Exploration of the relationships between tumor mutation burden with immune infiltrates in clear cell renal cell carcinoma

Comprehensive bioinformatics analysis of human cytomegalovirus pathway genes in pan-cancer

BACKGROUND

Human cytomegalovirus (HCMV) is a herpesvirus that can infect various cell types and modulate host gene expression and immune response. It has been associated with the pathogenesis of various cancers, but its molecular mechanisms remain elusive.

METHODS

We comprehensively analyzed the expression of HCMV pathway genes across 26 cancer types using the Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We also used bioinformatics tools to study immune invasion and tumor microenvironment in pan-cancer. Cox regression and machine learning were used to analyze prognostic genes and their relationship with drug sensitivity.

RESULTS

We found that HCMV pathway genes are widely expressed in various cancers. Immune infiltration and the tumor microenvironment revealed that HCMV is involved in complex immune processes. We obtained prognostic genes for 25 cancers and significantly found 23 key genes in the HCMV pathway, which are significantly enriched in cellular chemotaxis and synaptic function and may be involved in disease progression. Notably, CaM family genes were up-regulated and AC family genes were down-regulated in most tumors. These hub genes correlate with sensitivity or resistance to various drugs, suggesting their potential as therapeutic targets.

CONCLUSIONS

Our study has revealed the role of the HCMV pathway in various cancers and provided insights into its molecular mechanism and therapeutic significance. It is worth noting that the key genes of the HCMV pathway may open up new doors for cancer prevention and treatment.

Elevated ADH5 expression suggested better prognosis in kidney renal clear cell carcinoma (KIRC) and related to immunity through single-cell and bulk RNA-sequencing

BACKGROUND

Despite the rapid advances in modern medical technology, kidney renal clear cell carcinoma (KIRC) remains a challenging clinical problem in urology. Researchers urgently search for useful markers to break through the therapeutic conundrum due to its high lethality. Therefore, the study explores the value of ADH5 on overall survival (OS) and the immunology of KIRC.

METHODS

The gene expression matrix and clinical information on ADH5 in the TCGA database were validated using external databases and qRT-PCR. To confirm the correlation between ADH5 and KIRC prognosis, univariate/multivariate Cox regression analysis was used. We also explored the signaling pathways associated with ADH5 in KIRC and investigated its association with immunity.

RESULTS

The mRNA and protein levels showed an apparent downregulation of ADH5 in KIRC. Correlation analysis revealed that ADH5 was directly related to histological grade, clinical stage, and TMN stage (p < 0.05). Univariate and multivariate Cox regression analysis identified ADH5 as an independent factor affecting the prognosis of KIRC. Enrichment analysis looked into five ADH5-related signaling pathways. The results showed no correlation between ADH5 and TMB, TNB, and MSI. From an immunological perspective, ADH5 was found to be associated with the tumor microenvironment, immune cell infiltration, and immune checkpoints. Lower ADH5 expression was associated with greater responsiveness to immunotherapy. Single-cell sequencing revealed that ADH5 is highly expressed in immune cells.

CONCLUSION

ADH5 could be a promising prognostic biomarker and a potential therapeutic target for KIRC. Besides, it was found that KIRC patients with low ADH5 expression were more sensitive to immunotherapy.

A novel prognostic predictor of immune microenvironment and therapeutic response in clear cell renal cell carcinoma based on angiogenesis-immune-related gene signature

Background

Clear cell renal cell carcinoma (ccRCC), the most common type of RCC, typically produces no symptoms initially. Patients with ccRCC are at increased risk of developing advanced metastatic disease due to the absence of dependable and effective prognostic biomarkers. Therefore, it is particularly urgent to find optimal stratification of patients with ccRCC to distinguish the clinical benefits of different malignant degrees. Angiogenesis has a profound impact on the malignant behavior of renal cancer cells, and anti-angiogenic drugs have been applied to metastatic renal cancer patients. Moreover, immune function dysregulation is also a significant factor in tumorigenesis. We aim to construct a predictive model that combines angiogenesis and immune-related genes (AIRGs) to aid clinicians in predicting ccRCC prognosis.

Methods

We gathered transcriptome and clinicopathology data from two datasets, the E-MTAB-1980 dataset and the Cancer Genome Atlas (TCGA). We utilized consensus clustering to find new molecular subgroups. A predictive model for the prognosis of angiogenesis-immune-associated genes (AIRGs) was conducted by the lasso and multivariate Cox regression analysis. The signature's predictive ability was then tested in different datasets. Meticulous scrutiny and comprehensive assessment were undertaken, both internally and externally, to establish the prognostic model. Analyses of immunogenomics were carried out to examine the relationship between risk scores and clinical/immune features, including immune cell infiltration, genomic alterations, and response to targeted and immunotherapy therapy.

Results

Our prognostic signature, comprising 4 AIRGs, stood as an independent prognostic factor for ccRCC, while risk scores emerged as a novel indicator for forecasting overall survival. Risk scores exhibited significant associations with various immunophenotypic factors, such as oncogenic pathways, antitumor response, different immune cell infiltration, antitumor immunity, and response to targeted and immunotherapy therapy.

Conclusions

AIRGs-based prognostic prediction model could effectively predict immunotherapy responses and survival outcomes of ccRCC.

XRCC1: a potential prognostic and immunological biomarker in LGG based on systematic pan-cancer analysis

X-ray repair cross-complementation group 1 (XRCC1) is a pivotal contributor to base excision repair, and its dysregulation has been implicated in the oncogenicity of various human malignancies. However, a comprehensive pan-cancer analysis investigating the prognostic value, immunological functions, and epigenetic associations of XRCC1 remains lacking. To address this knowledge gap, we conducted a systematic investigation employing bioinformatics techniques across 33 cancer types. Our analysis encompassed XRCC1 expression levels, prognostic and diagnostic implications, epigenetic profiles, immune and molecular subtypes, Tumor Mutation Burden (TMB), Microsatellite Instability (MSI), immune checkpoints, and immune infiltration, leveraging data from TCGA, GTEx, CELL, Human Protein Atlas, Ualcan, and cBioPortal databases. Notably, XRCC1 displayed both positive and negative correlations with prognosis across different tumors. Epigenetic analysis revealed associations between XRCC1 expression and DNA methylation patterns in 10 cancer types, as well as enhanced phosphorylation. Furthermore, XRCC1 expression demonstrated associations with TMB and MSI in the majority of tumors. Interestingly, XRCC1 gene expression exhibited a negative correlation with immune cell infiltration levels, except for a positive correlation with M1 and M2 macrophages and monocytes in most cancers. Additionally, we observed significant correlations between XRCC1 and immune checkpoint gene expression levels. Lastly, our findings implicated XRCC1 in DNA replication and repair processes, shedding light on the precise mechanisms underlying its oncogenic effects. Overall, our study highlights the potential of XRCC1 as a prognostic and immunological pan-cancer biomarker, thereby offering a novel target for tumor immunotherapy.

A Comprehensive Prognostic Model for Colon Adenocarcinoma Depending on Nuclear-Mitochondrial-Related Genes

Background: Colon adenocarcinoma (COAD) has increasing incidence and is one of the most common malignant tumors. The mitochondria involved in cell energy metabolism, oxygen free radical generation, and cell apoptosis play important roles in tumorigenesis and progression. The relationship between mitochondrial genes and COAD remains largely unknown. Methods: COAD data including 512 samples were set out from the UCSC Xena database. The nuclear mitochondrial-related genes (NMRGs)-related risk prognostic model and prognostic nomogram were constructed, and NMRGs-related gene mutation and the immune environment were analyzed using bioinformatics methods. Then, a liver metastasis model of colorectal cancer was constructed and protein expression was detected using Western blot assay. Results: A prognostic model for COAD was constructed. Comparing the prognostic model dataset and the validation dataset showed considerable correlation in both risk grouping and prognosis. Based on the risk score (RS) model, the samples of the prognostic dataset were divided into high risk group and low risk group. Moreover, pathologic N and T stage and tumor recurrence in the two risk groups were significantly different. The four prognostic factors, including age and pathologic T stage in the nomogram survival model also showed excellent predictive performance. An optimal combination of nine differentially expressed NMRGs was finally obtained, including LARS2, PARS2, ETHE1, LRPPRC, TMEM70, AARS2, ACAD9, VARS2, and ATP8A2. The high-RS group had more inflamed immune features, including T and CD4+ memory cell activation. Besides, mitochondria-associated LRPPRC and LARS2 expression levels were increased in vivo xenograft construction and liver metastases assays. Conclusion: This study established a comprehensive prognostic model for COAD, incorporating nine genes associated with nuclear-mitochondrial functions. This model demonstrates superior predictive performance across four prognostic factors: age, pathological T stage, tumor recurrence, and overall prognosis. It is anticipated to be an effective model for enhancing the prognosis and treatment of COAD.

Comprehensive analysis reveals TSPEAR as a prognostic biomarker in colorectal cancer

Background: Colorectal cancer (CRC) is one of the most common malignant tumors and has high morbidity and mortality rates. Previous studies have shown that TSPEAR mutations are involved in the development and progression of gastric cancer and liver cancer. However, the role of TSPEAR in CRC is still unclear. Methods: In The Cancer Genome Atlas (TCGA) database, 590 CRC patients with complete survival information were analyzed. We assessed TSPEAR expression in a pan-cancer dataset from the TCGA database. Cox regression analysis was performed to evaluate factors associated with prognosis. Enrichment analysis via the R package "clusterProfiler" was used to explore the potential function of TSPEAR. The single-sample GSEA (ssGSEA) method from the R package "GSVA" and the TIMER database were used to investigate the association between the immune infiltration level and TSPEAR expression in CRC. The R package "maftools" was used to explore the association between tumour mutation burden (TMB) and TSPEAR expression in CRC. CCK-8 assays and cell invasion assays were used to detect the effect of TSPEAR and TGIF2 on the biological behavior of CRC cells. Results: Pan-cancer analysis revealed that TSPEAR was upregulated in CRC tissues compared to normal tissues and that high TSPEAR expression was associated with poorer overall survival (OS) (p=0.0053). The expression of TSPEAR increased with increasing TNM stage, T stage, N stage, and M stage. The nomogram constructed with TSPEAR, age, and TNM stage showed better predictive value than TSPEAR, age, or TNM stage alone. Immune cell infiltration analysis revealed that high expression of TSPEAR was associated with lower immune cell infiltration. Tumor mutation burden (TMB) analysis indicated that high expression of TSPEAR was associated with lower TMB (p=0.005), and high TMB was associated with shorter OS (p=0.02). CCK-8 assays and cell invasion assays indicated that in vitro knockdown of TSPEAR inhibited the proliferation, migration, and invasion of CRC cells. In addition, TSPEAR expression may be regulated by the upstream transcription factor TGIF2. Conclusion: TSPEAR expression was higher in CRC tissues than in normal tissues. Its upregulation was significantly associated with a poor prognosis. Additionally, TSPEAR plays a significant role in tumor immunity and the biological behavior of CRC cells. Thus, TSPEAR may become a promising prognostic biomarker and therapeutic target for CRC patients.

Chemokine- and chemokine receptor-based signature predicts immunotherapy response in female colorectal adenocarcinoma patients

The clinical significance and comprehensive characteristics of chemokines and chemokine receptors in female patients with advanced colorectal adenocarcinoma have not ever been reported. Our study explored the expression profiles of chemokines and chemokine receptors and constructed a chemokine- and chemokine receptor-based signature in female patients with advanced colorectal adenocarcinoma. Four independent cohorts containing 1335 patients were enrolled in our study. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct the signature. CIBERSORT was used to evaluate the landscape of immune cell infiltration. Thirty-two pairs of tissue specimens of female advanced colorectal cancer (CRC) patients and two CRC cell lines were used to validate the signature in vitro. Quantitative real-time PCR and western blotting were performed to validate the mRNA and protein expression levels of signature genes. EdU and colony formation assays were performed to examine proliferative ability. Transwell and wound healing assays were used to evaluate cell invasion and migration capacity. During the signature construction and validation process, we found that the signature was more applicable to female patients with advanced colorectal adenocarcinoma. Hence, the subsequent study mainly focused on the particular subgroup. Enrichment analyses revealed that the signature was closely related to immunity. The landscape of immune cell infiltration presented that the signature was significantly associated with T cells CD8 and neutrophils. Gene set enrichment analysis (GSEA) confirmed that the high-risk group was chiefly enriched in the tumor-promoting related pathways and biological processes, whereas the low-risk group was mainly enriched in anti-tumor immune response pathways and biological processes. The signature was closely correlated with CTLA4, PDL1, PDL2, TMB, MSI, and TIDE, indicating that our signature could serve as a robust biomarker for immunotherapy and chemotherapy response. ROC curves verified that our signature had more robust prognostic power than all immune checkpoints and immunotherapy-related biomarkers. Finally, we used 32 pairs of tissue specimens and 2 CRC cell lines to validate our signature in vitro. We first provided a robust prognostic chemokine- and chemokine receptor-based signature, which could serve as a novel biomarker for immunotherapy and chemotherapy response to guide individualized treatment for female patients with advanced colorectal adenocarcinoma.

A four-gene-based methylation signature associated with lymph node metastasis predicts overall survival in lung squamous cell carcinoma

We aimed to identify prognostic methylation genes associated with lymph node metastasis (LNM) in lung squamous cell carcinoma (LUSC). Bioinformatics methods were used to obtain optimal prognostic genes for risk model construction using data from the Cancer Genome Atlas database. ROC curves were adopted to predict the prognostic value of the risk model. Multivariate regression was carried out to identify independent prognostic factors and construct a prognostic nomogram. The differences in overall survival, gene mutation and pathways between high- and low-risk groups were analyzed. Finally, the expression and methylation level of the optimal prognostic genes among different LNM stages were analyzed. FGA, GPR39, RRAD and TINAGL1 were identified as the optimal prognostic genes and were applied to establish a prognostic risk model. Significant differences were found among the different LNM stages. The risk model could predict overall survival, showing a moderate performance with AUC of 0.64-0.68. The model possessed independent prognostic value, and could accurately predict 1-, 3- and 5-year survival. Patients with a high risk score showed poorer survival. Lower gene mutation frequencies and enrichment of leukocyte transendothelial migration and the VEGF signaling pathway in the high-risk group may lead to the poor prognosis. This study identified several specific methylation markers associated with LNM in LUSC and generated a prognostic model to predict overall survival for LUSC patients.

Identification of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer

Introduction

Breast cancer (BC) is now the most common type of cancer in women. Disulfidptosis is a new regulation of cell death (RCD). RCD dysregulation is causally linked to cancer. However, the comprehensive relationship between disulfidptosis and BC remains unknown. This study aimed to explore the predictive value of disulfidptosis-related genes (DRGs) in BC and their relationship with the TME.

Methods

This study obtained 11 disulfidptosis genes (DGs) from previous research by Gan et al. RNA sequencing data of BC were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) databases. First, we examined the effect of DG gene mutations and copy number changes on the overall survival of breast cancer samples. We then used the expression profile data of 11 DGs and survival data for consensus clustering, and BC patients were divided into two clusters. Survival analysis, gene set variation analysis (GSVA) and ss GSEA were used to compare the differences between them. Subsequently, DRGs were identified between the clusters used to perform Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a prognosis model. Finally, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. CCK-8 and a colony assay obtained by knocking down genes and gene sequencing were used to validate the model.

Result

Two DG clusters were identified based on the expression of 11DGs. Then, 225 DRGs were identified between them. RS, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-RS shows a better prognosis and higher immunotherapy response than high-RS. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. CCK-8 and colony assay obtained by knocking down genes have demonstrated that the knockdown of high-risk genes in the RS model significantly inhibited cell proliferation.

Discussion

This study elucidates the potential relationship between disulfidptosis-related genes and breast cancer and provides new guidance for treating breast cancer.

A novel necroptosis-related long non-coding RNA signature predicts prognosis and immune response in cervical cancer patients

BACKGROUND

Necroptosis has been linked to the development of tumors. Long non-coding RNAs (IncRNAs) have been identified as having a major role in numerous biological and pathological procedures. Despite this, the precise role that necroptosis-related lncRNAs (NRLs) have in cervical cancer (CC) and their potential for predicting its prognosis is still to a large extent unclear.

METHODS

Gene expression RNA-sequencing data, mutational data, and clinical profiles for 309 CC patients were obtained from the Cancer Genome Atlas (TCGA) database. The NRLs were then identified with Pearson correlation analysis followed by splitting of the patients into training and validation sets in a 3:2 ratio. Cox and LASSO regression models were performed to construct a cervical cancer prognostic signature based on NRLs. This 5-NRLs signature was then verified by Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, and nomogram for prognostic prediction. Further, a correlation study between the risk score (RS) and immune cell infiltration, immune checkpoint molecules, tumor mutation burden (TMB), and the sensitivity of chemotherapy drug was conducted. To validate the 5-NRLs, a quantitative reverse transcription polymerase chain reaction (qRT-PCR) was finally performed.

RESULTS

The 5-NRLs signature was designed to accurately predict the prognosis of CC. It consists of AC092153.1, AC007686.3, LINC01281, AC009097.2, and RUSC1-AS1 and was found to be highly predictive using ROC and Kaplan-Meier curves. Furthermore, when analyzed through stratified survival analysis, it was confirmed to be an independent risk factor for prognosis. The nomogram and calibration curves further validated its clinical utility. Moreover, distinct differences between two risk groups were observed when examining immune cell infiltration, immune checkpoint molecules, somatic gene alterations and half-inhibitory concentration of anticancer drug.

CONCLUSIONS

The 5-NRLs signature is a novel and valuable tool for evaluating the prognosis of CC patients, providing clinicians with an informed decision-making framework to formulate tailored treatment plans for their patients.

Immune-related risk prognostic model for clear cell renal cell carcinoma: Implications for immunotherapy

Clear cell renal cell carcinoma (ccRCC) is associated with complex immune interactions. We conducted a comprehensive analysis of immune-related differentially expressed genes in patients with ccRCC using data from The Cancer Genome Atlas and ImmPort databases. The immune-related differentially expressed genes underwent functional and pathway enrichment analysis, followed by COX regression combined with LASSO regression to construct an immune-related risk prognostic model. The model comprised 4 IRGs: CLDN4, SEMA3G, CAT, and UCN. Patients were stratified into high-risk and low-risk groups based on the median risk score, and the overall survival rate of the high-risk group was significantly lower than that of the low-risk group, confirming the reliability of the model from various perspectives. Further comparison of immune infiltration, tumor mutation load, and immunophenoscore (IPS) comparison between the 2 groups indicates that the high-risk group could potentially demonstrate a heightened sensitivity towards immunotherapy checkpoints PD-1, CTLA-4, IL-6, and LAG3 in ccRCC patients. The proposed model not only applies to ccRCC but also shows potential in developing into a prognostic model for renal cancer, thus introducing a novel approach for personalized immunotherapy in ccRCC.

HYDIN mutation status as a potential predictor of immune checkpoint inhibitor efficacy in melanoma

BACKGROUND

The advent of immune checkpoint inhibitors (ICIs) has altered the outlook for cancer treatment. The estimation of predictive biomarkers could contribute to maximizing the benefits from ICIs treatment. Here, we explored the association between HYDIN mutations (HYDIN-MUT) in melanoma and ICIs efficacy.

METHODS

Clinical data and sequencing data from published studies were utilized to assess the association between HYDIN-MUT and the efficacy of ICIs treatment in melanoma patients.

RESULTS

Compared to other tumor types, HYDIN (36.14%) has the highest mutation rate in melanoma patients. In the anti-PD-1 treated cohort (n = 254), the HYDIN-MUT patients had a longer OS after ICIs treatment than the HYDIN wild-type (HYDIN-WT) patients (HR = 0.590 [95% CI, 0.410-0.847], P = 0.004); the objective response rate (ORR) and durable clinical benefit (DCB) were increased in patients with HYDIN-MUT (ORR = 46.25, DCB = 56.00%) compared to patients with HYDIN-WT (ORR = 30.99%, DCB = 42.76%) (ORR: P = 0.019; DCB: P = 0.060). In the anti-CTLA4 treated cohort (n = 174), HYDIN-MUT patients achieved significantly longer OS than HYDIN-WT patients (HR = 0.549 [95% CI, 0.366-0.823], P = 0.003); the proportion of ORR and DCB in HYDIN-MUT patients was significantly higher than that in HYDIN-WT patients (ORR 40.54% vs. 14.42%, P = 0.031; DCB 45.76% vs. 22.22%, P = 0.002). Further gene set enrichment analysis demonstrated that DNA repair and anti-tumor immunity were significantly enhanced in HYDIN-MUT patients.

CONCLUSIONS

HYDIN mutations are a potential predictive biomarker of ICIs efficacy in melanoma patients.

Construction of a novel immune response prediction signature to predict the efficacy of immune checkpoint inhibitors in clear cell renal cell carcinoma patients

Background

Immune checkpoint inhibitor (ICI) treatment has enhanced survival outcomes in clear cell renal cell carcinoma (ccRCC) patients. Nevertheless, the effectiveness of immunotherapy in ccRCC patients is restricted and we intended to develop and characterize an immune response prediction signature (IRPS) to forecast the efficacy of immunotherapy.

Methods

RNA-seq expression profile and clinicopathologic characteristics of 539 kidney cancer and 72 patients with normal specimens, were downloaded from the Cancer Genome Atlas (TCGA) database, while the Gene Expression Omnibus (GEO) database was used as the validation set, which included 24 ccRCC samples. Utilization of the TCGA data and immune genes databases (ImmPort and the InnateDB), we explored through Weighted Gene Co-expression Network Analysis (WGCNA), along with Least Absolute Shrinkage and Selection Operator method (LASSO), and constructed an IRPS for kidney cancer patients. GSEA and CIBERSORT were performed to declare the molecular and immunologic mechanism underlying the predictive value of IRPS. The Human Protein Atlas (HPA) was deployed to verify the protein expressions of IRPS genes. Tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS) were computed to determine the risk of immune escape and value the discrimination of IRPS. A ccRCC cohort with anti-PD-1 therapy was obtained as an external validation data set to verify the predictive value of IRPS.

Results

We constructed a 10 gene signature related to the prognosis and immune response of ccRCC patients. Considering the IRPS risk score, patients were split into high and low risk groups. Patients with high risk in the TCGA cohort tended towards advanced tumor stage and grade with poor prognosis (p < 0.001), which was validated in GEO database (p = 0.004). High-risk group tumors were related with lower PD-L1 expression, higher TMB, higher MSIsensor score, lower IPS, higher TIDE score, and enriched Treg cells, which might be the potential mechanism of immune dysfunction and exclusion. Patients in the IRPS low risk group had better PFS (HR:0.73; 95% CI: 0.54-1.0; P = 0.047).

Conclusion

A novel biomarker of IRPS was constructed to predict the benefit of immunotherapy, which might lead to more individualized prognoses and tailored therapy for kidney cancer patients.

Development and validation of two redox-related genes associated with prognosis and immune microenvironment in endometrial carcinoma

In recent studies, the tumourigenesis and development of endometrial carcinoma (EC) have been correlated significantly with redox. We aimed to develop and validate a redox-related prognostic model of patients with EC to predict the prognosis and the efficacy of immunotherapy. We downloaded gene expression profiles and clinical information of patients with EC from the Cancer Genome Atlas (TCGA) and the Gene Ontology (GO) dataset. We identified two key differentially expressed redox genes (CYBA and SMPD3) by univariate Cox regression and utilised them to calculate the risk score of all samples. Based on the median of risk scores, we composed low-and high-risk groups and performed correlation analysis with immune cell infiltration and immune checkpoints. Finally, we constructed a nomogram of the prognostic model based on clinical factors and the risk score. We verified the predictive performance using receiver operating characteristic (ROC) and calibration curves. CYBA and SMPD3 were significantly related to the prognosis of patients with EC and used to construct a risk model. There were significant differences in survival, immune cell infiltration and immune checkpoints between the low-and high-risk groups. The nomogram developed with clinical indicators and the risk scores was effective in predicting the prognosis of patients with EC. In this study, a prognostic model constructed based on two redox-related genes (CYBA and SMPD3) were proved to be independent prognostic factors of EC and associated with tumour immune microenvironment. The redox signature genes have the potential to predict the prognosis and the immunotherapy efficacy of patients with EC.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.

5-Methylcytosine (m5C) Modification Patterns and Tumor Immune Infiltration Characteristics in Clear Cell Renal Cell Carcinoma

Recently, studies have revealed the prognostic value of 5-methylcytosine (m5C) in clear cell renal cell carcinoma (ccRCC). However, the role of m5C methylation in ccRCC immune infiltration and the immunotherapeutic response remains unknown. Based on the mRNA expressions of 14 m5C regulators, we evaluated the m5C modification patterns of 530 tumor samples from the TCGA-ccRCC database. We used the principal component analysis (PCA) algorithm to construct individual patient m5Cscores to facilitate individual analysis of m5C modification patterns in ccRCC patients. We finally defined three different m5C modification patterns. Different clinical features and immune heterogeneity existed among the three patterns, and their immune infiltration characteristics could correspond to different immune phenotypes, including the immune-inflamed, immune-excluded, and immune-desert phenotype. We designed the m5Cscore calculated by the PCA algorithm to measure individual patients' m5C modification patterns. The low m5Cscore group presented with a positive prognosis, increased TMB, and immune activation. Additionally, low m5Cscore patients showed an increased response to immune checkpoint inhibitors. We further the value of the m5Cscore in predicting OS verified in four other tumor cohorts. Our findings revealed that m5C methylation modifications are essential in regulating ccRCC immune infiltration. Assessing single ccRCC patients' m5C modification patterns can fully improve our comprehension of tumor immune characteristics and be used to provide effective personalized immunotherapy strategies for clinical use.

The prognostic index of m7G-related genes in CRC correlates with immune infiltration

N7-methyladenosine (m⁷G) modifications have been the subject of growing research interest with respect to their relationship with the progression and treatment of various cancers. This analysis was designed to examine the association between m⁷G-related gene expression and colorectal cancer (CRC) patient outcomes. Initial training analyses were performed using the TCGA dataset, with the GSE28722 dataset then being used to validate these results. Univariate Cox analyses were initially conducted to screen out prognostic m⁷G-related genes, after which a LASSO approach was used to construct an m⁷G risk score (MRS) model. Kaplan-Meier curves, ROC curves, and Cox analyses were subsequently used to validate the prognostic utility of this model in CRC patients. The R maftools package was further employed to assess mutational characteristics in CRC patients in different MRS subgroups, while the ESTIMATE, CIBERSORT, and ssGSEA tools were used to conduct immune infiltration analyses. A WGCNA was then performed to identify key immune-associated hub genes. The EIF4E3, GEMIN5, and NCBP2 genes were used to establish the MRS model. Patients with high MRS scores exhibited worse overall survival than patients with low scores. In Cox analyses, MRS scores were independently associated with CRC patient prognosis. Patients with low MRS scores exhibited a higher tumor mutational burden and higher levels of microsatellite instability. In immune infiltration analyses, higher immune checkpoint expression and greater immune cell infiltration were also observed in patients with low MRS scores. WGCNA analyses further identified 25 CD8+ T cell infiltration-associated genes. These findings suggest that MRS values represent a useful biomarker capable of differentiating among CRC patients with different immunological features and prognostic outcomes, offering an opportunity to better determine which patients are likely to benefit from immune checkpoint inhibitor treatment.

Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma

BACKGROUND

To comprehensively analyze the stemness characteristics related to prognosis and the immune microenvironment in lung adenocarcinoma (LUAD).

METHODS

The OCLR machine learning method was used to calculate the stemness index (mRNAsi) of the LUAD samples. DEGs common between the low mRNAsi, normal, and high mRNAsi groups were screened and the immune-stemness genes were obtained. Then the PPI network was created and enrichment analyses were performed. Moreover, different subtypes based on immune-stemness genes associated with prognosis were identified, and the relationships between LUAD stemness and TIME variables were systematically analyzed, followed by TMB analysis.

RESULTS

Patients in the high mRNAsi groups with poor prognosis were screened along with 144 immune-stemness genes. IL-6, FPR2, and RLN3 showed a higher degree in the PPI network. A total of 26 immune-stemness genes associated with prognosis were screened. Two clusters were obtained (cluster 1 and cluster 2). Survival analysis revealed that patients in cluster 2 had a poor prognosis. A total of 12 immune cell subpopulations exhibited significant differences between cluster 1 and cluster 2 (P < 0.05). A total of 10 immune checkpoint genes exhibited significantly higher expression in cluster 1 (P < 0.05) than in cluster 2. Further, the TMB value in cluster 2 was higher than that in cluster 1 (P < 0.05).

CONCLUSION

Immune-stemness genes, including L-6, FPR2, and RLN3, might play significant roles in LUAD development via cytokine-cytokine receptor interaction, neuroactive ligand‒receptor interaction, and the JAK‒STAT pathway. Immune-stemness genes were related to tumor-infiltrating immune cells, TMB, and expression of immune checkpoint gene.

Bioinformatics analysis of immune-related prognostic genes and immunotherapy in renal clear cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is an immunogenic tumor, and investigating the immunorelated genes is essential. To investigate the immunoprognostic genes of ccRCC, we analyzed the data assimilated from a public database (The Cancer Genome Atlas (TCGA) database and the gene expression omnibus (GEO) database) using bioinformatics. Then, an immunoprognosis model was constructed to identify four hub genes with moderate predictive values for the prognosis of ccRCC patients. These four genes were associated with the prognosis of ccRCC patients based on Oncomine and Gena Expression Profiling Interactive Analysis (GEPIA) databases. The correlation analysis between the immune infiltrate, immune checkpoints, and immunotherapy and this immunoprognosis model showed that immune infiltration could predict the immunotherapy effects. We also conducted a quantitative real-time polymerase chain reaction analysis and found that the expressions of three hub genes were associated with tumor progression (P<0.1). In conclusion, four genes that may serve as potential biomarkers in ccRCC were identified with respect to prognosis.

Molecular characterization of renal cell carcinoma tumors from a phase III anti-angiogenic adjuvant therapy trial

Multigene assays can provide insight into key biological processes and prognostic information to guide development and selection of adjuvant cancer therapy. We report a comprehensive genomic and transcriptomic analysis of tumor samples from 171 patients at high risk for recurrent renal cell carcinoma post nephrectomy from the S-TRAC trial (NCT00375674). We identify gene expression signatures, including STRAC11 (derived from the sunitinib-treated population). The overlap in key elements captured in these gene expression signatures, which include genes representative of the tumor stroma microenvironment, regulatory T cell, and myeloid cells, suggests they are likely to be both prognostic and predictive of the anti-angiogenic effect in the adjuvant setting. These signatures also point to the identification of potential therapeutic targets for development in adjuvant renal cell carcinoma, such as MERTK and TDO2. Finally, our findings suggest that while anti-angiogenic adjuvant therapy might be important, it may not be sufficient to prevent recurrence and that other factors such as immune response and tumor environment may be of greater importance.

Based on the S-TRAC results, sunitinib is approved as adjuvant treatment for adult patients at high risk of recurrent RCC following nephrectomy. Here, the authors report the results of an integrated multi-omics tumor analysis of 171 patients from the trial and identify specific molecular subtypes as well as potential new targets.

A signature based on chromatin regulation and tumor microenvironment infiltration in clear cell renal cell carcinoma

Aims: This research aimed to construct a signature based on chromatin regulation in localized clear cell renal cell carcinoma (ccRCC). Materials & methods: Non-negative matrix factorization clustering was performed on 438 localized ccRCC cases. The immune infiltration was generated by the single-sample gene set enrichment analysis algorithm. Survival analyses were performed using the Kaplan-Meier method, and the significance of the differences was determined using the log-rank test. The risk score was constructed based on the expression of chromatin regulators to quantify chromatin modification. Results: A score system based on chromatin modification was established. The high-risk subtype was characterized by increased tumor mutation burden, whereas a low-risk score was characterized by an increase in chromatin regulator expression and better overall survival. Conclusion: This research has constructed a signature based on chromatin regulation in localized ccRCC.

Development and validation of a novel endoplasmic reticulum stress-related lncRNA prognostic signature and candidate drugs in breast cancer

Breast cancer (BC), the most common malignancy in women, has a high cancer-related mortality. Endoplasmic reticulum stress (ERS), a response to the accumulation of unfolded proteins, has emerging roles in tumorigenesis, including invasion, metastasis, immune escape, etc. However, few studies have focused on the correlation between ERS with long non-coding RNAs (lncRNAs) in BC. We attempted to construct an ERS-related lncRNA prognostic signature and study its value in BC from tumor mutational burden (TMB), tumor immune microenvironment (TIME), cluster, clinical treatment, and so on. In the present study, transcriptomic and clinical data of BC patients were extracted from The Cancer Genome Atlas (TCGA) database. Correlation test, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) method were performed to determine an ERS-related lncRNA prognostic signature. Survival and predictive performance were analyzed according to Kaplan-Meier curves and receiver operating characteristic (ROC) curves, while nomograms and calibration curves were established. Then, an enrichment analysis was performed to study the functions and biological processes of ERS-related lncRNAs. TMB and TIME were also analyzed to assess the mutational status and immune status. Additionally, by using consensus cluster analysis, we compared differences among tumor subtypes. Drug sensitivity analysis and immunologic efficacy evaluations were performed together for further exploration. We identified a novel prognostic signature consisting of 9 ERS-related lncRNAs. High-risk patients had worse prognoses. The signature had a good predictive performance as an independent prognostic indicator and was significantly associated with clinicopathological characteristics. Enrichment analysis showed that metabolic pathways were enriched in high-risk patients, while immune pathways were more active in low-risk patients. Low-risk patients had lower TMB, higher immune scores, and stronger immune functions. Cluster analysis clarified that cluster 2 had the most active immune functions and was sensitive to more drugs, which may have the best clinical immunological efficacy. A clinical efficacy evaluation revealed that patients in the low-risk group may benefit more from chemotherapy, targeted therapy, and immunotherapy. The novel signature has significant clinical implications in prognosis prediction for BC. Our study clarifies that there is a potential connection between the ERS-related lncRNAs and BC, which may provide new treatment guidelines for BC.

A novel tumor mutational burden-based risk model predicts prognosis and correlates with immune infiltration in ovarian cancer

Tumor mutational burden (TMB) has been reported to determine the response to immunotherapy, thus affecting the patient’s prognosis in many cancers. However, it is unclear whether TMB or TMB-related signature could be used as prognostic indicators for ovarian cancer (OC), as its potential association with immune infiltration remains poorly understood. Therefore, this study aimed to develop a novel TMB-related risk model (TMBrisk) to predict the prognosis of OC patients on the basis of exploring TMB-related genes, and to explore the potential association between TMB/TMBrisk and immune infiltration. The mutational landscape, TMB scores, and correlations between TMB and clinical characteristics and immune infiltration were investigated in The Cancer Genome Atlas (TCGA)-OV cohort. Differentially expressed gene (DEG) analyses and weighted gene co-expression network analysis (WGCNA) were performed to derive TMB-related genes. TMBrisk was constructed by Cox regression and further validated in Gene Expression Omnibus (GEO) datasets. The mRNA and protein expression levels and biological functions of TMBrisk hub genes were verified through Gene Expression Profiling Interactive Analysis (GEPIA), GSCA Lite, the Human Protein Atlas (HPA) database, and RT-qPCR. TMBrisk-related biological phenotypes were analyzed in function enrichment and tumor immune infiltration signature. Potential therapeutic regimens were inferred utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). According to our results, higher TMB was associated with better survival and higher CD8+ T cell, regulatory T cell, and NK cell infiltration. TMBrisk was developed based on CBWD1, ST7L, RFX5-AS1, C3orf38, LRFN1, LEMD1, and HMGB1. High TMBrisk was identified as a poor factor for prognosis in TCGA and GEO datasets; the high-TMBrisk group comprised more higher-grade (G2 and G3) and advanced clinical stage (stage III/IV) tumors. Meanwhile, higher TMBrisk was associated with an immunosuppressive phenotype, with less infiltration of a majority of immunocytes and less expression of several genes of the human leukocyte antigen (HLA) family. Moreover, a nomogram containing TMBrisk showed a strong predictive ability demonstrated by time-dependent ROC analysis. Overall, this novel TMB-related risk model (TMBrisk) could predict prognosis, evaluate immune infiltration, and discover new therapeutic regimens in OC, which is very promising in clinical promotion.

Integrated multi-omics analyses reveal that BCAM is associated with epigenetic modification and tumor microenvironment subtypes of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common and highly heterogeneous subtype of renal cell carcinoma. Dysregulated basal cell adhesion molecule (BCAM) gene is associated with poor prognosis in various cancers. However, the dysregulated functions and related multi-omics features of BCAM in ccRCC stay unclear.

RESULTS

BCAM expression was aberrantly downregulated in ccRCC and correlated with adverse pathological parameters and poor prognosis. Low mRNA expression of BCAM was remarkably associated with its CpG methylation levels and BAP1 mutation status. Patients with lower-expressed BCAM concomitant with BAP1 mutation had a worse prognosis. Using RNA-seq data from The cancer genome atlas, we found that compared to the BCAM-high expression subgroup, ccRCC patients in the BCAM-low expression subgroup had significantly higher levels of immune infiltration, higher immune checkpoint expression levels and lower TIDE (tumor immune dysfunction and exclusion) score, indicating potential better response to immunotherapy. Data from the Clinical Proteomic Tumor Analysis Consortium further validated the association between low BCAM expression and CD8 + inflamed phenotype at protein level. Meanwhile, our results suggested that the angiogenesis-related pathways were enriched in the BCAM-high expression subgroup. More importantly, according to the data from the GDSC database, we revealed that the BCAM-high expression subgroup should be more sensitive to anti-angiogenetic therapies, including sorafenib, pazopanib and axitinib.

CONCLUSIONS

These results suggest that BCAM could serve as a biomarker distinguishing different tumor microenvironment phenotypes, predicting prognosis and helping therapeutic decision-making for patients with ccRCC.

Comprehensive analysis of 7-methylguanosine and immune microenvironment characteristics in clear cell renal cell carcinomas

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. ccRCC has obvious immunological characteristics, and the infiltration of immune cells is related to the prognosis of ccRCC. The effect of immune checkpoint therapy is related to the dynamic changes of the tumor immune microenvironment (TIM). The 7-methylguanosine (m7G) is an additional mRNA modification ability besides m6A, which is closely related to the TIM and affects the occurrence and development of tumors. At present, the correlations between m7G and the immune microenvironment, treatment, and prognosis of ccRCC are not clear. As far as we know, there was no study on the relationship between m7G and the immune microenvironment and survival of clear cell renal cell carcinomas. A comprehensive analysis of the correlations between them and the construction of a prognosis model are helpful to improve the treatment strategy. Two different molecular subtypes were identified in 539 ccRCC samples by describing the differences of 29 m7G-related genes. It was found that the clinical features, TIM, and prognosis of ccRCC patients were correlated with the m7G-related genes. We found that there were significant differences in the expression of PD-1, CTLA4, and PD-L1 between high- and low-risk groups. To sum up, m7G-related genes play a potential role in the TIM, treatment, and prognosis of ccRCC. Our results provide new findings for ccRCC and help to improve the immunotherapy strategies and prognosis of patients.

Interleukin 20 receptor subunit beta (IL20RB) predicts poor prognosis and regulates immune cell infiltration in clear cell renal cell carcinoma

BACKGROUND AND OBJECTIVE

Emerging evidence has proven the robust role of tumor mutation burden (TMB) and immune cell infiltration (ICI) in cancer immunotherapy. However, the precise effect of TMB and ICI on clear cell renal cell carcinoma (ccRCC) remains elusive and merits further investigation. Therefore, we aim to identify the TMB-related genes in predicting prognosis and to explore the potential mechanisms of the identified Interleukin 20 receptor subunit beta (IL20RB) in ICI in ccRCC.

METHOD

The relative information of patients with ccRCC was obtained from The Cancer Genome Atlas database (TCGA). Immune-related genes were downloaded from the Immunology Database and Analysis Portal database. Cox regression analysis was used to identify prognosis-related immune genes for ccRCC. The relationship of IL20RB expression levels with clinicopathological parameters was analyzed using the "limma" and "survival" packages. Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) databases were used as external validation. Quantitative Real-time PCR (qRT-PCR) and western blots were used to validate the expression levels of IL20RB in tumor cells. Cell counting kit-8 (CCK-8) assay and colony formation assay were used to examine the effect of IL20RB on the viability of ccRCC cells. Gene set enrichment analysis (GSEA) was introduced for the analysis of IL20RB-related signaling pathways. Tumor Immune Estimation Resource (TIMER) and Tumor and Immune System Interaction Database (TISIDB) were utilized to determine the correlation of IL20RB expression levels with tumor-infiltrating immune cells (TIICs).

RESULTS

IL20RB was significantly overexpressed in different ccRCC tissues and cells. High IL20RB expression in ccRCC patients was associated with short overall survival, high tumor grade, and advanced TNM stage. After knockdown of IL20RB with small interfering RNA (siRNA) technology, ccRCC cells' proliferation was significantly attenuated. Moreover, overexpression of IL20RB could increase the infiltration level of several immune cells, especially T follicular helper cells (Tfh), and overexpressed Tfh cells were correlated with poor prognosis in ccRCC.

CONCLUSIONS

IL20RB may function as an immune-associated therapeutic target for it determines cancer progression and regulates immune cell infiltration in ccRCC.

A signature based on m6A pattern and tumor microenvironment infiltration in clear cell renal cell carcinoma

BACKGROUND

RNA N6-methyladenosine (m6A) has been found to have a critical impact on clear cell renal cell carcinoma (ccRCC) by affecting the tumor microenvironment (TME) and immune cell (IC) infiltration and is related to the treatment and survival rate of patients with ccRCC. However, the mechanism of m6A in TME and IC infiltration remained unclear.

METHODS

Nonnegative Matrix Factorization (NMF) clustering was performed on 650 ccRCC cases from the Cancer Genome Atlas (TCGA) and the Gene-Expression Omnibus (GEO) datasets. The immune infiltration was generated by the single-sample gene-set enrichment analysis (ssGSEA) algorithm. Survival analyses were performed using the Kaplan-Meier method, and the significance of the differences was determined using the log-rank test. The m6A score was constructed based on the expression of m6A regulators to quantify m6A modification. The package "survminer R" was employed to layer patients' low and high scores groups and predict the immunotherapy response.

RESULTS

Three different patterns of m6A modification were established, and significant differences in TME and IC infiltration features were found in these three patterns. Survival analysis demonstrated that m6A cluster A and m6A gene cluster A experienced a longer survival time. Evaluation of m6A modification patterns in individual tumors was initiated by the m6A score. The low m6A score subtype was characterized by increased tumor mutation burden (TMB) and immune infiltration, whereas a high m6A score with a lack of immune cell infiltration showed significantly better overall survival. m6A score was also associated with the expression of programmed cell death protein 1 (PD-L1) and cytotoxic T lymphocyte antigen 4 (CTLA-4). Patients in the high m6A score group had high PD-L1 expression and low CTLA-4 expression. Significant differences in prognosis were identified among types of different TMB and m6A scores, where low TMB and high m6A score had longer survival time.

CONCLUSIONS

This research indicated that m6A modification greatly affected TME and IC infiltration. Physicians can develop practical immunotherapy strategies for patients with ccRCC by evaluating m6A-associated genes.

Comprehensive Genomic Characterization of Tumor Microenvironment and Relevant Signature in Clear Cell Renal Cell Carcinoma

Purpose

To systematically investigate the characterization of tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC), we performed a comprehensive analysis incorporating genomic alterations, cellular interactions, infiltrating immune cells, and risk signature.

Patients and Methods

Multi-omics data including RNA-seq, single-nucleotide variant (SNV) data, copy number variation (CNV) data, miRNA, and corresponding prognostic data were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. The CIBERSORT algorithm was utilized to identify prognostic TME subclusters, and TMEscore was further quantified. Moreover, the mutational landscape of TCGA-KIRC was explored. Lastly, TIDE resource was applied to assess the significance of TMEscore in predicting immunotherapeutic benefits.

Results

We analyzed the TME infiltration patterns from 621 ccRCC patients and identified 5 specific TME subclusters associated with clinical outcomes. Then, we found that TMEcluster5 was significantly related to favorable prognosis and enriched memory B-cell infiltration. Accordingly, we depicted the clustering landscape of TMEclusters, TMEscore levels, tumor mutation burden (TMB), tumor grades, purity, and ploidy in all patients. Lastly, TIDE was used to assess the efficiency of immune checkpoint blockers (ICBs) and found that the TMEscore has superior predictive significance to TMB, making it an essential independent prognostic biomarker and drug indicator for clinical use.

Conclusions

Our study depicted the clustering landscape of TMEclusters, TMEscore levels, TMB, tumor grades, purity, and ploidy in total ccRCC patients. The TMEscore was proved to have promising significance for predicting prognosis and ICB responses, in accordance with the goal of developing rationally individualized therapeutic interventions.

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.

TP53 R273C Mutation Is Associated With Poor Prognosis in LGG Patients

Purpose: With the progress of cancer immunotherapy, hotspot mutations of common oncogenes and tumor suppressors are becoming new potential therapeutic targets. TP53 R273C mutation is one of the hotspot mutations of TP53, and it has a higher frequency in low-grade glioma (LGG). However, the function of this mutation and its prognostic significance in LGG are not still clear.

Methods: To address this question, RNA sequencing, clinical, and SNP data of LGG patients from the TCGA database were downloaded. The Kaplan–Meier (KM) method was used for survival analysis. Immune cell populations in this cohort were assessed via the MCP counter and CIBERSORT. DNA damage/repair scores were calculated by GSVA analysis. WGCNA was conducted to identify genes related to TMB.

Results: In the context of IDH1/2 mutation, LGG patients with TP53 R273C mutation had worse prognosis than other mutation types and wild types. This conclusion is still valid in LGG patients who had received chemotherapy or radiotherapy. Considering the 1p19q codeletion status, it was found that patients with both R273C mutation and 1p19q non-codeletion had the worst prognosis. Further analysis showed that LGG patients with TP53 R273C mutation had higher M2 macrophage infiltration and tumor mutation burden (TMB) than that of TP53 wild-type LGG patients, and higher TMB indicates poor prognosis in LGG patients. Furthermore, we identified genes which could be associated with higher M2 macrophage infiltration and TMB in LGG patients with TP53 R273C mutation.

Conclusion: The study indicates that TP53 R273C mutation is very likely oncogenic and may be used as an indicator of the prognosis of LGG.

Correlation Between Immune Cell Infiltration and PD-L1 Expression and Immune-Related lncRNA Determination in Triple-Negative Breast Cancer

As a key element of the tumor microenvironment (TME), immune cell infiltration (ICI) is a frequently observed histologic finding in people with triple-negative breast cancer (TNBC), and it is linked to immunotherapy sensitivity. Nonetheless, the ICI in TNBC, to the best of our knowledge, has not been comprehensively characterized. In our current work, computational algorithms based on biological data from next-generation sequencing were employed to characterize ICI in a large cohort of TNBC patients. We defined various ICI patterns by unsupervised clustering and constructed the ICI scores using the principal component analysis (PCA). We observed patients with different clustering patterns had distinct ICI profiles and different signatures of differentially expressed genes. Patients with a high ICI score tended to have an increased PD-L1 expression and improved outcomes, and these patients were associated with decreased tumor mutational burden (TMB). Interestingly, it was showed that patients with high TMB exhibited an ameliorated overall survival (OS) than patients with low TMB. Furthermore, TMB scores only affected the prognosis of TNBC patients in the low-ICI score group but not in the high group. Finally, we identified a new immune-related lncRNA (irlncRNA) signature and established a risk model for the TNBC prognosis prediction. In addition, the high-risk group was related to poor prognosis, a high infiltration level of plasma B cells, monocytes, M2 macrophages, and neutrophils and a low PD-L1 expression. Therefore, the characterization and systematic evaluation of ICI patterns might potentially predict the prognosis and immunotherapy response in TNBC patients.

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.

CD103+ Tissue Resident T-Lymphocytes Accumulate in Lung Metastases and Are Correlated with Poor Prognosis in ccRCC

Clear cell renal cell carcinoma (ccRCC) is a highly immunogenic tumor with variable responses to immune checkpoint therapy. The significance of the immune cell infiltrate in distant metastases, their association with the immune infiltrate in the primary tumors and their impact on prognosis are poorly described. We hypothesized that specific subtypes of immune cells may be involved in the control of metastases and may have an impact on the prognosis of ccRCC. We analyzed the immune microenvironment in ccRCC primary tumors with distant metastases, paired distant metastases and non-metastasized ccRCC (n = 25 each group) by immunohistochemistry. Confirmatory analyses for CD8+ and CD103+ cells were performed in a large ccRCC cohort (n = 241) using a TCGA-KIRC data set (ITGAE/CD103). High immune cell infiltration in primary ccRCC tumors was significantly correlated with the development of distant tumor metastasis (p < 0.05). A high density of CD103+ cells in ccRCC was more frequent in poorly differentiated tumors (p < 0.001). ccRCCs showed high levels of ITGAE/CD103 compared with adjacent non-neoplastic tissue. A higher density of CD103+ cells and a higher ITGAE/CD103 expression were significantly correlated with poor overall survival in ccRCC (log rank p < 0.05). Our results show a major prognostic value of the immune pattern, in particular CD103+ cell infiltration in ccRCC, and highlight the importance of the tumor immune microenvironment.

Chemokine CCL17 Affects Local Immune Infiltration Characteristics and Early Prognosis Value of Lung Adenocarcinoma

CCL17 is an important chemokine that plays a vital immunomodulatory role in the tumor microenvironment (TME). Analysis of lung adenocarcinoma (LUAD) data in Kaplan–Meier plotter databases found that the overall survival of patients in the CCL17 high-expression group was higher than that of the low-expression group, especially for patients with early (stages I and II) LUAD, which has a more positive prognostic value. Expression of CCL17 in LUAD was positively correlated with the proportion of tumor-infiltrating lymphocytes, immunostimulators, and major histocompatibility complexes using the TISIDB databases. Based on the RNA-seq and clinical data of 491 LUAD patients obtained from the TCGA database, 1,455 differential genes were found between the CCL17 high- and low-expression groups. Using WGCNA analysis confirmed that the expression of differential genes in the blue module is negatively correlated with poor survival and clinical stages of LUAD patients, and CCL17 and CCR4 genes belong to the hub genes in the blue module. Further analysis by the ESTIMATE and CIBERSORT algorithm found that the naive B cells and CD8⁺ T cells in the CCL17 high-expression group have a higher distribution ratio in the early LUAD patients, and the high immune score has a positive relationship with the overall survival rate. Using somatic mutation data of TCGA-LUAD, we found that 1) the tumor mutation burden values of the CCL17 high-expression group were significantly lower than those of the CCL17 low-expression group and 2) the expression levels of CCL17 and the tumor mutation burden values were negatively correlated. Transwell chemotaxis and cytotoxicity assays confirmed that CCL17 contributes to the migration of CCR4-positive lymphocytes into the H1993 LUAD TME and enhances the specific lysis of LUAD cells. In summary, high expression of CCL17 in the LUAD TME promotes local immune cell infiltration and antitumor immune response, which may contribute to the better survival and prognosis of patients with early LUAD.

Comprehensive Characterization of Metabolism-Associated Subtypes of Renal Cell Carcinoma to Aid Clinical Therapy

Renal cell carcinoma (RCC) is a disease characterized by excessive administration complexity because it exhibits extraordinary nonuniformity among distinct molecular subtypes. We herein intended to delineate the metabolic aspects of clear cell RCC (ccRCC) in terms of the gene expression profile. Recent studies have revealed that metabolic variations within tumors are related to the responsiveness to immune checkpoint inhibitor (ICI) therapy and patient prognosis. We used 100 previously reported metabolic (MTB) pathways to quantify the metabolic landscape of the 729 ccRCC patients. Three MTB subtypes were established, and the MTB scores were calculated using principal component analysis (PCA). The high MTB score group had better overall survival (OS) and was associated with higher expression of immune-checkpoint and immune-activity signatures. The opposite was true of the low MTB score group, which may explain the poor prognosis of these patients. Three ICI-treated cohorts or tyrosine kinase inhibitor (TKI) treated cohort proved that patients with higher MTB scores exhibited notable therapeutic benefits and clinical gains. This research explained that the MTB score could be applied as a powerful prognostic indicator and predictive of ICI or TKI therapy. Assessing the MTB scores in a more extended group will facilitate our perception of tumor metabolism and provide guidance for studies on targeted approaches for ccRCC patients.

Mining GEO and TCGA Database for Immune Microenvironment of Lung Squamous Cell Carcinoma Patients With or Without Chemotherapy

Background

Chemotherapy is the main treatment for patients with lung squamous cell carcinoma (LUSC). However, how chemotherapy affects their immune system is rarely reported. This study was aimed to compare the differences in the immune microenvironment of LUSC patients with or without chemotherapy.

Methods

A total of 494 LUSC samples were obtained from The Cancer Genome Atlas (TCGA) database. The immune cell infiltration was evaluated by the ssGSEA algorithm, and the tumor subtype was assayed by ConsensusClusterPlus. The differences in tumor mutation burden (TMB) and clinical information between the two types were then compared. Additionally, the differentially expressed genes (DEGs) between two types were analyzed and hub genes were validated in the GEO database.

Results

LSCC samples in TCGA were divided into three subtypes. Then, combining the tumor subtype and immune scores, the samples were divided into hot and cold tumors. Regardless of whether LUSC patients received chemotherapy, the survival of the hot tumor group was not significantly prolonged compared with that of the cold tumor group. For LUSC patients who received chemotherapy, the TMB value in hot tumor group was significantly higher. Total 501 DEGs were identified between two groups. The high expressions of hub genes CD19, CTLA4, FCGR3B, CD80, IL-10, etc. were also validated in the GSE37745 dataset.

Conclusion

Chemotherapy does not affect the survival and prognosis of LUSC patients, but it significantly increases the TMB value of patients with hot tumor. The DEGs, especially hub genes, such as CD19, CTLA4, and FCGR3B, may serve as biomarkers to distinguish cold and hot tumors in LUSC.

RFC2: a prognosis biomarker correlated with the immune signature in diffuse lower-grade gliomas

Diffuse lower-grade gliomas (LGG) represent the highly heterogeneous and infiltrative neoplasms in the central nervous system (CNS). Replication factor C 2 (RFC2) is a subunit of the RFC complex that modulates DNA replication and repair. However, the prognosis value of RFC2 and its association with the immune signature of tumor microenvironment (TME) in LGG remains unknown. Based on Oncomine, TCGA, GTEx, TIMER, GEPIA, and HPA databases, we evaluated RFC2 expression levels and its clinical prognostic value in LGG and other cancers. Then we analyzed the correlations between RFC2 expression and tumor mutation burden (TMB), tumor microsatellite instability (MSI), and mismatch repair (MMR) genes across cancers. And CIBERSORT and ESTIMATE algorithms were conducted to estimate the association of RFC2 with immune cell infiltration of LGG. Additionally, we performed the functional enrichment analyses of RFC2 in LGG. Then functional experiments were employed to further validate the oncogenic role of RFC2 in LGG. Our results showed that RFC2 was widely highly expressed in most types of cancer. And its expression was closely related to the clinicopathological features and prognosis in LGG and other cancer types. RFC2 levels were also correlated with TMB and MSI across various cancers. Furthermore, RFC2 was positively associated with the infiltration levels of immune cells and immune checkpoint genes in LGG. Additionally, in vitro experiments revealed that RFC2 played an oncogenic role in LGG progression. In conclusion, our findings revealed that RFC2 could serve as a reliable biomarker to predict the prognosis and immune signature for LGG.

A Ferroptosis-Related Genes Model Allows for Prognosis and Treatment Stratification of Clear Cell Renal Cell Carcinoma: A Bioinformatics Analysis and Experimental Verification

Introduction

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor characterized by poor prognosis and difficult treatment. Ferroptosis is a relatively new form of programmed cell death that involved in cancer development and therapy resistance. Studies have shown that targeted ferroptosis may be a novel option for the treatment of ccRCC, but key genes and their roles between ferroptosis and ccRCC are limited so far. This study aims to develop a ccRCC stratified model based on ferroptosis-related genes to provide a reference for the prognosis prediction and the individualized treatment of ccRCC.

Materials and Methods

The mRNAs expression data of ccRCC and FRGs were obtained from TCGA and FerrDb database, respectively. Through multiple analysis, a 4-FRG based prognostic stratified model was constructed and its predictive performance was validated through various methods. Then, a nomogram based on the model was constructed and ccRCC patients stratified by the model were analyzed for tumor microenvironment, immune infiltration, sensitivity for immune checkpoint inhibitors (ICIs)/traditional anti-tumor therapy and tumor mutation burden (TMB). Functional enrichment analysis was performed to explore potential biological pathways. Finally, we verified our model by RT-qPCR, siRNA transfection, scratch assay and CCK-8 assay.

Results

In this study, the stratified model and a model-based nomogram can accurately predict the prognosis of ccRCC patients in TCGA database. The patients stratified by the model showed different tumor microenvironments, immune infiltration, TMB, resistance to traditional and ICIs therapy, and sensitivity to ferroptosis. Functional enrichment analysis suggested several biological pathways related to the process and prognosis of ccRCC. RT-qPCR confirmed the differential expression of ferroptosis-related genes. Scratch assay and CCK-8 assay indicated the promotion effects of CD44 on the proliferation and migration of ccRCC.

Conclusion

In this study, we established a novel ccRCC stratified model based on FRGs, which can accurately predict the prognosis of ccRCC patients and provide a reference for clinical individualized treatment.

Immunotherapy: A new standard in the treatment of metastatic clear cell renal cell carcinoma

Renal cell cancer (RCC) represents 2%-3% of all adulthood cancers and is the most common malignant neoplasm of the kidney (90%). In the mid-nineties of the last century, the standard of treatment for patients with metastatic RCC was cytokines. Sunititib and pazopanib were registered in 2007 and 2009, respectively, and have since been the standard first-line treatment for metastatic clear cell RCC (mccRCC). Renal cell cancer is a highly immunogenic tumor with tumor infiltrating cells, including CD8+ T lymphocytes, dendritic cells, natural killer cells (NK) and macrophages. This observation led to the design of new clinical trials in which patients were treated with immunotherapy. With the growing evidence that proangiogenic factors can have immunomodulatory effects on the host's immune system, the idea of combining angiogenic drugs with immunotherapy has emerged, and new clinical trials have been designed. In the last few years, several therapeutic options have been approved [immunotherapy and immunotherapy/tyrosine kinase inhibitors (TKI)] for the first-line treatment of mccRCC. Nivolumab/ipilimumab is approved for the treatment of patients with intermediate and poor prognoses. Several checkpoint inhibitors (pembrolizumab, nivolumab, avelumab) in combination with TKI (axitinib, lenvatinib, cabozantinib) are approved for the treatment of patients regardless of their International mRCC Database Consortium prognostic group and PD-L1 expression. There is no specific and ideal biomarker that could help in selecting the ideal patient for the appropriate first-line treatment.

High tumor mutation burden indicates a poor prognosis in patients with intrahepatic cholangiocarcinoma

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is malignancies of the biliary duct system and constitutes approximately 10%-20% of all primary liver cancers. Tumor mutation burden (TMB) is a useful biomarker across many cancer types for the identification of patients who will benefit from immunotherapy. Despite the role of TMB in calculating the effectiveness and prognosis of immune checkpoint inhibitors has been confirmed in multiple human cancer types, the prognostic value of TMB in ICC patients is rare investigated.

AIM

To investigate the prognostic value of TMB in patients with ICC.

METHODS

Data of 412 patients with ICC were included in the study. TMB was calculated as the total number of somatic non-silent protein-coding mutations divided by the coding region. The Kaplan-Meier method was used to analyze overall survival (OS), and relapse free survival (RFS). The cut-off value of TMB was determined by time-dependent receiver operating characteristic (ROC) curve. Cox regression was performed for multivariable analysis of OS. The nomogram and calibration curve were analyzed to construct and evaluate the prognostic model.

RESULTS

In the analysis of the time-dependent ROC curve, we defined 3.1 mut/Mb as the cut-off value of TMB. The Kaplan-Meier plot revealed that patients with high TMB had poor OS (HR = 1.47, P = 0.002) and RFS (HR = 1.42, P = 0.035). Cox regression analysis also demonstrated that TMB was an independent risk predictor for ICC (HR = 1.43, P = 0.0240). Furthermore, independent prognostic factors of ICC included CA19-9 (HR = 1.78, P = 0.0005), chronic viral hepatitis (HR = 1.72, P = 0.0468), tumor resection (HR = 2.58, P < 0.0001) and disease progression (metastatic disease vs. solitary liver tumor; HR = 2.55, P = 0.0002). The nomogram and calibration curve also indicated the effectiveness of the constructed prognostic model.

CONCLUSION

TMB was an independent prognostic biomarker in patients with ICC. Moreover, patients with ICC with high TMB had poor OS and RFS as compared to those with low TMB.

Association of a Novel Prognosis Model with Tumor Mutation Burden and Tumor-Infiltrating Immune Cells in Thyroid Carcinoma

Although immunotherapy has recently demonstrated a substantial promise in treating advanced thyroid carcinoma (THCA), it is not appropriate for all THCA patients. As a result, this study aims to identify biomarkers for predicting immunotherapy efficacy and prognosis in THCA patients based on a constructed prognostic model. The transcriptomic and corresponding clinical data of THCA patients were obtained from the Cancer Genome Atlas (TCGA) database. We identified differentially expressed genes (DEGs) between THCA and normal samples and performed an intersection analysis of DEGs with immune-related genes (IRGs) downloaded from the ImmPort database. Functional enrichment analysis was performed on the chosen immune-related DEGs. Subsequently, Cox and LASSO regression analyses were conducted to obtain three hub immune-related DEGs, including PPBP, SEMA6B, and GCGR. Following that, a prognostic risk model was established and validated based on PPBP, SEMA6B, and GCGR genes to predict immunotherapy efficacy and THCA prognosis. Finally, we investigated the association between the constructed risk model and tumor mutational burden (TMB), abundance of tumor-infiltrating immune cells (TICs) as well as immunotherapeutic targets (PDL-1, PD-1, and CTLA4) in THCA. THCA patients in the high-risk score (RS) group showed higher TMB levels and worse prognosis than the low RS group. Patients in the high-RS group had higher proportions of monocytes, M2 macrophages, and activated dendritic cells, whereas those in the low-RS group exhibited higher numbers of M1 macrophages and dendritic resting cells. Our data implied that the constructed THCA prognostic model was sound and we concluded that the THCA patients having high TMB and low PD-L1 expression levels might respond poorly to immunotherapy. Taken together, we constructed a novel prognostic model for THCA patients to predict their prognosis and immunotherapy efficacy, providing a viable option for the future management of THCA patients in the clinic.

Characterization of the CpG island methylator phenotype subclass in papillary thyroid carcinoma

CpG island methylator phenotype (CIMP), characterized by the concurrent and widespread hypermethylation of a cluster of CpGs, has been reported to play an important role in carcinogenesis. Limited studies have explored the role of CIMP in papillary thyroid carcinomas (PTCs). Here, in genome-wide DNA methylation analysis of 350 primary PTCs from the Cancer Genome Atlas database that were assessed using the Illumina HumanMethylation450K platform, our study helps to identify two subtypes displayed markedly distinct DNA methylation levels, termed CIMP (high levels of DNA methylation) and nCIMP subgroup (low levels of DNA methylation). Interestingly, PTCs with CIMP tend to have a higher degree of malignancy, since this subtype was tightly associated with older age, advanced pathological stage, and lymph node metastasis (all P < 0.05). Differential methylation analysis showed a broad methylation gain in CIMP and subsequent generalized gene set testing analysis based on the significantly methylated probes in CIMP showed remarkable enrichment in epithelial mesenchymal transition and angiogenesis hallmark pathways, confirming that the CIMP phenotype may promote the tumor progression from another perspective. Analysis of tumor microenvironment showed that CIMP PTCs are in an immune-depletion status, which may affect the effectiveness of immunotherapy. Genetically, the significantly higher tumor mutation burden and copy number alteration both at the genome and focal level confirmed the genomic heterogeneity and chromosomal instability of CIMP. tumor Corresponding to the above findings, PTC patients with CIMP showed remarkable poor clinical outcome as compared to nCIMP regarding overall survival and progression-free survival. More importantly, CIMP was associated with worse survival independent of known prognostic factors.

Identification of CCL4 as an Immune-Related Prognostic Biomarker Associated With Tumor Proliferation and the Tumor Microenvironment in Clear Cell Renal Cell Carcinoma

The last decade has witnessed revolutionary advances taken in immunotherapy for various malignant tumors. However, immune-related molecules and their characteristics in the prediction of clinical outcomes and immunotherapy response in clear cell renal cell carcinoma (ccRCC) remain largely unclear. C-C Motif Chemokine Ligand 4 (CCL4) was extracted from the intersection analysis of common differentially expressed genes (DEGs) of four microarray datasets from the Gene Expression Omnibus database and immune-related gene lists in the ImmPort database using Cytoscape plug-ins and univariate Cox regression analysis. Subsequential analysis revealed that CCL4 was highly expressed in ccRCC patients, and positively correlated with multiple clinicopathological characteristics, such as grade, stage and metastasis, while negatively with overall survival (OS). We performed gene set enrichment analysis (GSEA) and gene set variant analysis (GSVA) with gene sets coexpressed with CCL4, and observed that gene sets positively related to CCL4 were enriched in tumor proliferation and immune-related pathways while metabolic activities in the negatively one. To further explore the correlation between CCL4 and immune-related biological process, the CIBERSORT algorithm, ESTIMATE method, and tumor mutational burden (TMB) score were employed to evaluate the tumor microenvironment (TME) characteristics of each sample and confirmed that high CCL4 expression might give rise to high immune cell infiltration. Moreover, correlation analysis revealed that CCL4 was positively correlated with common immune checkpoint genes, such as programmed cell death protein 1 (PD-1), cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and lymphocyte activating 3 (LAG3). Overall, this study demonstrated that CCL4 might serve as a potential immune-related prognostic biomarker to predict clinical outcomes and immunotherapy response in ccRCC. Moreover, CCL4 might contribute to TME modulation, indicating the mechanism CCL4 involved in tumor proliferation and metastasis, which could provide novel therapeutic perceptions for ccRCC patients.

Prognostic Value of Tumor Mutational Burden Related to Immune Infiltration in Cervical Squamous Cell Carcinoma

Cervical squamous cell carcinoma is one of the most common causes of female cancer deaths worldwide. At present, immunotherapy using immune checkpoint blockade (ICB) has improved the prognosis of many cancer patients, and neoantigens generated by mutations may serve as potential biomarkers for predicting the outcome of ICB therapy. In this study, we identified missense mutations as the most frequent in landscapes of gene mutation in cervical squamous cell carcinoma (CESC) samples. Patients with higher tumor mutation burden (TMB) presented higher overall survival (OS). In addition, there was a significant correlation between the high TMB group and fractions of most immune cells. Univariate and multivariate Cox regression analyses identified five hub genes (IFNG, SERPINA3, CCL4L2, TNFSF15, and IL1R1) that were used to build a prognostic model. In the prognostic model, the low-risk group achieved better OS. Mutations in the five hub genes mainly affected the infiltration level of CD8+ T cells and dendritic cells. In conclusion, our study is valuable for exploring the role of TMB and its relationship with immune infiltration in CESC. Moreover, the prognosis model may help predict the sensitivity of patients to immunotherapy and provide underlying biomarkers for personalized immunotherapy.

From Bench to Bedside: How the Tumor Microenvironment Is Impacting the Future of Immunotherapy for Renal Cell Carcinoma

Immunotherapy has revolutionized the treatment landscape for many cancer types. The treatment for renal cell carcinoma (RCC) has especially evolved in recent years, from cytokine-based immunotherapies to immune checkpoint inhibitors. Although clinical benefit from immunotherapy is limited to a subset of patients, many combination-based approaches have led to improved outcomes. The success of such approaches is a direct result of the tumor immunology knowledge accrued regarding the RCC microenvironment, which, while highly immunogenic, demonstrates many unique characteristics. Ongoing translational work has elucidated some of the mechanisms of response, as well as primary and secondary resistance, to immunotherapy. Here, we provide a comprehensive review of the RCC immunophenotype with a specific focus on how preclinical and clinical data are shaping the future of immunotherapy.