Screening and Identifying Immune-Related Cells and Genes in the Tumor Microenvironment of Bladder Urothelial Carcinoma: Based on TCGA Database and Bioinformatics

Immunochemistry-based quantification of tumor-infiltrating lymphocytes and immunoscore as prognostic biomarkers in bladder cancer

BACKGROUND

Tumor-infiltrating lymphocytes (TILs) and the derived immunoscore (IS) have gained considerable attention over the last decade as prognostic markers in many solid cancers. However, in bladder cancer (BC), their prognostic value is not clearly established.

METHODS

The present study aimed to quantify the TILs rates in BC, assess the derived immunoscore, and investigate their prognostic value. An immunochemistry-based quantification of the different subtypes of TILS was performed on paraffin-embedded blocks from patients with invasive urothelial carcinoma of the bladder. We have assessed the rates of TILs, respectively, on peri-tumoral (PT) and intra-tumoral (IT) areas and calculated for each case the corresponding IS which is the index: CD8+/CD3+ TILs. The IS was then classified as low (I0, I1) or high (I2, I3, I4). We included 30 cases in the analysis.

RESULTS

The median age of patients was 65 years with a sex ratio of 9. TILs densities and distribution were significantly variable between IT and PT areas CD3+ (p = 0.03) and CD8+ (p = 0.004) with the highest rates on the PT areas. In univariate analysis, a low density of CD8+ TILs was significantly associated with an advanced age (p = 0.05), with the presence of lympho-vascular invasion (p = 0.02) and with the absence of specific histological subtype (p = 0.05). A low immunoscore was significantly associated with the presence of lympho-vascular invasion (p = 0.004). No significant association was found between TILs subpopulations, the IS, and the other clinicopathological and survival data. The overall survival (OS) and disease-free survival (DFS) medians were slightly superior in highly T (CD3+/CD8+)-cell infiltrated tumors as well as tumors with a high IS densities. However, the univariate analysis showed that TILs and immunoscore did not impact overall survival (OS) and disease-free survival (DFS).

CONCLUSION

TILs and immunoscore might be effective prognostic tools in BC. However, standardized quantification methods and further investigation on larger samples are highly recommended to definitively attest the prognostic value of TILs and IS in BC.

Development and experimental verification of a prognosis model for disulfidptosis-associated genes in HNSCC

Disulfidptosis is a newly discovered cell death pattern that has been less studied in head and neck squamous carcinoma (HNSCC). Exploring the molecular features of different subtypes of HNSCC based on disulfidptosis-associated genes (DAGs) is important for HNSCC. In addition, immunotherapy plays a pivotal role in the treatment of HNSCC. Exploring the sensitivity of immunotherapies and developing predictive models is essential for HNSCC. We analyzed the expression and mutational status of DAGs in 790 HNSCC patients and correlated the dates with clinical prognosis. HNSCC patients were divided into 2 groups based on their DAG expression. The relationship between DAGs, risk genes, and the immune microenvironment was analyzed using the CIBERSORT algorithm. A disulfidptosis risk model was constructed based on 5 risk genes using the LASSO COX method. To facilitate the clinical applicability of the proposed risk model, we constructed column line plots and performed stem cell correlation analysis and antitumor drug sensitivity analysis. Two different disulfidptosis-associated clusters were identified using consistent unsupervised clustering analysis. Correlations between multilayer DAG alterations and clinical characteristics and prognosis were observed. Then, a well-performing disulfidptosis-associated risk model (DAG score) was developed to predict the prognosis of HNSCC patients. We divided patients into high-risk and low-risk groups based on the DAG score and found that patients in the low-risk group were more likely to survive than those in the high-risk group (P < .05). A high DAG score implies higher immune cell infiltration and increased mutational burden. Also, univariate and multivariate Cox regression analyses revealed that the DAG score was an independent prognostic predictor for patients with HNSCC. Subsequently, a highly accurate predictive model was developed to facilitate the clinical application of DAG scores, showing good predictive and calibration power. Overall, we present a comprehensive overview of the DAG profile in HNSCC and develop a new risk model for the therapeutic status and prognosis of patients with HNSCC. Our findings highlight the potential clinical significance of DAG and suggest that disulfidptosis may be a potential therapeutic target for patients with HNSCC.

Cancer-associated Fibroblasts in Bladder Cancer: Origin, Biology, and Therapeutic Opportunities

CONTEXT

Bladder cancer (BLCA) is a highly prevalent tumour and a health problem worldwide, especially among men. Recent work has highlighted the relevance of the tumour microenvironment (TME) in cancer biology with translational implications. Cancer-associated fibroblasts (CAFs) are a prominent, heterogeneous population of cells in the TME. CAFs have been associated with tumour development, progression, and poor prognosis in several neoplasms. However, their role in BLCA has not yet been exploited deeply.

OBJECTIVE

To review the role of CAFs in BLCA biology and provide an understanding of CAF origin, subtypes, markers, and phenotypic and functional characteristics to improve patient management.

EVIDENCE ACQUISITION

A PubMed search was performed to review manuscripts published using the terms "cancer associated fibroblast" and "bladder cancer" or "urothelial cancer". All abstracts were reviewed, and the full content of all relevant manuscripts was analysed. In addition, selected manuscripts on CAFs in other tumours were considered.

EVIDENCE SYNTHESIS

CAFs have been studied less extensively in BLCA than in other tumours. Thanks to new techniques, such as single-cell RNA-seq and spatial transcriptomics, it is now possible to accurately map and molecularly define the phenotype of fibroblasts in normal bladder and BLCA. Bulk transcriptomic analyses have revealed the existence of subtypes among both non-muscle-invasive and muscle-invasive BLCA; these subtypes display distinct features regarding their CAF content. We provide a higher-resolution map of the phenotypic diversity of CAFs in these tumour subtypes. Preclinical studies and recent promising clinical trials leverage on this knowledge through the combined targeting of CAFs or their effectors and the immune microenvironment.

CONCLUSIONS

Current knowledge of BLCA CAFs and the TME is being increasingly applied to improve BLCA therapy. There is a need to acquire a deeper understanding of CAF biology in BLCA.

PATIENT SUMMARY

Tumour cells are surrounded by nontumoural cells that contribute to the determination of the behaviour of cancers. Among them are cancer-associated fibroblasts. The "neighbourhoods" established through these cellular interactions can now be studied with much greater resolution. Understanding these features of tumours will contribute to the designing of more effective therapies, especially in relationship to bladder cancer immunotherapy.

PRR11 is a prognostic biomarker and correlates with immune infiltrates in bladder urothelial carcinoma

Abnormal proline-rich protein 11 (PRR11) expression is associated with various tumors. However, there are few reports concerning PRR11 with prognostic risk, immune infiltration, or immunotherapy of bladder urothelial carcinoma (BLCA). This study is based on online databases, such as Oncomine, GEPIA, HPA, LinkedOmics, TIMER, ESTIMATE and TISIDB, and BLCA data downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus, we employed an array of bioinformatics methods to explore the potential oncogenic roles of PRR11, including analyzing the relationship between PRR11 and prognosis, tumor mutational burden (TMB), microsatellite instability, and immune cell infiltration in BLCA. The results depict that PRR11 is highly expressed in BLCA, and BLCA patients with higher PRR11 expression have worse outcomes. In addition, there was a significant correlation between PRR11 expression and TMB and tumor immune infiltration. These findings suggest that PRR11 can be used as a potential marker for BLCA patient assessment and risk stratification to improve clinical prognosis, and its potential regulatory mechanism in the BLCA tumor microenvironment and targeted therapy is worthy of further investigation.

Impact of tissue inhibitor of metalloproteinases-3 genetic variants on clinicopathological characteristics of urothelial cell carcinoma

To investigate the distribution of single nucleotide polymorphism (SNP) of tissue inhibitor of metalloproteinases-3 (TIMP-3) in patients with/without urothelial cell carcinoma (UCC), three loci of TIMP-3 SNPs (rs9862 C/T, rs9619311 T/C, rs11547635 C/T) were genotyped via TaqMan allelic discrimination for 424 UCC patients and 848 non-UCC participants. Furthermore, the TIMP-3 mRNA expression and its correlation with clinical characters of urothelial bladder carcinoma was analyzed using The Cancer Genome Atlas database (TCGA). The distribution of all 3 studied SNPs of TIMP-3 was insignificantly different between the UCC and non-UCC groups. However, significantly lower tumor T status was found in TIMP-3 SNP rs9862 CT + TT variant than the wild type (OR: 0.515, 95% CI: 0.289-0.917, P = 0.023). Moreover, the muscle invasive tumor type was significantly correlated to the TIMP-3 SNP rs9619311 TC + CC variant in the non-smoker subgroup (OR: 2.149, 95% CI: 1.143-4.039, P = 0.016). With the TIMP-3 expression data provided in TCGA, significantly higher TIMP-3 mRNA expression was observed in UCC with high tumor stage (P < 0.0001), high tumor T status (P < 0.0001) and high lymph node status (P = 0.0005). In conclusions, TIMP-3 SNP rs9862 variant is associated with lower tumor T status of UCC while TIMP-3 SNP rs9619311 variant is correlated to muscle invasive UCC development in non-smoker.

Tumor microenvironment and epithelial-mesenchymal transition in bladder cancer: Cytokines in the game?

Bladder cancer (BlCa) is a highly immunogenic cancer. Bacillus Calmette-Guérin (BCG) is the standard treatment for non-muscle invasive bladder cancer (NMIBC) patients and, recently, second-line immunotherapies have arisen to treat metastatic BlCa patients. Understanding the interactions between tumor cells, immune cells and soluble factors in bladder tumor microenvironment (TME) is crucial. Cytokines and chemokines released in the TME have a dual role, since they can exhibit both a pro-inflammatory and anti-inflammatory potential, driving infiltration and inflammation, and also promoting evasion of immune system and pro-tumoral effects. In BlCa disease, 70-80% are non-muscle invasive bladder cancer, while 20-30% are muscle-invasive bladder cancer (MIBC) at the time of diagnosis. However, during the follow up, about half of treated NMIBC patients recur once or more, with 5-25% progressing to muscle-invasive bladder cancer, which represents a significant concern to the clinic. Epithelial-mesenchymal transition (EMT) is one biological process associated with tumor progression. Specific cytokines present in bladder TME have been related with signaling pathways activation and EMT-related molecules regulation. In this review, we summarized the immune landscape in BlCa TME, along with the most relevant cytokines and their putative role in driving EMT processes, tumor progression, invasion, migration and metastasis formation.

A risk score for the prognosis prediction of the muscle-invasive bladder cancer patients who received gemcitabine plus cisplatin chemotherapy

To develop an individualized gene-based risk score to predict the prognosis of muscle-invasive bladder cancer (MIBC) patients who received GC regimens. We downloaded transcriptome profiling data and clinical information from the Cancer Genome Atlas (TCGA) database. We identified 1854 survival-associated genes and then constructed the risk score based on six special genes selected from the survival-associated genes. We divided patients into high-risk and low-risk groups according to the median risk score. High-risk patients have significantly poorer overall survival than low-risk patients (log-rank test chi-square = 38.08, p = 7e-10, C-index = 0.785, se = 0.032). The risk score was evaluated by Kaplan-Meier survival curve, time-dependent ROC curves, and C-index. Multivariate Cox regression and nomogram suggested that the risk score was an independent prognostic indicator. Gene set enrichment analysis indicated that the survival-associated genes were significantly enriched in immune-related terms. Among six special genes, CHPF2, TRAV26-2, and BTF3P12 were found to be immune-related genes. In conclusion, our risk score provided an indicator to predict the prognosis of MIBC patients who received GC regimens and potential immunotherapeutic targets for MIBC.

Development and experimental verification of a prognosis model for cuproptosis-related subtypes in HCC

BACKGROUND

Cuproptosis is a recently discovered mechanism of programmed cell death caused by intracellular aggregation of mitochondrial lipoylated proteins and destabilization of iron-sulfur proteins triggered by copper. Hepatocellular carcinoma (HCC) is a common malignant tumor with a poor prognosis. We aimed to predict the survival of patients with HCC using the cuproptosis-related gene (CRG) expression.

METHODS

We analyzed the expression, methylation, and mutation status of CRGs in 538 HCC patients and correlated the date with clinical prognosis. HCC patients were divided into two clusters based on their CRG expression. The relationship between CRGs, risk genes, and the immune microenvironment was analyzed using the CIBERSORT algorithm and the single-cell data analysis method. A cuproptosis risk model was constructed according to the five risk genes using the LASSO COX method. To facilitate the clinical applicability of the proposed risk model, we constructed a nomogram and conducted an antineoplastic drug sensitivity analysis.

RESULTS

Our results suggest that the expression levels of CRGs in HCC are regulated by methylation. The prognoses were significantly different between the patients of the two clusters. The prognostic risk score positively correlated with memory T cell activation and negatively correlated with natural killer (NK) and regulatory T cell activation.

CONCLUSION

Our findings indicate the involvement of CRG regulation in HCC and provide new insights into prognosis assessment. Drug sensitivity analysis predicted drug candidates for the treatment of patients with different HCC subtypes.

A signature based on five immune-related genes to predict the survival and immune characteristics of neuroblastoma

BACKGROUND

MYCN amplification (MNA) has been proved to be related to poor prognosis in neuroblastoma (NBL), but the MYCN-related immune signatures and genes remain unclear.

METHODS

Enrichment analysis was used to identify the significant enrichment pathways of differentially expressed immune-related genes (DEIRGs). Weight gene coexpression network analysis (WGCNA) was applied to reveal the correlation between these DEIRGs and MYCN status. Univariate and multivariate Cox analyses were used to construct risk model. The relevant fractions of immune cells were evaluated by CIBERSORT and single-sample gene set enrichment analysis (ssGSEA).

RESULTS

Five genes, including CHGA, PTGER1, SHC3, PLXNC1, and TRIM55 were enrolled into the risk model. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve showed that our model performed well in predicting the outcomes of NBL (3-years AUC = 0.720, 5-year AUC = 0.775, 10-years AUC = 0.782), which has been validated in the GSE49711 dataset and the E-MTAB-8248 dataset. By comparing with the tumor immune dysfunction and exclusion (TIDE) and tumor inflammation signature (TIS), we further proved that our model is reliable. Univariate and multivariate Cox regression analyses indicated that the risk score, age, and MYCN can serve as independent prognostic factors in the E-MATB-8248. Functional enrichment analysis showed the DEIRGs were enriched in leukocyte adhesion-related signaling pathways. Gene set enrichment analysis (GSEA) revealed the significantly enriched pathways of the five MYCN-related DEIRGs. The risk score was negatively correlated with the immune checkpoint CD274 (PD-L1) but no significant difference with the TMB. We also confirmed the prognostic value of our model in predicting immunotherapeutics.

CONCLUSION

We constructed and verified a signature based on DEIRG that related to MNA and predicted the survival of NBL based on relevant immune signatures. These findings could provide help for predicting prognosis and developing immunotherapy in NBL.

Identification of adenylate cyclase 2 methylation in bladder cancer with implications for prognosis and immunosuppressive microenvironment

Background

The incidence and mortality of bladder cancer (BCa) are increasing, while the existing diagnostic methods have limitations. Therefore, for early detection and response prediction, it is crucial to improve the prognosis and treatment strategies. However, with existing diagnostic methods, detecting BCa in the early stage is challenging. Hence, novel biomarkers are urgently needed to improve early diagnosis and treatment efficiency.

Methods

The gene expression profile and gene methylation profile dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), differentially methylated genes (DMGs), and methylation-regulated differentially expressed genes (MeDEGs) were gradually identified. A cancer genome map was obtained using online gene expression profile interaction analysis, and survival implications were produced using Kaplan-Meier survival analysis. GSEA was employed to predict the marker pathways where DEGs were significantly involved. The study used bisulfite PCR amplification combined with bisulfite amplicon sequencing (BSAS) to screen for methylation analysis of multiple candidate regions of the adenylate cyclase 2 (ADCY2) based on the sequence design of specific gene regions and CpG islands.

Results

In this study, DEGs and DMGs with significantly up- or down-regulated expression were selected. The intersection method was used to screen the MeDEGs. The interaction network group in STRING was then visualized using Cytoscape, and the PPI network was constructed to identify the key genes. The key genes were then analyzed using functional enrichment. To compare the relationship between key genes and the prognosis of BCa patients, we further investigated ADCY2 and found that ADCY2 can be a potential clinical biomarker in BCa prognosis and immunotherapy response prediction. In human BCa 5637 and MGH1 cells, we developed and verified the effectiveness of ADCY2 primers using BSAS technology. The findings revealed that the expression of ADCY2 is highly regulated by the methylation of the promoter regions.

Conclusion

This study revealed that increased expression of ADCY2 was significantly correlated with increased tumor heterogeneity, predicting worse survival and immunotherapy response in BCa patients.

Next-generation sequencing for MRD monitoring in B-lineage malignancies: from bench to bedside

Minimal residual disease (MRD) is considered the strongest relevant predictor of prognosis and an effective decision-making factor during the treatment of hematological malignancies. Remarkable breakthroughs brought about by new strategies, such as epigenetic therapy and chimeric antigen receptor-T (CAR-T) therapy, have led to considerably deeper responses in patients than ever, which presents difficulties with the widely applied gold-standard techniques of MRD monitoring. Urgent demands for novel approaches that are ultrasensitive and provide sufficient information have put a spotlight on high-throughput technologies. Recently, advances in methodology, represented by next-generation sequencing (NGS)-based clonality assays, have proven robust and suggestive in numerous high-quality studies and have been recommended by some international expert groups as disease-monitoring modalities. This review demonstrates the applicability of NGS-based clonality assessment for MRD monitoring of B-cell malignancies by summarizing the oncogenesis of neoplasms and the corresponding status of immunoglobulin (IG) rearrangements. Furthermore, we focused on the performance of NGS-based assays compared with conventional approaches and the interpretation of results, revealing directions for improvement and prospects in clinical practice.

Identification of an immune‐related gene signature as a prognostic target and the immune microenvironment for adrenocortical carcinoma

Background

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy. Even with complete tumor resection and adjuvant therapies, the prognosis of patients with ACC remains unsatisfactory. In the microtumor environment, the impact of a disordered immune system and abnormal immune responses is enormous. To improve treatment, novel prognostic predictors and treatment targets for ACC need to be identified. Hence, credible prognostic biomarkers of immune‐associated genes (IRGs) should be explored and developed.

Material and methods

We downloaded RNA‐sequencing data and clinical data from The Cancer Genome Atlas (TCGA) data set, Genotype‐Tissue Expression data set, and Gene Expression Omnibus data set. Gene set enrichment analysis (GSEA) was applied to reveal the potential functions of differentially expressed genes.

Results

GSEA indicated an association between ACC and immune‐related functions. We obtained 332 IRGs and constructed a prognostic signature on the strength of 3 IRGs (INHBA, HELLS, and HDAC4) in the training cohort. The high‐risk group had significantly poorer overall survival than the low‐risk group (p < .001). Multivariate Cox regression was performed with the signature as an independent prognostic indicator for ACC. The testing cohort and the entire TCGA ACC cohort were utilized to validate these findings. Moreover, external validation was conducted in the GSE10927 and GSE19750 cohorts. The tumor‐infiltrating immune cells analysis indicated that the quantity of T cells, natural killer cells, macrophage cells, myeloid dendritic cells, and mast cells in the immune microenvironment differed between the low‐risk and high‐risk groups.

Conclusion

Our three‐IRG prognostic signature and the three IRGs can be used as prognostic indicators and potential immunotherapeutic targets for ACC. Inhibitors of the three novel IRGs might activate immune cells and play a synergistic role in combination therapy with immunotherapy for ACC in the future.

Bioinformatic analysis of RNA-seq data from TCGA database reveals prognostic significance of immune-related genes in colon cancer

The tumor immune microenvironment is of crucial importance in cancer progression and anticancer immune responses. Thus, systematic exploration of the expression landscape and prognostic significance of immune-related genes (IRGs) to assist in the prognosis of colon cancer is valuable and significant. The transcriptomic data of 470 colon cancer patients were obtained from The Cancer Genome Atlas database and the differentially expressed genes were analyzed. After an intersection analysis, the hub IRGs were identified and a prognostic index was further developed using multivariable Cox analysis. In addition, the discriminatory ability and prognostic significance of the constructed model were validated and the characteristics of IRGs associated overall survival were analyzed to elucidate the underlying molecular mechanisms. A total of 465 differentially expressed IRGs and 130 survival-associated IRGs were screened. Then, 46 hub IRGs were identified by an intersection analysis. A regulatory network displayed that most of these genes were unfavorable for the prognosis of colon cancer and were regulated by transcription factors. After a least absolute shrinkage and selection operator regression analysis, 14 hub IRGs were ultimately chose to construct a prognostic index. The validation results illustrated that this model could act as an independent indicator to moderately separate colon cancer patients into low- and high-risk groups. This study ascertained the prognostic significance of IRGs in colon cancer and successfully constructed an IRG-based prognostic signature for clinical prediction. Our results provide promising insight for the exploration of diagnostic markers and immunotherapeutic targets in colon cancer.

Protein-based prognostic signature for predicting the survival and immunotherapeutic efficiency of endometrial carcinoma

Background

Endometrial cancer (EC) is the most frequent malignancy of the female genital tract worldwide. Our study aimed to construct an effective protein prognostic signature to predict prognosis and immunotherapy responsiveness in patients with endometrial carcinoma.

Methods

Protein expression data, RNA expression profile data and mutation data were obtained from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA). Prognosis-related proteins in EC patients were screened by univariate Cox regression analysis. Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis were performed to establish the protein-based prognostic signature. The CIBERSORT algorithm was used to quantify the proportions of immune cells in a mixed cell population. The Immune Cell Abundance Identifier (ImmuCellAI) and The Cancer Immunome Atlas (TCIA) web tools were used to predict the response to immunochemotherapy. The pRRophetic algorithm was used to estimate the sensitivity of chemotherapeutic and targeted agents.

Results

We constructed a prognostic signature based on 9 prognostic proteins, which could divide patients into high-risk and low-risk groups with distinct prognoses. A novel prognostic nomogram was established based on the prognostic signature and clinicopathological parameters to predict 1, 3 and 5-year overall survival for EC patients. The results obtained with Clinical Proteomic Tumor Analysis Consortium (CPTAC), Human Protein Atlas (HPA) and immunohistochemical (IHC) staining data from EC samples in our hospital supported the predictive ability of these proteins in EC tumors. Next, the CIBERSORT algorithm was used to estimate the proportions of 22 immune cell types. The proportions of CD8 T cells, T follicular helper cells and regulatory T cells were higher in the low-risk group. Moreover, we found that the prognostic signature was positively associated with high tumor mutation burden (TMB) and high microsatellite instability (MSI-H) status in EC patients. Finally, ImmuCellAI and TCIA analyses showed that patients in the low-risk group were more inclined to respond to immunotherapy than patients in the high-risk group. In addition, drug sensitivity analysis indicated that our signature had potential predictive value for chemotherapeutics and targeted therapy.

Conclusion

Our study constructed a novel prognostic protein signature with robust predictive ability for survival and efficiency in predicting the response to immunotherapy, chemotherapy and targeted therapy. This protein signature represents a promising predictor of prognosis and response to cancer treatment in EC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09402-w.

Expressions of PD-L1 and Nectin-4 in urothelial cancer patients treated with pembrolizumab

OBJECTIVES

Recently, the standard of care for advanced urothelial cancer (UC) has been changed by developing immune-checkpoint inhibitors (ICIs). However, its response rate is limited to 20-30%. The identification of biomarkers to predict the therapeutic effects of ICIs is urgently needed. The present study explored the association between immunohistochemical biomarkers and clinical outcomes in UC patients treated with pembrolizumab.

PATIENTS AND METHODS

A total of 85 patients with UC who received pembrolizumab after chemotherapy from January 2018 to May 2020 were retrospectively reviewed. Tumor tissues were obtained for immunohistochemical study from 47 out of 85 patients. The protein expressions of PD-L1, WT1, Nectin-4, CD4, CD8, Foxp3, and CD68 in tumor cells and/or tumor infiltrating lymphocytes were immunohistochemically examined. The associations between protein expressions and overall survival (OS), progression-free survival (PFS), and disease control rate (DCR) were statistically analyzed.

RESULTS

Patients with positive PD-L1 in tumor cells showed significantly worse OS (Log-rank test: HR 5.146, p = 0.001, Cox regression analysis: HR 4.331, p = 0.014) and PFS (Log-rank test: HR 3.31. p = 0.022), along with significantly lower DCR (14.3%) compared to the PD-L1 negative patients (67.5%). In addition, patients with strong expression of Nectin-4 in tumor cells showed significantly higher DCR (100%) than the other patients (50%).

CONCLUSION

PD-L1 expression in tumor cells was associated with poor prognosis (OS and PFS) and low DCR. Interestingly, the strong expression of Nectin-4 was correlated with high DCR. PD-L1 and Nectin-4 expression in tumor cells could be prognostic biomarkers useful for pembrolizumab in patients with advanced UC.

Development and Validation of a Six-Gene Prognostic Signature for Bladder Cancer

Human bladder cancer (BCa) is the most common urogenital system malignancy. Patients with BCa have limited treatment efficacy in clinical practice. Novel biomarkers could provide more crucial information conferring to cancer diagnosis, treatment, and prognosis. Here, we aimed to explore and identify novel biomarkers associated with cancer-specific survival of patients with BCa to build a prognostic signature. Based on univariate Cox regression, Lasso regression, and multivariate Cox regression analysis, we conducted an integrated analysis in the training set (GSE32894) and established a six-gene signature to predict the cancer-specific survival for human BCa. The six genes were Cyclin Dependent Kinase 4 (CDK4), E2F Transcription Factor 7 (E2F7), Collagen Type XI Alpha 1 Chain (COL11A1), Bradykinin Receptor B2 (BDKRB2), Yip1 Interacting Factor Homolog B (YIF1B), and Zinc Finger Protein 415 (ZNF415). Then, we validated the prognostic value of the model by using two other datasets (GSE13507 and TCGA). Also, we conducted univariate and multivariate Cox regression analyses, and results indicated that the six-gene signature was an independent prognostic factor of cancer-specific survival of patients with BCa. Functional analysis was performed based on the differentially expressed genes of low- and high-risk patients, and we found that they were enriched in lipid metabolic and cell division-related biological processes. Meanwhile, the gene set enrichment analysis (GSEA) revealed that high-risk samples were enriched in cell cycle and cancer-related pathways [G2/M checkpoint, E2F targets, mitotic spindle, mTOR signaling, spermatogenesis, epithelial–mesenchymal transition (EMT), DNA repair, PI3K/AKT/mTOR signaling, unfolded protein response (UPR), and MYC targets V2]. Lastly, we detected the relative expression of each signature in BCa cell lines by quantitative real-time PCR (qRT-PCR). As far as we know, currently, the present study is the first research that developed and validated a cancer-specific survival prognostic index based on three independent cohorts. The results revealed that this six-gene signature has a predictive ability for cancer-specific prognosis. Moreover, we also verified the relative expression of these six signatures between the bladder cell line and four BCa cell lines by qRT-PCR. Nevertheless, experiments to further explore the function of six genes are lacking.

The prognostic value of plasma complement factor B (CFB) in thyroid carcinoma

Stromal and immune cells are major components of tumor microenvironment (TME) and affect the growth and development of thyroid carcinoma (THCA). However, data on the exact mechanisms that define the relationship between the TME and THCA remain scant. We calculated stromal and immune cells scores and the proportion of tumor-infiltrating immune cells (TICs) by CIBERSORT and ESTIMATE based on the THCA gene expression data from the Cancer Genome Atlas (TCGA). In addition, we evaluated differentially expressed genes (DEGs) from high- and low-score groups and performed functional enrichment analysis. Furthermore, our data show a significant correlation between plasma complement factor B (CFB) and PTC development and prognosis. Gene Set Enrichment Analysis (GSEA) demonstrated that the CFB was mainly enriched in immune response pathways. The expression of CFB was positively correlated with T cells CD8, Macrophages M1, Plasma cells, T cells CD4 memory activated, T cells follicular helper and T cells regulatory (Tregs), whereas negatively correlated with Eosinophils, Macrophages M0, Macrophages M2, Mast cells resting, T cells CD4 memory resting in the TME. Finally, the expression level of CFB was verified by other cohorts from Gene Expression Omnibus (GEO) database and quantitative Real-Time PCR (qRT-PCR) analyses, which was consistent with the results of bioinformatic analysis. Taken together, our data demonstrated that the CFB could be a prognostic marker for THCA and its expression influences the infiltration of immune cells.

Up-regulation of S100P predicts the poor long-term survival and construction of prognostic signature for survival and immunotherapy in patients with pancreatic cancer

Pancreatic cancer is associated with a high mortality rate, and the prognosis is positively related to immune status. In this study, we constructed a prognostic signature from survival- and immune-related genes (IRGs) to guide treatment and assess prognosis of patients with pancreatic cancer. The transcriptomic data were obtained from The Cancer Genome Atlas (TCGA) database, and IRGs were extracted from the ImmPort database. Univariate and LASSO regression analysis were used to obtain survival-related IRGs. Finally, the prognostic signature was constructed using multivariate regression analysis. The laboratory experiments were conducted to verify the key IRG expression. Immune cells infiltration was analyzed using the CIBERSORT algorithm and TIMER database. Prognostic signature containing four IRGs (ADA2, TLR1, PTPN6, S100P) was constructed with good predictive performance; in particular, S100P played a significant role in the immune microenvironment, and tumorigenesis of pancreatic cancer. Moreover, we found that CD8⁺ T cell and activated CD4⁺ memory T cell tumor infiltration was lower in the high-risk group, while high-risk score correlated positively with higher tumor mutational burden, and the higher half inhibitory centration 50 of chemotherapeutic agents Docetaxel and Sunitinib. In summary, this study identified and constructed an immune-related prognostic signature that can predict overall survival, besides suggests that S100P was a novel immune-related biomarker. We hope that this signature will aid the identification of new biomarkers for the individualized immunotherapy of pancreatic cancer.

TNFSF13B and PPARGC1A expression is associated with tumor-infiltrating immune cell abundance and prognosis in clear cell renal cell carcinoma

Growing evidence suggests that the tumor microenvironment (TME) plays crucial roles in tumor progression and treatment efficacy in clear cell renal cell carcinoma (ccRCC), which typically has a poor prognosis due to high relapse and metastasis rates. We comprehensively analyzed ccRCC RNA-sequencing data from The Cancer Genome Atlas (TCGA) database to identify candidate prognostic TME-related genes involved in ccRCC. We used the ESTIMATE and CIBERSORT algorithms to estimate the proportions of immune cells, stromal cells, and tumor-infiltrating immune cells (TICs) in the TME in ccRCC samples from 539 patients. By examining the intersection of the differentially expressed genes (DEGs) obtained by Cox regression analysis and protein-protein interaction network, we identified five overlapping DEGs (IGLL5, MZB1, HSD11B1, TNFSF13B, and PPARGC1A). Further analysis revealed that TNFSF13B expression was elevated in ccRCC tumor tissues and negatively associated with overall survival. PPARGC1A expression exhibited the opposite patterns. Immunohistochemical analysis of 35 paired ccRCC and adjacent normal tissues confirmed the in-silico results. Gene set enrichment analysis revealed that genes in the groups with high TNFSF13B and PPARGC1A expression were enriched mainly in immune-related activities. In the group with low PPARGC1A expression, genes were enriched in metabolic pathways. CIBERSORT analysis of TIC proportions revealed that Tregs and CD8 T-cell abundance correlated positively with TNFSF13B expression, but negatively with PPARGC1A expression. These findings demonstrate that TNFSF13B and PPARGC1A are prognostic predictors and possible therapeutic targets in ccRCC.

A robust signature of immune-related long non-coding RNA to predict the prognosis of bladder cancer

Background

Bladder cancer is the second most common malignant tumor in the urogenital system. The research investigated the prognostic role of immune‐related long non‐coding RNA (lncRNA) in bladder cancer.

Methods

We extracted 411 bladder cancer samples from The Cancer Genome Atlas database. Single‐sample gene set enrichment analysis was employed to assess the immune cell infiltration of these samples. We recognized differentially expressed lncRNAs between tumors and paracancerous tissues, and differentially expressed lncRNAs between the high and low immune cell infiltration groups. Venn diagram analysis detected differentially expressed lncRNAs that intersected the above groups. LncRNAs with prognostic significance were identified by regression analysis. Multivariate Cox analysis was used to establish the risk score model. Then we established and evaluated the nomogram. Additionally, we performed gene set enrichment analysis to explore the potential functions of the screened lncRNAs in tumor pathogenesis.

Results

Three hundred and twenty differentially expressed lncRNAs were recognized. We randomly divided patients into the training data set and the testing data set at a 2: 1 ratio. In the training data set, 9 immune‐related lncRNAs with prognostic significance were identified. The risk score model was constructed to classify patients as high‐ and low‐risk cohorts. Patients in the low‐risk cohort had better survival outcomes than those in the high‐risk cohort. The nomogram was established based on the indicators including age, gender, tumor‐node‐metastases stage, and risk score. The model's predictive performance was confirmed by the receiver operating characteristic curve analysis, concordance index method, calibration curve, and decision curve analysis. The testing data set also achieved similar results. Bioinformatics analysis suggested that the 9‐lncRNA signature was involved in the modulation of various immune responses, antigen processing and presentation, and T cell receptor signaling pathway.

Conclusions

Our study uncovered the prognostic value of immune‐related lncRNAs for bladder cancer and showed that they may regulate tumor pathogenesis in various ways.

Integrative analysis of immune molecular subtypes and microenvironment characteristics of bladder cancer

The emergence of immunotherapy has provided an option of treatment methods for bladder cancer (BC). However, the beneficiaries of immunotherapy are still limited to small‐scale patients, and immunotherapy‐related adverse events often occur. It is a major challenge for clinical work to study the immune subtypes of BC and the molecular mechanism of immune escape, and identify the immune responders accurately. Here, we explore the immune molecular subtypes of bladder cancer and potential escape mechanisms. First, we screened the expression profiles of 303 differentially expressed immune‐related genes in BC patients from the Cancer Genome Atlas (TCGA) database, and successfully identified 4 molecular subtypes of BC. By comparing the clinical characteristics, immune cells infiltration, the expression of checkpoint genes, human leukocyte antigen (HLA) genes, and gene mutation status of different subtypes, we identified different clinical and immunological characteristics of 4 subtypes. Among 4 subtypes, Cluster 2 met the general characteristics of immunotherapy responders and responded well to immunotherapy, while Cluster 4 had the highest expression of immune characteristics, and is similar to the immune environment of normal bladder tissue. Then, the weighted gene co‐expression network analysis (WGCNA) of immune‐related genes revealed that brown module was positively correlated with subtypes. Pathway enrichment analysis explored the major pathways associated with subtypes, which are also associated with immune escape mechanisms. Moreover, the decision tree model, which was constructed by the principle of random forest screening factors, was also validated in internal validation set and external validation set from the Gene Expression Omnibus (GEO) cohort (GSE133624), and could achieve accurate subtypes prediction for BC patients with high‐throughput sequencing. Taken together, we explored the immune molecular subtypes and their mechanisms of BC, and these results may provide guidance for the development of new BC immunotherapy strategies.

Insights of fibroblast growth factor receptor 3 aberrations in pan-cancer and their roles in potential clinical treatment

Fibroblast growth factor receptor 3 (FGFR3) alters frequently across various cancer types and is a common therapeutic target in bladder urothelial carcinoma (BLCA) with FGFR3 variants. Although emerging evidence supports the role of FGFR3 in individual cancer types, no pan-cancer analysis is available. In this work, we used the open comprehensive datasets, covering a total of 10,953 patients with 10,967 samples across 32 TCGA cancer types, to identify the full alteration spectrum of FGFR3. FGFR3 abnormal expression, methylation patterns, alteration frequency, mutation location distribution, functional impact, and prognostic implications differed greatly from cancer to cancer. The overall alteration frequency of FGFR3 was relatively low in all cancers. Targetable mutations were mainly detected in BLCA, and S249C, Y373C, G370C, and R248C were hotspot mutations that could be targeted by an FDA approved erdafitinib. Genetic fusions were mainly observed in glioma, followed by BLCA. FGFR3-TACC3 was the most common fusion type which was proposed as novel therapeutic targets in glioma and was targetable with erdafitinib in BLCA. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) were two lung cancer subtypes, FGFR3 fusion and hotspot mutation like S249C were observed more commonly in LUSC but not in LUAD. DNA methylation was correlated with the expression of FGFR3 and its downstream genes in some tumors. FGFG3 abnormal expression and alterations exhibited clinical correlations with patient prognosis in several tumors. This work exhibited the full alteration spectrum of FGFR3 and indicated several new clues for their application as potential therapeutic targets and prognostic indicators.

Identification of a tumor microenvironment-related seven-gene signature for predicting prognosis in bladder cancer

Background

Accumulating evidences demonstrated tumor microenvironment (TME) of bladder cancer (BLCA) may play a pivotal role in modulating tumorigenesis, progression, and alteration of biological features. Currently we aimed to establish a prognostic model based on TME-related gene expression for guiding clinical management of BLCA.

Methods

We employed ESTIMATE algorithm to evaluate TME cell infiltration in BLCA. The RNA-Seq data from The Cancer Genome Atlas (TCGA) database was used to screen out differentially expressed genes (DEGs). Underlying relationship between co-expression modules and TME was investigated via Weighted gene co-expression network analysis (WGCNA). COX regression and the least absolute shrinkage and selection operator (LASSO) analysis were applied for screening prognostic hub gene and establishing a risk predictive model. BLCA specimens and adjacent tissues from patients were obtained from patients. Bladder cancer (T24, EJ-m3) and bladder uroepithelial cell line (SVHUC1) were used for genes validation. qRT-PCR was employed to validate genes mRNA level in tissues and cell lines.

Results

365 BLCA samples and 19 adjacent normal samples were selected for identifying DEGs. 2141 DEGs were identified and used to construct co-expression network. Four modules (magenta, brown, yellow, purple) were regarded as TME regulatory modules through WGCNA and GO analysis. Furthermore, seven hub genes (ACAP1, ADAMTS9, TAP1, IFIT3, FBN1, FSTL1, COL6A2) were screened out to establish a risk predictive model via COX and LASSO regression. Survival analysis and ROC curve analysis indicated our predictive model had good performance on evaluating patients prognosis in different subgroup of BLCA. qRT-PCR result showed upregulation of ACAP1, IFIT3, TAP1 and downregulation of ADAMTS9, COL6A2, FSTL1,FBN1 in BLCA specimens and cell lines.

Conclusions

Our study firstly integrated multiple TME-related genes to set up a risk predictive model. This model could accurately predict BLCA progression and prognosis, which offers clinical implication for risk stratification, immunotherapy drug screen and therapeutic decision.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08447-7.

Characterization of the immune cell infiltration landscape in bladder cancer to aid immunotherapy

The tumor microenvironment (TME) is composed mainly of tumor cells, tumorinfiltrating immune cells, and matrix components. Recent clinical studies have indicated that tumor immune cell infiltration (ICI) is related to the sensitivity of immunotherapy and the prognosis of patients with bladder cancer (BC). Nevertheless, up to now, the landscape of immune infiltration in BC has not been clearly defined. Here we present two algorithms to reveal the landscape of ICI in 277 cases of BC. Two kinds of ICI patterns were established, and ICI scores were based on the analysis of the main components. In sub-types with high ICI scores, we found highly expressed immunecheckpoint and activated transforming growth factor b and WNT signal pathways. These might be the cause of poor prognosis. A low ICI score indicated a better prognosis. Our study showed that ICI scores in immunotherapy could be a valid biomarker for the prognosis of patients and a predictive indicator. The evaluation of ICI patterns of a larger cohort of samples would expand our cognition of TME, and the present study might guide the strategies of immunotherapy for patients with BC.

Identification of mast cells as a candidate significant target of immunotherapy for acute myeloid leukemia

OBJECTION

Immunotherapy based on T cells is a new therapy for Acute myeloid leukemia (AML). However, there has not been considerable improvement compared with traditional chemotherapeutics. This study aimed to identify important immune cells, genes, and drugs associated with the immunotherapy of AML.

METHODS

The gene expression profile and clinical data of patients with AML were downloaded from TCGA database, and the abundance ratio of immune cells was obtained via CIBERSORT. Kaplan-Meier (KM) survival analysis was used to assess the relationship between immune cells and survival time of patients with AML. Differentially expressed genes (DEGs) analysis was conducted to obtained DEGs related to mast cells. Then, protein-protein interaction (PPI) analysis and enrichment analysis were performed to explore the hub genes. Finally, Connectivity Map (CMap) database was utilized to predicts potential drugs that may reverse or induce the mast cell-related gene expression.

RESULTS

Our study showed that mast cell was correlated with survival time of patients with AML, and 135 genes were screened to be related with mast cells. 6 hub genes were identified via PPI network, and 3 potential small molecule drugs were screened to be related to regulating the mast cell-related gene expression via CMap database.

CONCLUSION

The hub genes and drugs have high research value and clinical application in AML therapy. Our study not only provides gene targets and small molecule drugs for AML immunotherapy concerning mast cells but also provides new ideas for researchers to explore immunotherapy targets of other tumors.

KIF11 Serves as an Independent Prognostic Factor and Therapeutic Target for Patients With Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is challenging in clinical practice due to the poor understanding of molecular mechanisms and limited therapeutic targets. Herein, the work aimed to use bioinformatics to identify a promising molecular target for LUAD therapy.

Methods

Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) dataset were used for a weighted gene co-expression network analysis (WGCNA) to screen the hub gene. After a prognostic estimation with meta-analysis and COX regression analysis, we performed a function analysis on the corresponding gene. The ESTIMATE and CIBERSORT methods were adopted to analyze the association of the hub gene with the tumor microenvironment (TME). A cohort of functional assays was conducted to establish the functional roles of the hub gene in A549 and PC-9 cells.

Results

Our screen identified KIF11 as a prognostic factor, which indicated the poor overall survival and the worse progression-free survival in LUAD patients. Additionally, KIF11 was primarily involved in cell cycle, TME alteration and tumor-infiltrating immune cells proportions. KIF11 knockdown exerted inhibitory effects on cell proliferation, migration, and invasion. Results of the flow cytometry analysis revealed that KIF11 knockdown induced a G2/M phase arrest and improved apoptosis in LUAD cells.

Conclusions

KIF11 is essential for LUAD cell proliferation and metastasis, and it may serve as an independent prognostic factor as well as a promising therapeutic target for LUAD patients.

STAT1 is a key gene in a gene regulatory network related to immune phenotypes in bladder cancer: An integrative analysis of multi-omics data

The immunophenotype of bladder cancer plays a pivotal role in the prognosis of cancer, but the effect of different epigenetic factors on different immunophenotypes in bladder tumours remains unclear. This study used multi-omics data analysis to provide molecular basis support for different immune phenotypes. Unsupervised cluster analysis revealed distinct subclusters with higher (subcluster B2) or lower cytotoxic immune phenotypes (subcluster A1) related to PD-L1 and IFNG expression. Mutational landscape analyses showed that the mutation level of TP53 in subcluster B1 was highest than other subclusters, and subcluster B1 had a lower frequency of concurrent mutation than subcluster A2. A total of 2364 differentially expressed genes were identified between subclusters A2 and B1, and the main functions of the up-regulated genes in subcluster B1 were enriched in the activation of T cells and other related pathways. We found that STAT1 was a key gene in a gene regulatory network related to immune phenotypes in bladder cancer. Finally, we constructed a prognostic prediction model by LASSO Cox regression which could distinguish high-risk and low-risk cases significantly. In conclusion, the present study addressed a field synopsis between genetic and epigenetic events in immune phenotypes of bladder cancer.

Screening and Identification of Prognostic Tumor-Infiltrating Immune Cells and Genes of Endometrioid Endometrial Adenocarcinoma: Based on The Cancer Genome Atlas Database and Bioinformatics

Background

Endometrioid endometrial adenocarcinoma (EEA) is one of the most common tumors in the female reproductive system. With the further understanding of immune regulation mechanism in tumor microenvironment, immunotherapy is emerging in tumor treatment. However, there are few systematic studies on EEA immune infiltration.

Methods

In this study, prognostic tumor-infiltrating immune cells (TIICs) and related genes of EEA were comprehensively analyzed for the first time through the bioinformatics method with CIBERSORT algorithm as the core. Gene expression profile data were downloaded from the TCGA database, and the abundance ratio of TIICs was obtained. Kaplan-Meier analysis and Cox regression analysis were used to identify prognostic TIICs. EEA samples were grouped according to the risk score in Cox regression model. Differential analysis and functional enrichment analyses were performed on high- and low-risk groups to find survival-related hub genes, which were verified by Tumor Immune Estimation Resource (TIMER).

Result

Four TIICs including memory CD4+ T cells, regulatory T cells, natural killer cells and dendritic cells were identified. And two hub gene modules were found, in which six hub genes including APOL1, CCL17, RBP4, KRT15, KRT71, and KRT79 were significantly related to overall survival and were closely correlated with some certain TIICs in the validation of TIMER.

Conclusion

In this study, four prognostic TIICs and six hub genes were found to be closely related to EEA. These findings provided new potential targets for EEA immunotherapy.

Identification of Immune Cell Infiltration and Immune-Related Genes in the Tumor Microenvironment of Glioblastomas

Glioblastoma (GBM) is one of the most prevalent malignant brain tumors with poor prognosis. Increasing evidence has revealed that infiltrating immune cells and other stromal components in the tumor microenvironment (TME) are associated with prognosis of GBM. The aim of the present study was to identify immune cells and immune-related genes extracted from TME in GBM. RNA-sequencing and clinical data of GBM were downloaded from The Cancer Genome Atlas (TCGA). Four survival-related immune cells were identified via Kaplan-Meier survival analysis and immune-related differentially expressed genes (DEGs) screened. Functional enrichment and protein-protein interaction (PPI) networks for the genes were constructed. In addition, we identified 24 hub genes and the expressions of 6 of the genes were significantly associated with prognosis of GBM. Finally, the genes were validated in single-cell sequencing studies of GBM, and the immune cells validated in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Overall, 24 immune-related genes infiltrating the tumor microenvironment were identified in the present study, which could serve as novel biomarkers and immune therapeutic targets.

Prognostic significance of tumor-infiltrating immune cells in muscle-invasive bladder cancer

BACKGROUND

Muscle-invasive bladder cancer (MIBC) is a lethal disease with poor treatment response and a high death rate. Immune cells infiltrating the tumor tissues have been shown to play a vital role in tumorigenesis and tumor progression, but their prognostic significance in MIBC remains unclear.

OBJECTIVES

To explore the landscape and prognostic significance of tumor-infiltrating immune cells (TIICs) in MIBC, and to develop a model to improve the prognostic predictions of MIBC.

METHODS AND MATERIALS

The gene expression profile and clinical data of MIBC patients were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas portal. The fractions of 22 TIIC subtypes were calculated using the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. A TIICs-based model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in a training cohort and validated in the validation cohort.

RESULTS

Ten types of TIICs demonstrated different infiltration abundance between MIBC and normal tissue. We also found 11 types of TIICs that were significantly associated with overall survival (OS). A TIICs-based model was established, consisting of 15 types of immune cells, and an immunoscore was calculated. Significant differences in OS were found between the high and low immunoscore groups, in both training (n = 343) and validation (n = 146) cohorts. The model could identify patients who would have worse OS despite having similar clinical characteristics. Furthermore, multivariate analysis identified the immunoscore as an independent risk factor (hazard ratio, 3.23; 95% confidence interval; 2.22-4.70) for OS in MIBC patients.

CONCLUSION

The landscape of immune infiltration is different between MIBC and normal tissue. The TIICs-based model could provide promising predictive value to complement the existing staging system for predicting the OS of MIBC patients.

LCP1 is a prognostic biomarker correlated with immune infiltrates in gastric cancer

BACKGROUND

Previous studies have identified LCP1 as a diagnostic and prognostic marker in several cancers. However, the role of LCP1 in gastric cancer (GC) and its effect on tumor immune infiltration remain unclear.

OBJECTIVE

The aim was to explore the role of LCP1 in GC and its effect on tumor immune infiltration.

METHODS

We explored the expression of LCP1 relative to clinicopathology in GC patients by bioinformatics analysis and immunohistochemistry. Using cBioportal database, we analyzed the characteristic genetic variations of LCP1 in GC. In addition, we evaluated the correlation between LCP1 expression and tumor-infiltrating lymphocytes (TILs) using R software, TIMER and TISIDB databases. Finally, we analyzed the biological functions in which LCP1 may participate and the signaling pathways it may regulate.

RESULTS

Here, we showed that LCP1 expression is significantly correlated with tumor aggressiveness and poor prognosis in GC patients. Additionally, the results indicated that LCP1 was associated with TILs, including both immunosuppressive and immunosupportive cells, and was strongly correlated with various immune marker sets in GC. GSEA analysis demonstrated that LCP1 expression played an important role in lymphocyte formation and immune reaction.

CONCLUSIONS

LCP1 may be a potential prognostic biomarker for GC patients and a marker for tumor immunotherapy.

Prognostic landscape of tumor-infiltrating immune cells and immune-related genes in the tumor microenvironment of gastric cancer

The tumor microenvironment is closely related to the progression and immune escape of tumor cells. Tumor-infiltrating immune cells (TIICs) and immune-related genes (IRGs) are indispensable components of the tumor microenvironment and have been demonstrated to be highly valuable in determining the prognosis of multiple cancers. To elucidate the prognostic value of TIICs and IRGs in gastric cancer, we conducted a comprehensive analysis focusing on the abundances of 22 types of TIICs and differentially expressed IRGs based on a dataset from The Cancer Genome Atlas (TCGA). The results showed that great composition differences in TIICs and immune cell subfractions were associated with survival outcomes in different stages. Additionally, 29 hub genes were characterized from 345 differentially expressed IRGs and found to be significantly associated with survival outcomes. Then, an independent prognostic indicator based on ten IRGs was successfully constructed after multivariate adjustment for some clinical parameters. Further validation revealed that these hub IRGs could reflect the infiltration levels of immune cells. Thus, our results confirmed the clinical significance of TIICs and IRGs in gastric cancer and may establish a foundation for further exploring immune cell and gene targets for personalized treatment.

Identification of the Prognostic Value of Immune-Related Genes in Esophageal Cancer

Esophageal cancer (EC) is a serious malignant tumor, both in terms of mortality and prognosis, and immune-related genes (IRGs) are key contributors to its development. In recent years, immunotherapy for tumors has been widely studied, but a practical prognostic model based on immune-related genes (IRGs) in EC has not been established and reported. This study aimed to develop an immunogenomic risk score for predicting survival outcomes among EC patients. In this study, we downloaded the transcriptome profiling data and matched clinical data of EC patients from The Cancer Genome Atlas (TCGA) database and found 4,094 differentially expressed genes (DEGs) between EC and normal esophageal tissue (p < 0.05 and fold change >2). Then, the intersection of DEGs and the immune genes in the “ImmPort” database resulted in 303 differentially expressed immune-related genes (DEIRGs). Next, through univariate Cox regression analysis of DEIRGs, we obtained 17 immune genes related to prognosis. We detected nine optimal survival-associated IRGs (HSPA6, CACYBP, DKK1, EGF, FGF19, GAST, OSM, ANGPTL3, NR2F2) by using Lasso regression and multivariate Cox regression analyses. Finally, we used those survival-associated IRGs to construct a risk model to predict the prognosis of EC patients. This model could accurately predict overall survival in EC and could be used as a classifier for the evaluation of low-risk and high-risk groups. In conclusion, we identified a practical and robust nine-gene prognostic model based on immune gene dataset. These genes may provide valuable biomarkers and prognostic predictors for EC patients and could be further studied to help understand the mechanism of EC occurrence and development.

Identification of prognostic biomarkers associated with stromal cell infiltration in muscle-invasive bladder cancer by bioinformatics analyses

Muscle-invasive bladder cancer (MIBC) is one of the common malignant tumors. Patients with MIBC still have high tumor recurrence and progression rates after surgery. Bioinformatics analysis of stromal infiltration-related genes in the tumor microenvironment (TME) of MIBC patients was performed in this study to determine the major stromal cells types and biomarkers for their progression and poor prognosis. The ESTIMATE algorithm was applied to evaluate the stromal score and immune score of samples from MIBC patients in The Cancer Genome Atlas (TCGA) and found that stromal score was closely related to the clinical characteristics of the patients. The Gene Set Enrichment Analysis (GSEA) further revealed that stromal cells were involved in biological processes such as activation of leukocytes and positive regulation of cell migration during MIBC progression, as well as PI3K-Akt, MAPK, and Rap1 signaling pathways. Five hub genes related to prognosis, including ACTA2, COL5A1, DCN, LUM, and PRRX1 were identified by the Weighted Gene Co-Expression Network Analysis (WGCNA), Protein-Protein Interaction (PPI), survival analysis, and Oncomine, Gene Expression Omnibus (GEO) database validation. Besides, we identified five stromal cell types associated with overall survival time, among which chondrocytes and fibroblasts were identified as the major stromal cell types through correlation analysis. Finally, the Receiver Operating Characteristic (ROC) curve and immunohistochemistry were used to verify the diagnostic value and expression of hub genes in different invasive tumors. In summary, we investigated the biological behavior of stromal cells in the TME of MIBC to promote tumor progression obtained hub genes associated with progression and poor prognosis and identified the main stromal cells types in the TME.

Redefining Tumor-Associated Macrophage Subpopulations and Functions in the Tumor Microenvironment

The immunosuppressive status of the tumor microenvironment (TME) remains poorly defined due to a lack of understanding regarding the function of tumor-associated macrophages (TAMs), which are abundant in the TME. TAMs are crucial drivers of tumor progression, metastasis, and resistance to therapy. Intra- and inter-tumoral spatial heterogeneities are potential keys to understanding the relationships between subpopulations of TAMs and their functions. Antitumor M1-like and pro-tumor M2-like TAMs coexist within tumors, and the opposing effects of these M1/M2 subpopulations on tumors directly impact current strategies to improve antitumor immune responses. Recent studies have found significant differences among monocytes or macrophages from distinct tumors, and other investigations have explored the existence of diverse TAM subsets at the molecular level. In this review, we discuss emerging evidence highlighting the redefinition of TAM subpopulations and functions in the TME and the possibility of separating macrophage subsets with distinct functions into antitumor M1-like and pro-tumor M2-like TAMs during the development of tumors. Such redefinition may relate to the differential cellular origin and monocyte and macrophage plasticity or heterogeneity of TAMs, which all potentially impact macrophage biomarkers and our understanding of how the phenotypes of TAMs are dictated by their ontogeny, activation status, and localization. Therefore, the detailed landscape of TAMs must be deciphered with the integration of new technologies, such as multiplexed immunohistochemistry (mIHC), mass cytometry by time-of-flight (CyTOF), single-cell RNA-seq (scRNA-seq), spatial transcriptomics, and systems biology approaches, for analyses of the TME.

Identification of Immune-Related Prognostic Biomarkers Based on the Tumor Microenvironment in 20 Malignant Tumor Types With Poor Prognosis

Cancer, especially malignant tumors with poor prognosis, has become a major hazard to human life and health. The tumor microenvironment is gaining increasing attention from researchers, as it offers a new focus for tumor diagnosis, therapy, and prognosis. The numbers of immune and stromal cells, which are major components of the tumor microenvironment, could be determined from RNA-seq data with the Estimation of STromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. To explore the effects of immune and stromal cells on tumor prognosis, we analyzed associations between overall survival and immune/stromal scores for 20 malignant tumor types based on The Cancer Genome Atlas (TCGA) data. For six of the 20 tumor types, we observed statistically significant associations. Furthermore, to better explain the predictive ability of these scores, differentially expressed genes (DEGs) were identified in groups of cases with high or low immune or stromal scores for each of these six malignant tumor types. In addition, a list of immune-related genes was screened to identify prognostic predictors for one or more tumor types. Thus, multi-database joint analysis can provide a new approach to the assessment of tumor prognosis and allow the identification of related genes that may be new biomarkers for tumor metastasis and prognosis.

Identification of an immune-related long non-coding RNA signature and nomogram as prognostic target for muscle-invasive bladder cancer

To identify an immune-related prognostic signature based on long non-coding RNAs (lncRNAs) and find immunotherapeutic targets for bladder urothelial carcinoma, we downloaded RNA-sequencing data from The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis demonstrated bladder urothelial carcinoma was related to immune-related functions. We obtained 332 immune-related genes and 262 lncRNAs targeting immune-related genes. We constructed a signature based on eight lncRNAs in training cohort. Patients were classified as high-risk and low-risk according to signature risk score. High-risk patients had poor overall survival compared with low-risk patients (P < 0.001). Multivariate Cox regression suggested the signature was an independent prognostic indicator. The findings were further validated in testing, entire TCGA and external validation cohorts. Gene set enrichment analysis indicated significant enrichment of immune-related phenotype in high-risk group. Immunohistochemistry and online analyses validated the functions of 4 key immune-related genes (LIG1, TBX1, CTSG and CXCL12) in bladder urothelial carcinoma. Nomogram proved to be a good classifier for muscle-invasive bladder cancer through combining the signature. In conclusion, our immune-related prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for muscle-invasive bladder cancer.