumor microenvironment profile will be presented. Conclusions: Our preliminary data indicate that the efficacy (ORR) of lifileucel may be independent of TMB, regardless of treatment setting, consistent with its proposed immune checkpoint pathway-independent mechanism of action. The percentage of patients with high TMB tended to be lower in tumors with prior ICI exposure than in those that were ICI-naïve. Clinical trial information: NCT03645928; NCT02360579.

Predicting Differences in Treatment Response and Survival Time of Lung Adenocarcinoma Patients Based on a Prognostic Risk Model of Glycolysis-Related Genes

Background: Multiple factors influence the survival of patients with lung adenocarcinoma (LUAD). Specifically, the therapeutic outcomes of treatments and the probability of recurrence of the disease differ among patients with the same stage of LUAD. Therefore, effective prognostic predictors need to be identified. Methods: Based on the tumor mutation burden (TMB) data obtained from The Cancer Genome Atlas (TCGA) database, LUAD patients were divided into high and low TMB groups, and differentially expressed glycolysis-related genes between the two groups were screened. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to obtain a prognostic model. A receiver operating characteristic (ROC) curve and a calibration curve were generated to evaluate the nomogram that was constructed based on clinicopathological characteristics and the risk score. Two data sets (GSE68465 and GSE11969) from the Gene Expression Omnibus (GEO) were used to verify the prognostic performance of the gene. Furthermore, differences in immune cell distribution, immune-related molecules, and drug susceptibility were assessed for their relationship with the risk score. Results: We constructed a 5-gene signature (FKBP4, HMMR, B4GALT1, SLC2A1, STC1) capable of dividing patients into two risk groups. There was a significant difference in overall survival (OS) times between the high-risk group and the low-risk group (p < 0.001), with the low-risk group having a better survival outcome. Through multivariate Cox analysis, the risk score was confirmed to be an independent prognostic factor (HR = 2.709, 95% CI = 1.981–3.705, p < 0.001), and the ROC curve and nomogram exhibited accurate prediction performance. Validation of the data obtained in the GEO database yielded similar results. Furthermore, there were significant differences in sensitivity to immunotherapy, cisplatin, paclitaxel, gemcitabine, docetaxel, gefitinib, and erlotinib between the low-risk and high-risk groups. Conclusion: Our results reveal that glycolysis-related genes are feasible predictors of survival and the treatment response of patients with LUAD.

Development of Tumor Mutation Burden-Related Prognostic Model and Novel Biomarker Identification in Stomach Adenocarcinoma.

Background: Stomach adenocarcinoma (STAD) is one of the most common tumors. Tumor mutation burden (TMB) has been linked to immunotherapy response. We wanted to see if there was any link between TMB and cancer prognosis. Methods: The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to obtain mutation data, gene expression profiles, and clinical data. We looked at the differences in gene expression and immune markers between low and high TMB groups, built an immune prognostic model, and created a dynamic nomograph App that may be used in the clinic. Simultaneously, We ran the immunotherapy prediction and model comparison at the same time. Finally, model gene mutation and copy number variation (CNV) were displayed. The cellular functional experiments were used to investigate the potential role of GLP2R in gastric cancer. Results: Firstly, basic mutation information and differences in immune infiltration in STAD are revealed. Secondly, the prognostic model developed by us has good accuracy, and the corresponding dynamic nomograph Apps online and immunotherapy prediction facilitate clinical transformation. Furthermore, GLP2R knockdown significantly inhibited the proliferation, migration of gastric cancer cells in vitro. Conclusion: Our findings imply that TMB plays a significant role in the prognosis of STAD patients from a biological perspective. GLP2R may serve as a potential target for gastric cancer.

Identification and validation of a tumor mutation burden-related signature combined with immune microenvironment infiltration in adrenocortical carcinoma.

Tumor mutation burden (TMB), an emerging molecular determinant, is accompanied by microsatellite instability and immune infiltrates in various malignancies. However, whether TMB is related to the prognosis or immune responsiveness of adrenocortical carcinoma (ACC) remains to be elucidated. This paper aims to investigate the impact of TMB on the prognosis and immune microenvironment infiltration in ACC. The somatic mutation data, gene expression profile, and corresponding clinicopathological information were retrieved from TCGA. The mutation landscape was summarized and visualized with the waterfall diagram. The ACC patients were divided into low and high TMB groups based on the median TMB value and differentially expressed genes (DEGs) between the two groups were identified. Diverse functional analyses were conducted to determine the functionality of the DEGs. The immune cell infiltration signatures were evaluated based on multiple algorithms. Eventually, a TMB Prognostic Signature (TMBPS) was established and its predictive accuracy for ACC was evaluated. Single nucleotide polymorphism and C > T were found to be more common than other missense mutations. In addition, lower TMB levels indicated improved survival outcomes and were correlated with younger age and earlier clinical stage. Functional analysis suggested that DEGs were primarily related to the cell cycle, DNA replication, and cancer progression. Additionally, significant differences in infiltration levels of activated CD4+ T cells, naive B cells, and activated NK cells were observed in two TMB groups. We also found that patients with higher TMBPS showed worse survival outcomes, which was validated in the Gene Expression Omnibus database. Our study systematically analyzed the mutation and identified a TMBPS combined with immune microenvironment infiltration in ACC. It is expected that this paper can promote the development of ACC treatment strategies.

Identifying GNG4 might play an important role in colorectal cancer TMB.

Colorectal carcinoma (CRC) is one of the most leading cause of cancer death all over the world. The tumor immune microenvironment is illustrated to be necessary for the progress of CRC. And the accumulating evidence indicated that tumor mutation burden (TMB) is effective in differentiating responding population of immune checkpoint inhibitor (ICI) therapies in various cancers. In this study, we aimed to evaluated the potential relationship between TMB and the recurrence risk of CRC. The transcriptomic and clinical data of CRC patients were collected from The Cancer Genome Atlas (TCGA) database (n= 382). Then the genomic analysis of tumor mutation burden and tumor purity were conducted by a computational method based on transcriptomic data. Firstly, we accessed the distribution of TMB and preferences at the gene and mutation level using somatic mutation data from TCGA data about CRC. We identified that high TMB predicted better prognosis of CRC patients. Secondly, the differentially expressed genes (DEGs) between the low TMB and high TMB group was clarified. Then the protein-protein interaction (PPI) analysis was performed, and the results confirmed ten hub genes among the DEGs. Utilizing the GEPIA web-tool, we discovered that GNG4 was up-regulated in tumor tissues, and GNG4 was related to the overall survival (OS) and tumor free survival (TFS) of CRC patients. Therefore, we considered GNG4 was essential for the tumor immune microenvironment of CRC. Furthermore, we also accessed the protein level of GNG4 in CRC and liver metastases from CRC. In this study, GNG4 was demonstrated to be the key element of the CRC TMB, which will be essential for the ICI therapy of CRC. Besides, GNG4 was up-regulated in CRC and liver metastases from CRC tissues. Thus, we thought that GNG4 might play an important role in colorectal cancer TMB and induce its metastasis in liver.

Identification of Three Core Secretome Genes Associated with Immune Infiltration in High Tumor Mutation Burden Across 14 Major Solid Tumors.

BackgroundSecretome genes, encoding proteins secreted from the cell, are involved in the tumor immune response and correlated with levels of tumor mutation burden (TMB) in multiple tumors. This study aimed to identify core secretome genes and their potential association with immunomodulators and immune infiltration in high TMB groups across 14 major solid tumors through bioinformatics analysis.MethodsMulti-omics data for 14 major solid tumors were downloaded from The Cancer Genome Atlas (TCGA) database. Patients were divided into high TMB (TMB-high) and low TMB (TMB-low) groups using the median TMB values for each of the solid tumors. The CIBERSORT algorithm was conducted to estimate the proportion of 22 tumor-infiltrating immune cells (TIICs). Kaplan–Meier analysis and the log-rank test were utilized to screened prognosis-related genes. The correlations between core secretome genes and TIICs were analyzed using Spearman correlation coefficients.ResultsIn TMB-high groups, multi-omics data analysis revealed that secretome genes were strongly associated with clinical characteristics, and 65 prognosis-related secretome genes were screened. Among the prognosis-related genes, 21 core secretome genes were identified, and strongly associated with five types of TIICs, namely activated NK cells, follicular helper T cells, CD8 T cells, and macrophages M0 and M2. Notably, three secretome genes (ADAMTS12, COL12A1, and COL5A2) were significantly related to immunomodulators and TIICs in multiple solid tumors. In addition, 12 core secretome genes were significantly differentially expressed between responding and non-responding patients receiving immunotherapy. Furthermore, core secretome genes may be involved in the PI3K/AKT signaling pathway.ConclusionWe examined the prognostic significance of secretome genes and their potential association with immunomodulators and immune infiltration across 14 major solid tumors. In summary, three secretome genes (ADAMTS12, COL12A1, and COL5A2) may be pivotal mediators of immune infiltration in TMB-high patients, which may help to identify patients who could benefit from immunotherapy.

MiRNA-Based Signature Associated With Tumor Mutational Burden in Colon Adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most common malignant tumors. Tumor mutation burden (TMB) has become an independent biomarker for predicting the response to immune checkpoint inhibitors (ICIs). miRNAs play an important role in cancer-related immune regulation. However, the relationship between miRNA expression and TMB in COAD remains unclear. Therefore, the transcriptome profiling data, clinical data, mutation annotation data, and miRNA expression profiles for cases of COAD were downloaded from the TCGA database. Subsequently, 323 COAD cases were randomly divided into training and test sets. The differential expression of miRNAs in the high and low TMB groups in the training set was obtained as a signature using the least absolute shrinkage and selection operator (LASSO) logistic regression and verified in the test set. Based on the LASSO method, principal component analysis (PCA), and ROC, we found that the signature was credible because it can discriminate between high and low TMB levels. In addition, the correlation between the 18-miRNA-based signature and immune checkpoints was performed, followed by qRT-PCR, to measure the relative expression of 18 miRNAs in COAD patients. The miRNA-based model had a strong positive correlation with TMB and a weak positive correlation with CTLA4 and CD274 (PD-L1). However, no correlation was observed between the model and SNCA (PD-1). Finally, enrichment analysis of the 18 miRNAs was performed to explore their biological functions. The results demonstrated that 18 miRNAs were involved in the process of immunity and cancer pathways. In conclusion, the 18-miRNA-based signature can effectively predict and discriminate between the different TMB levels of COAD and provide a guide for its treatment with ICIs.

Tumor Mutation Burden Predicts Relapse in Papillary Thyroid Carcinoma With Changes in Genes and Immune Microenvironment.

BackgroundThe risk factors of papillary thyroid carcinoma (PTC) recurrence are meaningful for patients and clinicians. Tumor mutation burden (TMB) has been a biomarker for the effectiveness of immune checkpoint inhibitor (ICI) and prognosis in cancer. However, the role of TMB and its latent significance with immune cell infiltration in PTC are still unclear. Herein, we aimed to explore the effect of TMB on PTC prognosis.Material and MethodsRNA-seq and DNA-seq datasets of PTC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Gene Ontology (GO) and gene set enrichment analysis (GSEA 4.0.1) were applied further to explore potential differences in PTC patients’ biological functions. The differentially expressed genes (DEGs) and immune microenvironment between the high and low TMB groups were determined.ResultsTMB had the highest AUC score than other clinical indicators in ROC analysis on recurrence-free survival, and a higher TMB score was related to a worse prognosis. Further, GSEA showed a higher level of oxidative phosphorylation (OXPHOS) in the high TMB group, and four genes correlated with recurrence-free survival rate were identified. The abundance of CD8+ T cells and M1 macrophages in the high TMB group was significantly lower than that in the low TMB group.ConclusionsOur study found that TMB was a better predictor variable at evaluating the risk of PTC recurrence. Moreover, TMB-related genes conferred dramatically correlated prognosis, which was worth exploring in guiding postoperative follow-up and predicting recurrence for PTC patients.

Correlation analysis of tumor mutation burden of hepatocellular carcinoma based on data mining.

The aim of this study was to determine the relationship between tumor mutation burden (TMB) and prognosis of patients with hepatocellular carcinoma (HCC), and to explore the differential expression of genes in HCC by TMB and the relationship between immune cells, TMB, and HCC. Somatic variation data, gene transcriptional expression data and clinical information of patients with HCC were obtained from cancer genome map (TCGA) database. Analyze the characteristics of the gene mutation data of the sample, divide the high and low TMB groups and draw the survival curve at the same time, carry on the difference analysis to the gene of TMB, further carry on the univariate Cox regression analysis and Lasso regression analysis and construct the clinical model. Download the dataset GSE14520, from the Gene Expression Omnibus (GEO) database to verify the genes of the prognostic model. The differential genes were analyzed by gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes by (KEGG) enrichment analysis. Then the relative abundance of 22 immune cell types in HCC and normal control samples was calculated. Finally, the correlation between the scores of immune cells and Risk model was analyzed. Tumor protein p53 (TP53), catenin1 (CTNNB1), titin (TTN), mucin 16 (MUC16), and albumin (ALB) are the most common top 5 mutations in HCC. The prognosis of high level TMB group is worse than that of low TMB group. A total of 122 differentially expressed genes were screened by differential analysis of TMB genes. SQSTM1, ME1, BAMBI and PTTG1 are independent risk factors for poor prognosis of HCC. GO and KEGG analysis showed that the differential genes were mainly in extracellular matrix and immune response. There were significant differences in the distribution of Macrophages M0 and T cells CD4 native cells between HCC and normal tissues, which were correlated with the differential genes of TMB and correlated with prognosis. There is a negative correlation between TMB and the prognosis of patients with HCC. TMB has an effect on the differential expression of genes in HCC cells and the distribution of immune cells in tumor tissues.

Identification of NTRK3 as a potential prognostic biomarker associated with tumor mutation burden and immune infiltration in bladder cancer

BackgroundBladder cancer (BLCA) is a common malignant tumor of urinary system with high morbidity and mortality. In recent years, immunotherapy has played a significant role in the treatment of BLCA. Tumor mutation burden (TMB) has been reported to be a powerful biomarker for predicting tumor prognosis and efficacy of immunotherapy. Our study aimed to explore the relationship between TMB, prognosis and immune infiltration to identify the key genes in BLCA.MethodsClinical information, somatic mutation and gene expression data of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA) database. Patients were divided into high and low TMB groups according to their calculated TMB scores. Gene Set Enrichment Analysis (GSEA) was performed to screen for significantly enriched pathways. Differentially expressed genes (DEGs) between the two groups were identified. Univariate Cox analysis and Kaplan-Meier survival analysis were applied for screening key genes. Immune infiltration was performed for TMB groups and NTRK3.ResultsHigher TMB scores were related with poor survival in BLCA. After filtering, 36 DEGs were identified. NTRK3 had the highest hazard ratio and significant prognostic value. Co-expressed genes of NTRK3 were mainly involved in several pathways, including DNA replication, basal transcription factors, complement and coagulation cascades, and ribosome biogenesis in eukaryotes. There was a significant correlation among TMB scores, NTRK3 expression and immune infiltration.ConclusionsOur results suggest that NTRK3 is a TMB-related prognostic biomarker, which lays the foundation for further research on the immunomodulatory effect of NTRK3 in BLCA.

Tumor Mutation Burden and Immune Invasion Characteristics in Triple Negative Breast Cancer: Genome High-Throughput Data Analysis.

In recent years, the emergence of immunotherapy has provided a new perspective for the treatment and management of triple-negative breast cancer (TNBC). However, the relationship between tumor mutation burden (TMB) and immune infiltration and the prognosis of TNBC remains unclear. In this study, to explore the immunogenicity of TNBC, we divided patients with TNBC into high and low TMB groups based on the somatic mutation data of TNBC in The Cancer Genome Atlas (TCGA), and screened out genes with mutation rate ≥10. Then, Kaplan-Meier survival analysis revealed that the 5-year survival rate of the high TMB group was much higher than that of the low TMB group and the two groups also showed differences in immune cell infiltration. Further exploration found that the FAT3 gene, which displays significant difference and a higher mutation rate between the two groups, is not only significantly related to the prognosis of TNBC patients but also exhibits difference in immune cell infiltration between the wild group and the mutant group of the FAT3 gene. The results of gene set enrichment analysis and drug sensitivity analysis further support the importance of the FAT3 gene in TNBC. This study reveals the characteristics of TMB and immune cell infiltration in triple-negative breast cancer and their relationship with prognosis, to provide new biomarkers and potential treatment options for the future treatment of TNBC. The FAT3 gene, as a risk predictor gene of TNBC, is considered a potential biological target and may provide new insight for the treatment of TNBC.