Identification of Four Immune Subtypes in Bladder Cancer Based on Immune Gene Sets

Causal association between skin cancer and immune cells: mendelian randomization (MR) study

BACKGROUND

Numerous meta-analyses and clinical studies have shown that subtypes of immune cells are associated with the development of skin cancer, but it is not clear whether this association is causal or biased. Mendelian randomization (MR) analysis reduces the effect of confounding factors and improves the accuracy of the results when compared to traditional studies. Thus, in order to examine the causal relationship between various immune cell and skin cancer, this study employs two-sample MR.

METHODS

This study assesses the causal association between 731 immune cell characteristics and skin cancer using a two-sample Mendel randomization (MR) methodology. Multiple MR methods were used to bias and to derive reliable estimates of causality between instrumental variables and outcomes. Comprehensive sensitivity analyses were used to validate the stability, heterogeneity and horizontal multiplicity of the results.

RESULTS

We discovered that potential causal relationships between different types of immune cells and skin cancer disease. Specifically, one type of immune cell as potentially causal to malignant melanoma of skin (MM), eight different types of immune cells as potentially causal to basal cell carcinoma (BCC), four different types of immune cells as potentially causal to actinic keratosis (AK), and no different types of immune cells were found to have a potential causal association with squamous cell carcinoma(SCC), with stability in all of the results.

CONCLUSION

This study demonstrates the close connection between immune cells and skin cancer disease by genetic means, which enriches the current knowledge about the role of immune cells in skin cancer and also contributes to the design of therapeutic strategies from an immunological perspective.

Validation of CDC45 as a novel biomarker for diagnosis and prognosis of gastric cancer

Background

Cell division cycle protein 45 (CDC45) has been demonstrated to play vital roles in the progression of various malignancies. However, the clinical significance of CDC45 in gastric cancer (GC) remains unreported.

Method

In this study, we employed the TCGA database and the TCGA & GTEx dataset to compare the mRNA expression levels of CDC45 between gastric cancer tissues and adjacent or normal tissues (p < 0.05 was considered statistically significant), which was further validated in multiple datasets including GSE13911, GSE29272, GSE118916, GSE66229, as well as RT-qPCR. Furthermore, we harnessed the Human Protein Atlas (HPA) to evaluate the protein expression of CDC45, which was subsequently verified through immunohistochemistry (IHC). To ascertain the diagnostic utility of CDC45, receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were calculated in TCGA database, and further validated it in TCGA & GTEx and GSE66229 datasets. The Kaplan-Meier method was used to reveal the prognostic importance of CDC45 in The Cancer Genome Atlas (TCGA) database and authenticated through the GSE66229, GSE84433, and GSE84437 datasets. Through cBioPortal, we identified co-expressed genes of CDC45, and pursued enrichment analysis. Additionally, we availed gene set enrichment analysis (GSEA) to annotate the biological functions of CDC45.

Results

Differential expression analysis revealed that CDC45 was significantly upregulated at both the mRNA and protein levels in GC (all p < 0.05). Remarkably, CDC45 emerged as a promising prognostic indicator and a novel diagnostic biomarker for GC. In a comprehensive the drug susceptibility analysis, we found that patients with high expression of CDC45 had high sensitivity to various chemotherapeutic agents, among which 5-fluorouracil, docetaxel, cisplatin, and elesclomol were most evident. Furthermore, our findings suggested a plausible association between CDC45 and immune cell infiltration. Enrichment analysis revealed that CDC45 and its associated genes may play crucial roles in muscle biofunction, whereas GSEA demonstrated significant enrichment of gene sets pertaining to G protein-coupled receptor ligand binding and G alpha (i) signaling events.

Conclusion

Our study elucidates that upregulation of CDC45 is intricately associated with immune cell infiltration and holds promising potential as a favorable prognostic marker and a novel diagnostic biomarker for GC.

Predictive Panel for Immunotherapy in Low-Grade Glioma

BACKGROUND

The main treatment of low-grade glioma (LGG) is still surgical resection followed by radiotherapy and/or chemotherapy, which has certain limitations, including side effects and drug resistance. Immunotherapy is a promising treatment for LGG, but it is generally hindered by the tumor microenvironment with the limited expression of tumor antigens.

METHODS

We integrated RNA sequencing data sets and clinical information and conducted consistent cluster analysis to explore the most suitable patients for immune checkpoint therapy. Gene set enrichment analysis, UMAP analysis, mutation correlation analysis, TIMER analysis, and TIDE analysis were used to identify the immune characteristics of 3 immune subtypes and the feasibility of 5 antigens as immune checkpoint markers.

RESULTS

We analyzed the isolation and mutation of homologous recombination repair genes (HRR) of the 3 immune subtypes, and the HRR genes of the 3 subtypes were obviously segregated. Among them, the IS2 subtype has a large number of HRR gene mutations, which increases the immunogenicity of tumors-this is consistent with the results of tumor mutation load analysis of 3 immune subtypes. Then we evaluated the immune cell infiltration of immune subtypes and found that IS2 and IS3 subtypes were rich in immune cells. It is worth noting that there are many Treg cells and NK cells in the IS1 subtype. In addition, when analyzing the immune checkpoint gene expression of the 3 subtypes, we found that they were upregulated most in IS2 subtypes compared with other subtypes. Then when we further confirmed the role of immune-related genes in LGG; through TIDE analysis and TISIDB analysis, we obtained 5 markers that can predict the efficacy of ICB in patients with LGG. In addition, we confirmed that they were associated with poor prognosis through survival analysis.

CONCLUSIONS

We obtained 3 reliable immune subtypes, and patients with the IS2 subtype are suitable for immunotherapy, in which NAMPT, SLC11A1, TNC, VIM, and SPP1 are predictive panel markers for ICB in the LGG group. Our findings provide a rationale for immunotherapy selection and prediction of patient prognosis in LGG patients.

Construction and validation of a novel lysosomal signature for hepatocellular carcinoma prognosis, diagnosis, and therapeutic decision-making

Lysosomes is a well-recognized oncogenic driver and chemoresistance across variable cancer types, and has been associated with tumor invasiveness, metastasis, and poor prognosis. However, the significance of lysosomes in hepatocellular carcinoma (HCC) is not well understood. Lysosomes-related genes (LRGs) were downloaded from Genome Enrichment Analysis (GSEA) databases. Lysosome-related risk score (LRRS), including eight LRGs, was constructed via expression difference analysis (DEGs), univariate and LASSO-penalized Cox regression algorithm based on the TCGA cohort, while the ICGC cohort was obtained for signature validation. Based on GSE149614 Single-cell RNA sequencing data, model gene expression and liver tumor niche were further analyzed. Moreover, the functional enrichments, tumor microenvironment (TME), and genomic variation landscape between LRRSlow/LRRShigh subgroup were systematically investigated. A total of 15 Lysosomes-related differentially expressed genes (DELRGs) in HCC were detected, and then 10 prognosis DELRGs were screened out. Finally, the 8 optimal DELRGs (CLN3, GBA, CTSA, BSG, APLN, SORT1, ANXA2, and LAPTM4B) were selected to construct the LRRS prognosis signature of HCC. LRRS was considered as an independent prognostic factor and was associated with advanced clinicopathological features. LRRS also proved to be a potential marker for HCC diagnosis, especially for early-stage HCC. Then, a nomogram integrating the LRRS and clinical parameters was set up displaying great prognostic predictive performance. Moreover, patients with high LRRS showed higher tumor stemness, higher heterogeneity, and higher genomic alteration status than those in the low LRRS group and enriched in metabolism-related pathways, suggesting its underlying role in the progression and development of liver cancer. Meanwhile, the LRRS can affect the proportion of immunosuppressive cell infiltration, making it a vital immunosuppressive factor in the tumor microenvironment. Additionally, HCC patients with low LRRS were more sensitive to immunotherapy, while patients in the high LRRS group responded better to chemotherapy. Upon single-cell RNA sequencing, CLN3, GBA, and LAPTM4B were found to be specially expressed in hepatocytes, where they promoted cell progression. Finally, RT-qPCR and external datasets confirmed the mRNA expression levels of model genes. This study provided a direct links between LRRS signature and clinical characteristics, tumor microenvironment, and clinical drug-response, highlighting the critical role of lysosome in the development and treatment resistance of liver cancer, providing valuable insights into the prognosis prediction and treatment response of HCC, thereby providing valuable insights into prognostic prediction, early diagnosis, and therapeutic response of HCC.

Inhibition of FGFR3 upregulates MHC-I and PD-L1 via TLR3/NF-kB pathway in muscle-invasive bladder cancer

BACKGROUND

Improving the potency of immune response is paramount among issues concerning immunotherapy of muscle-invasive bladder cancer (MIBC).

METHODS

On the basis of immune subtypes, we investigated possible molecular mechanisms involved in tumor immune escape in MIBC. According to the 312 immune-related genes, three MIBC immune subtypes were clustered.

RESULTS

Cluster 2 subtype is characterized by FGFR3 mutations and has a better clinical prognosis. However, the expression levels of MHC-I and immune checkpoints genes were the lowest, indicating that this subtype is subject to immune escape and has a low response rate to immunotherapy. Bioinformatics analysis and immunofluorescence staining of clinical samples revealed that the FGFR3 is involved in the immune escape in MIBC. Besides, after FGFR3 knockout with siRNA in RT112 and UMUC14 cells, the TLR3/NF-kB pathway was significantly activated and was accompanied by upregulation of MHC-I and PD-L1 gene expression. Furthermore, the use of TLR3 agonists poly(I:C) can further improve the effect.

CONCLUSION

Together, our results suggest that FGFR3 might involve in immunosuppression by inhibition of NF-kB pathway in BC. Considering that TLR3 agonists are currently approved for clinical treatment as immunoadjuvants, our study might provide more insights for improving the efficacy of immunotherapy in MIBC.

Primary intracranial sarcomas: a clinicopathological investigation

Background

The purpose of this study is to present a series of primary intracranial sarcomas (PIS), a rare type of tumor of the central nervous system, in order to improve our understanding of the disease. These tumors are heterogeneous and prone to recurrence after resection, exhibiting a high mortality rate. As PIS has yet to be understood and studied on a large scale, it is vital for further evaluation and research.

Methods

Our study included 14 cases of PIS. The patients' clinical, pathological, and imaging features were retrospectively analyzed. Additionally, targeted DNA next-generation sequencing (NGS) was applied for the 481-gene panel to detect gene mutations.

Results

The average age for PIS patients was 31.4 years. Headache (7, 50.0%) was the most common symptom leading to the hospital visit. Twelve cases had PIS located in the supratentorial area and two in the cerebellopontine angle region. The maximum tumor diameter ranged from 19.0 mm to 130.0 mm, with an average diameter of 50.3 mm. Pathological types of tumors were heterogeneous, with chondrosarcoma being the most common, followed by fibrosarcoma. Eight of the 10 PIS cases that underwent MRI scanning showed gadolinium enhancement; 7 of these cases were heterogeneous, and 1 of them was garland-like. Targeted sequencing was performed in two cases and identified mutations in genes such as NRAS, PIK3CA, BAP1, KDR, BLM, PBRM1, TOP2A, DUSP2, and CNV deletions of SMARCB1. Additionally, the SH3BP5::RAF1 fusion gene was also detected. Of the 14 patients, 9 underwent a gross total resection (GTR), and 5 chose subtotal resection. Patients who underwent GTR displayed a trend toward superior survival. Among the 11 patients with available follow-up information, one had developed lung metastases, three had died, and eight were alive.

Conclusion

PIS is extremely rare compared to extracranial soft sarcomas. The most common histological type of intracranial sarcoma (IS) is chondrosarcoma. Patients who underwent GTR of these lesions showed improved survival rates. Recent advancements in NGS aided in the identification of diagnostic and therapeutic PIS-relevant targets.

Distinct tumor microenvironment landscapes of rectal cancer for prognosis and prediction of immunotherapy response

PURPOSE

Tumor microenvironment (TME) affects the progression of rectal cancer (RC), and the clinical relevance of its immune elements was widely reported. Here we aim to delineate the complete TME landscape, including non-immune features, to improve our understanding of RC heterogeneity and provide a better strategy for precision medicine.

METHODS

Single-cell analysis of GSE161277 using Seurat and Cellcall was performed to identify cell-cell interactions. The ssGSEA was employed to quantify the TME elements in TCGA patients, which were further clustered into subtypes by hclust. WGCNA and LASSO were combined to construct a degenerated signature for prognosis, and its performance was validated in two GEO datasets.

RESULTS

We proposed a subtyping strategy based on the abundance of both immune and non-immune components, which divided all RC patients into 4 subtypes (Immune-, Canonical-, Dormant- and Stem-like). Different subtypes exhibited distinct mutation landscapes, biological features, immune characteristics, immunotherapy responses and prognoses. Next, WGCNA and LASSO regression were combined to construct a 10-gene signature based on differentially expressed genes among different subtypes. Subgroups divided by this signature also exhibited different clinical parameters and responses to immune checkpoint blockades. Diverse machine learning algorithms were applied to achieve higher accuracy for survival prediction and a nomogram was further established in combination with M stage and age to provide an accurate and visual prediction of prognosis.

CONCLUSIONS

We identified four TME-based RC subtypes with distinct biological and clinical features. Based on those subtypes, we also proposed a degenerated 10-gene signature to predict the prognosis and immunotherapy response.

A Robust Prognostic Signature of Tumor Microenvironment in Colorectal Cancer

Background: Colorectal cancer (CRC) has been a major public health problem. Tumor microenvironment (TME) greatly contributes to the heterogeneity of CRC and is crucial for the regulation of CRC progression. The authors' study aimed to develop a robust prognostic signature for CRC patients based on TME-related genes. Materials and Methods: Gene expression data and clinicopathologic information of CRC patients were collected from Gene Expression Omnibus and The Cancer Genome Atlas databases. TME-related genes with prognostic value were identified by Cox regression and bootstrap method. The authors used the prognostic genes to construct a robust prognostic model using the least absolute shrinkage and selection operator (LASSO) regression method. The immune and stromal cell abundance of CRC samples were estimated by a microenvironment cell populations-counter method. Results: Based on a training set that comprised 893 CRC samples and 4775 TME-related genes, they established a prognostic model consisting of 25 TME-related genes. With specific risk score formulae, the prognostic model divided CRC patients into high-risk and low-risk subgroups with significantly different survival, which were further confirmed in validation cohorts consisting of other 473 CRC cases or subpopulation of specific stages. The result of time-dependent receiver operating characteristic analysis demonstrated strong predictive accuracy of the prognostic model both in training and validation cohorts. Multivariate Cox regression analysis showed that the 25-gene signature was an independent prognostic factor for overall survival, which was validated through clinical subgroups analysis. Further analysis revealed that CRC samples of high-risk group was abundant of stromal-relevant processes and had a significantly higher proportion of fibroblasts and endothelial cells infiltration. Conclusion: The authors established a robust prognostic signature of 25 TME-related genes which may be an effective tool for prognostic prediction and CRC patient stratification to assist in making treatment decisions.

Molecular Taxonomy and Immune Checkpoint Therapy in Bladder Cancer

Bladder cancer is a heterogeneous disease, which exhibits a wide spectrum of clinical and pathologic features. Recent genomic studies have revealed that distinct molecular alterations may underlie the diverse clinical behaviors of bladder cancer, leading to a novel molecular classification. The intrinsic molecular subtypes exhibit distinct gene expression signatures and different clinicopathologic features. Genomic alterations also underlie the development of bladder cancer histologic subtypes. Genomic characterization provides new insights to understanding the biology of bladder cancer and improves the diagnosis and treatment of this complex disease. Biomarkers can aid the selection of patients for immune checkpoint therapy.

Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance

Background: Previous studies have suggested that patients with lung adenocarcinoma (LUAD) will significantly benefit from epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). However, many LUAD patients will develop resistance to EGFR-TKI. Thus, our study aims to develop models to predict EGFR-TKI resistance and the LUAD prognosis. Methods: Two Gene Expression Omnibus (GEO) datasets (GSE31625 and GSE34228) were used as the discovery datasets to find the common differentially expressed genes (DEGs) in EGFR-TKI resistant LUAD profiles. The association of these common DEGs with LUAD prognosis was investigated in The Cancer Genome Atlas (TCGA) database. Moreover, we constructed the risk score for prognosis prediction of LUAD by LASSO analysis. The performance of the risk score for predicting LUAD prognosis was calculated using an independent dataset (GSE37745). A random forest model by risk score genes was trained in the training dataset, and the diagnostic ability for distinguishing sensitive and EGFR-TKI resistant samples was validated in the internal testing dataset and external testing datasets (GSE122005, GSE80344, and GSE123066). Results: From the discovery datasets, 267 common upregulated genes and 374 common downregulated genes were identified. Among these common DEGs, there were 59 genes negatively associated with prognosis, while 21 genes exhibited positive correlations with prognosis. Eight genes (ABCC2, ARL2BP, DKK1, FUT1, LRFN4, PYGL, SMNDC1, and SNAI2) were selected to construct the risk score signature. In both the discovery and independent validation datasets, LUAD patients with the higher risk score had a poorer prognosis. The nomogram based on risk score showed good performance in prognosis prediction with a C-index of 0.77. The expression levels of ABCC2, ARL2BP, DKK1, LRFN4, PYGL, SMNDC1, and SNAI2 were positively related to the resistance of EGFR-TKI. However, the expression level of FUT1 was favorably correlated with EGFR-TKI responsiveness. The RF model worked wonderfully for distinguishing sensitive and resistant EGFR-TKI samples in the internal and external testing datasets, with predictive area under the curves (AUC) of 0.973 and 0.817, respectively. Conclusion: Our investigation revealed eight genes associated with EGFR-TKI resistance and provided models for EGFR-TKI resistance and prognosis prediction in LUAD patients.

Gene set-based identification of two immune subtypes of diffuse large B cell lymphoma for guiding immune checkpoint blocking therapy

Background: Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Tumour microenvironment is closely related to tumour prognosis and immune checkpoint blocking therapy (ICBT). This study aimed to investigate the immunological and prognostic characteristics of the tumour microenvironment (TME), as well as the regulatory mechanisms.

Methods: Gene expression profiles and clinical data of patients with DLBCL were obtained from GEO database. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to explore microenvironment characteristics and regulatory mechanism of the immune subtypes, which were identified by consistent clustering. The differences in enriched pathways were showed by GSEA. Hub genes associated with CD8⁺ T cells, which were identified by WCGNA, were exhibited biological functions through GO and KEGG. Immune-related gene scores (IRGSs) based on hub genes were used to evaluate the prediction of immune subtypes and ICBT, and retrospective analysis was used for validation. Finally, prognostic genes were screened to construct risk models.

Results: Consensus clustering divided patients with DLBCL into two subtypes with significant heterogeneities in prognosis and immune microenvironment. Low immune infiltration was associated with poor prognosis. Subtype C1 with high immune infiltration was enriched in multiple immune pathways. We observed that two common mutated genes (B2M and EZH2) in DLBCL were closely related to MHC-I and microenvironment. Our further analysis manifested that MYD88^L265P may be the main cause of TLR signalling pathway activation in subtype C1. Hub genes (SH2D1A, CD8A, GBP2, ITK, CD3D, RORA, IL1R2, CD28, CD247, CD3G, PRKCQ, CXCR6, and CD3E) in relation with CD8⁺ T cells were used to establish IRGS, which was proved an accurate predictor of immune subtypes, and patients in high-IRGS group were more likely to benefit from ICBT. Retrospective analysis showed that absolute lymphocyte count (ALC) was higher in the group that responded to the PD-1 inhibitor. Finally, the risk model was constructed based on two genes (CD3G and CD3D), and the low-risk group showed better prognosis.

Conclusion: DLBCL immune classifications with highly heterogeneity are a powerful predictor of prognosis and ICBT. The IRGS is proved to be a reliable tool to distinguish immune subtypes as a substitute for gene expression profile.

Immune landscape of distinct subtypes in urothelial carcinoma based on immune gene profile

Immune checkpoint blockade (ICB) has become a promising therapy for multiple cancers. However, only a small proportion of patients display a limited antitumor response. The present study aimed to classify distinct immune subtypes and investigate the tumor microenvironment (TME) of urothelial carcinoma, which may help to understand treatment failure and improve the immunotherapy response. RNA-seq data and clinical parameters were obtained from TCGA-BLCA, E-MTAB-4321, and IMVigor210 datasets. A consensus cluster method was used to distinguish different immune subtypes of patients. Infiltrating immune cells, TME signatures, immune checkpoints, and immunogenic cell death modulators were evaluated in distinct immune subtypes. Dimension reduction analysis was performed to visualize the immune status of urothelial carcinoma based on graph learning. Weighted gene co-expression network analysis (WGCNA) was performed to obtain hub genes to predict responses after immunotherapy. Patients with urothelial carcinoma were classified into four distinct immune subtypes (C1, C2, C3 and C4) with various types of molecular expression, immune cell infiltration, and clinical characteristics. Patients with the C3 immune subtype displayed abundant immune cell infiltrations in the tumor microenvironment and were typically identified as “hot” tumor phenotypes, whereas those with the C4 immune subtype with few immune cell infiltrations were identified as “cold” tumor phenotypes. The immune-related and metastasis-related signaling pathways were enriched in the C3 subtype compared to the C4 subtype. In addition, tumor mutation burden, inhibitory immune checkpoints, and immunogenic cell death modulators were highly expressed in the C3 subtype. Furthermore, patients with the C4 subtype had a better probability of overall survival than patients with the C3 subtype in TCGA-BLCA and E-MTAB-4321 cohorts. Patients with the C1 subtype had the best prognosis when undergoing anti-PD-L1 antibody treatment. Finally, the immune landscape of urothelial carcinoma showed the immune status in each patient, and TGFB3 was identified as a potential biomarker for the prediction of immunotherapy resistance after anti-PD-L1 monoclonal antibody treatment. The present study provided a bioinformatics basis for understanding the immune landscape of the tumor microenvironment of urothelial carcinoma.

Classification of Muscle Invasive Bladder Cancer to Predict Prognosis of Patients Treated with Immunotherapy

Background

Recently, immunotherapies have been approved for advanced muscle invasive bladder cancer (MIBC) treatment, but only a small fraction of MIBC patients could achieve a durable drug response. Our study is aimed at identifying tumor microenvironment (TME) subtypes that have different immunotherapy response rates.

Methods

The mRNA expression profiles of MIBC samples from seven discovery datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, GSE48276, and GSE69795) were analyzed to identify TME subtypes. The identified TME subtypes were then validated by an independent dataset (TCGA-MIBC). The subtype-related biomarkers were discovered using computational analyses and then utilized to establish a random forest predictive model. The associations of TME subtypes with immunotherapy therapeutic responses were investigated in a group of patients who had been treated with immunotherapy. A prognostic index model was constructed using the subtype-related biomarkers. Two nomograms were built by the subtype-related biomarkers or the clinical parameters.

Results

Two TME subtypes, including ECM-enriched class (EC) and immune-enriched class (IC), were found. EC was associated with greater extracellular matrix (ECM) pathways, and IC was correlated with immune pathways, respectively. Overall survival was significantly greater for tumors classified as IC, whereas the EC subtype had a worse prognosis. A total of nine genes (AKAP12, APOL3, CXCL13, CXCL9, GBP4, LRIG1, PEG3, PODN, and PTPRD) were selected by computational analyses to construct the random forest model. The area under the curve (AUC) values for this model were 0.827 and 0.767 in the testing and external validation datasets, respectively. Therapeutic response rates were greater in IC patients than in EC patients (28 percent vs. 18 percent). Patients with a high prognostic index had a poorer prognosis than those with a low prognostic index. The nomogram constructed from nine genes and stage achieved a C-index of 0.71.

Conclusion

The present investigation defined two distinct TME subtypes and developed models to assess immunotherapeutic treatment outcomes.

A Panel of E2F Target Gene Signature Predicting the Prognosis of Hepatocellular Carcinoma

Hepatocellular carcinoma is one of the most malignant tumors, and the therapeutic effects of traditional treatments are poor. It is urgent to explore and identify new biomarkers and therapeutic targets to develop novel treatments which are individualized and effective. Three hallmarks, including E2F targets, G2M checkpoint and DNA repair, were collected by GSEA analysis. The panel of E2F-related gene signature consisted of five genes: HN1, KIF4A, CDCA3, CDCA8 and SSRP1. They had various mutation rates ranging from 0.8 to 5% in hepatocellular carcinoma, and patients with gene mutation had poorer prognosis. Among these genes, HN1 has the greatest mutation rate, and SSRP1 has the greatest impact on the model with a B (COX) value of 0.8842. Patients with higher expression of these genes had poorer prognosis. Kaplan-Meier curves in stratified survival analysis confirmed that patients with high risk scores had poor prognosis (p < 0.05). The results of univariate and multivariate COX survival analysis showed that risk score was closely related to the overall survival of patients with hepatocellular carcinoma. For clinical validation, we found that all the genes in the model were upregulated in hepatocellular carcinoma tissues compared to normal liver tissues, which was consistent with the previous results we obtained. Our study demonstrated that a panel of E2F target genes signature including five genes could predict the prognosis of hepatocellular carcinoma. This panel gene signature can facilitate the development of individualized and effective treatment for hepatocellular carcinoma.

The Evolution of Immune Checkpoint Inhibitors in Advanced Urothelial Carcinoma

Simple Summary

Urothelial carcinoma is an aggressive cancer with a high risk of metastatic progression. Chemotherapy plays a key role in the management of metastatic urothelial carcinoma, with, however, no possibility of cure. Immune checkpoint inhibitors have significantly improved the outcomes of patients, delaying progression of disease and improving quality of life. However, many questions remain concerning the optimal use of immunotherapy in urothelial carcinoma: When to start? Which biomarker of sensitivity/resistance to use? Which of the available options will increase the efficacy of immune checkpoint inhibitors? We review the mechanisms of immune checkpoint inhibitors as well as the current management of patients with metastatic urothelial carcinoma in the era of immunotherapy.

Abstract

Urothelial carcinoma is an aggressive cancer and development of metastases remains a challenge for clinicians. Immune checkpoint inhibitors (ICIs) are significantly improving the outcomes of patients with metastatic urothelial cancer (mUC). These agents were first used in monotherapy after failure of platinum-based chemotherapy, but different strategies explored the optimal use of ICIs in a first-line metastatic setting. The “maintenance” strategy consists of the introduction of ICIs in patients who experienced benefit from first-line chemotherapy in a metastatic setting. This allows an earlier use of ICIs, without waiting for disease progression. We review the optimal management of mUC in the era of ICIs, based on the key clinical messages arising from the pivotal trials.

RNA modification writers influence tumor microenvironment in gastric cancer and prospects of targeted drug therapy

Background: RNA adenosine modifications are crucial for regulating RNA levels. N6-methyladenosine (m6A), N1-methyladenosine (m1A), adenosine-to-inosine RNA editing, and alternative polyadenylation (APA) are four major RNA modification types. Methods: We evaluated the altered mRNA expression profiles of 27 RNA modification enzymes and compared the differences in tumor microenvironment (TME) and clinical prognosis between two RNA modification patterns using unsupervised clustering. Then, we constructed a scoring system, WM_score, and quantified the RNA modifications in patients of gastric cancer (GC), associating WM_score with TME, clinical outcomes, and effectiveness of targeted therapies. Results: RNA adenosine modifications strongly correlated with TME and could predict the degree of TME cell infiltration, genetic variation, and clinical prognosis. Two modification patterns were identified according to high and low WM_scores. Tumors in the WM_score-high subgroup were closely linked with survival advantage, CD4[Formula: see text] T-cell infiltration, high tumor mutation burden, and cell cycle signaling pathways, whereas those in the WM_score-low subgroup showed strong infiltration of inflammatory cells and poor survival. Regarding the immunotherapy response, a high WM_score showed a significant correlation with PD-L1 expression, predicting the effect of PD-L1 blockade therapy. Conclusion: The WM_scoring system could facilitate scoring and prediction of GC prognosis.

Mitophagy-Related Gene Signature for Prediction Prognosis, Immune Scenery, Mutation, and Chemotherapy Response in Pancreatic Cancer

Mitophagy is a conserved cellular process that plays a vital role in maintaining cellular homeostasis by selectively removing dysfunctional mitochondria. Notwithstanding that growing evidence suggests that mitophagy is implicated in pancreatic tumorigenesis, the effect of mitophagy-related genes on pancreatic cancer (PC) prognosis and therapeutic response remains largely unknown. In this study, we sought to construct a mitophagy-related gene signature and assessed its ability to predict the survival, immune activity, mutation status, and chemotherapy response of PC patients. During the screening process, we identified three mitophagy-related genes (PRKN, SRC, VDAC1) from The Cancer Genome Atlas (TCGA) cohort and a 3-gene signature was established. The prognostic model was validated using an International Cancer Genome Consortium (ICGC) cohort and two Gene Expression Omnibus (GEO) cohorts. According to the median risk score, PC patients were divided into high and low-risk groups, and the high-risk group correlated with worse survival in the four cohorts. The risk score was then identified as an independent prognostic predictor, and a predictive nomogram was constructed to guide clinical decision-making. Remarkably, enhanced immunosuppressive levels and higher mutation rates were observed in patients from the high-risk group, which may account for their poor survival. Furthermore, we found that high-risk patients were more sensitive to paclitaxel and erlotinib. In conclusion, a mitophagy-related gene signature is a novel prognostic model that can be used as a predictive indicator and allows prognostic stratification of PC patients.

Immune cell gene expression signatures in diffuse glioma are associated with IDH mutation status, patient outcome and malignant cell state, and highlight the importance of specific cell subsets in glioma biology

The tumor micro-environment (TME) plays an important role in various cancers, including gliomas. We estimated immune cell type-specific gene expression profiles in 3 large clinically annotated glioma datasets using CIBERSORTx and LM22/LM10 blood-based immune signatures and found that the proportions and estimated gene expression patterns of specific immune cells significantly varied according to IDH mutation status. When IDH-WT and IDH-MUT tumors were considered separately, cluster-of-cluster analyses of immune cell gene expression identified groups with distinct survival outcomes. We confirmed and extended these findings by applying a signature matrix derived from single-cell RNA-sequencing data derived from 19 glioma tumor samples to the bulk profiling data, validating findings from the LM22/LM10 results. To link immune cell signatures with outcomes in checkpoint therapy, we then showed a significant association of monocytic lineage cell gene expression clusters with patient survival and with mesenchymal gene expression scores. Integrating immune cell-based gene expression with previously described malignant cell states in glioma demonstrated that macrophage M0 abundance significantly correlated with mesenchymal state in IDH-WT gliomas, with evidence of a previously implicated role of the Oncostatin-M receptor and macrophages in the mesenchymal state. Among IDH-WT tumors that were enriched for the mesenchymal cell state, the estimated M0 macrophage expression signature coordinately also trended to a mesenchymal signature. We also examined IDH-MUT tumors stratified by 1p/19q status, showing that a mesenchymal gene expression signature the M0 macrophage fraction was enriched in IDH-MUT, non-codeleted tumors. Overall, these results highlight the biological and clinical significance of the immune cell environment related to IDH mutation status, patient prognosis and the mesenchymal state in diffuse gliomas.

Immune-Checkpoint Inhibitors in Advanced Bladder Cancer: Seize the Day

Background: In advanced bladder cancer (BCa), platinum-based chemotherapy represents the first-choice treatment. In the last ten years, immune checkpoint inhibitors (ICIs) have changed the therapeutic landscape of many solid tumors. Our review aims to summarize the main findings regarding the clinical use of ICIs in advanced BCa. Methods: We searched PubMed, Embase, and Cochrane databases, and conference abstracts from international congresses (ASCO, ESMO, ASCO GU) for clinical trials, focusing on ICIs as monotherapy and combinations in metastatic BCa. Results: 18 studies were identified. ICIs targeting PD1 (nivolumab, pembrolizumab), PD-L1 (avelumab, atezolizumab, durvalumab), and CTLA4 (ipilimumab, tremelimumab) were used. Survival outcomes have been improved by second-line ICIs, whereas first-line results are dismal. Avelumab maintenance in patients obtaining disease control with chemotherapy has achieved the highest survival rates. Conclusions: ICIs improve survival after platinum-based chemotherapy. Avelumab maintenance represents a new practice-changing treatment. The combinations of ICIs and other compounds, such as FGFR-inhibitors, antibody-drug conjugates, and anti-angiogenic drugs, represent promising therapeutic approaches. Biomarkers with predictive roles and sequencing strategies are warranted for best patient selection.

Checkpoint Inhibition in Bladder Cancer: Clinical Expectations, Current Evidence, and Proposal of Future Strategies Based on a Tumor-Specific Immunobiological Approach

Simple Summary

In contrast with other strategies, immunotherapy is a treatment aimed at empowering the patient’s immune system in order to increase immunity and the response against cancer. Recently, a new class of drugs, immune checkpoint inhibitors, has shown potential in increasing treatment chances for patients with bladder cancers, improving their survival. However, predicting the response to immune checkpoint inhibition is important, since only a group of patients develop a good response. Biomarkers to predict the response to checkpoint inhibition must identify tumors’ and patients’ specific profiles. This study reviews the current knowledge on this most relevant clinical topic, focusing on bladder cancer, going from basic science to ongoing clinical trials and available clinical evidence. Finally, a critical analysis of published data is provided, and an original panel of biomarkers, able to select the right patients for treatments, based on patient-specific immune profiling, is proposed.

Abstract

In contrast with other strategies, immunotherapy is the only treatment aimed at empowering the immune system to increase the response against tumor growth. Immunotherapy has a role in the treatment of bladder cancer (BC) due to these tumors’ high tumor mutational burden (TMB) and mostly prominent immune infiltrate. The therapy or combination has to be adjusted to the tumor’s immunobiology. Recently, a new class of immunotherapeutic agents, immune checkpoint inhibitors (ICI), has shown potential in increasing treatment chances for patients with genitourinary cancers, improving their oncological outcomes. The clinical efficacy of ICI has been shown in both the first-line treatment of cisplatin-ineligible patients, with programmed death ligand 1 (PD-L1)-positive tumors (atezolizumab, pembrolizumab), and in second-line settings, for progression after platinum-based chemotherapy (atezolizumab, pembrolizumab, and nivolumab for FDA and EMA; durvalumab and avelumab for FDA alone). Predicting the response to ICI is important since only a subset of patients undergoing ICI therapy develop a concrete and lasting response. Most of the patients require a different therapy or therapy combination to achieve tumor control. The cancer immunity cycle provides a conceptual framework to assist therapy selection. Biomarkers to predict response to ICI must identify where the cancer immunity cycle is disrupted. We reviewed the current knowledge on ICI treatment in BC, going from basic science to current data and available clinical evidence. Secondly, a critical analysis of published data is provided, and an original panel of biomarkers able to predict response to ICI treatment, based on tumor-specific immune profiling, is proposed.

Immune Checkpoint Inhibitors in Urothelial Bladder Cancer: State of the Art and Future Perspectives

Simple Summary

Urothelial bladder cancer (BC) is one of the most fatal cancers, with a dismal five-year survival rate of 5% in patients with metastatic disease. Clinically relevant benefits of immunotherapy in advanced or metastatic bladder cancer have led to Food and Drug Administration (FDA) approval of immune checkpoint inhibitors (ICIs) as second- or first-line therapy in patients unresponsive to or ineligible for standard treatment. The advantage of ICIs is being investigated in various stages of BC, either as monotherapy or in combination with other drugs. In this review we discuss the role of ICIs in BC, highlighting their current clinical application and outlining future therapeutic perspectives.

Abstract

Bladder cancer (BC) is the most common malignancy of the genitourinary tract, with high morbidity and mortality rates. Until recently, the treatment of locally advanced or metastatic urothelial BC was based on the use of chemotherapy alone. Since 2016, five immune checkpoint inhibitors (ICIs) have been approved by the Food and Drug Administration (FDA) in different settings, i.e., first-line, maintenance and second-line treatment, while several trials are still ongoing in the perioperative context. Lately, pembrolizumab, a programmed death-1 (PD-1) inhibitor, has been approved for Bacillus Calmette–Guérin (BCG)-unresponsive high-risk non-muscle invasive bladder cancer (NMIBC), using immunotherapy at an early stage of the disease. This review investigates the current state and future perspectives of immunotherapy in BC, focusing on the rationale and results of combining immunotherapy with other therapeutic strategies.

Identification of ferroptosis genes in immune infiltration and prognosis in thyroid papillary carcinoma using network analysis

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analyse the gene expression profiles of the disease and established a new model for the correlation.

METHODS

The thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 types of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated.

RESULTS

We identify two subtypes in PTC based on the expression of ferroptosis related genes, with the proportion of cluster 1 significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. The mutations of Braf and Nras are detected as the major mutations of cluster 1 and 2, respectively. Subsequent analyses of TME immune cells infiltration indicated cluster 1 is remarkably richer than cluster 2. The risk score of THCA is in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data shows that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer.

CONCLUSIONS

Our study finds seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we construct the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.

Reconstruction and Analysis of the Immune-Related LINC00987/A2M Axis in Lung Adenocarcinoma

Enhancer RNAs (eRNAs) participate in tumor growth and immune regulation through complex signaling pathways. However, the immune-related function of the eRNA-mRNA axis in lung adenocarcinoma (LUAD) is unclear. Data on the expression of eRNAs and mRNAs were downloaded from The Cancer Genome Atlas, GEO, and UCSC Xena, including LUAD, and pan-cancer clinical data and mutational information. Immune gene files were obtained from ImmLnc and ImmPort databases. Survival indices, including relapse-free and overall survival, were analyzed using the Kaplan–Meier and log-rank methods. The level of immune cell infiltration, degree of tumor hypoxia, and tumor cell stemness characteristics were quantified using the single-sample gene set enrichment analysis algorithm. The immune infiltration score and infiltration degree were evaluated using the ESTIMATE and CIBERSORT algorithms. The tumor mutation burden and microsatellite instability were examined using the Spearman test. The LUAD-associated immune-related LINC00987/A2M axis was down-regulated in most cancer types, indicating poor survival and cancer progression. Immune cell infiltration was closely related to abnormal expression of the LINC00987/A2M axis, linking its expression to a possible evaluation of sensitivity to checkpoint inhibitors and response to chemotherapy. Abnormal expression of the LINC00987/A2M axis was characterized by heterogeneity in the degree of tumor hypoxia and stemness characteristics. The abnormal distribution of immune cells in LUAD was also verified through pan-cancer analysis. Comprehensive bioinformatic analysis showed that the LINC00987/A2M axis is a functional and effective tumor suppressor and biomarker for assessing the immune microenvironment and prognostic and therapeutic evaluations of LUAD.

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.

Immune Checkpoint Inhibitors for the Treatment of Bladder Cancer

A number of immune checkpoint inhibitors (ICIs) have been approved as first-line therapy in case of cisplatin-ineligible patients or as second-line therapy for patients with metastatic urothelial carcinoma (mUC) of the bladder. About 30% of patients with mUC will respond to ICIs immunotherapy. Programmed death-ligand 1 (PD-L1) expression detected by immunohistochemistry seems to predict response to immune checkpoint inhibitors in patients with mUC as supported by the objective response rate (ORR) and overall survival (OS) associated with the response observed in most clinical trials. Pembrolizumab, an anti-PD-1 antibody, demonstrated better OS respective to chemotherapy in a randomized phase 3 study for second-line treatment of mUC. Nivolumab, a PD-1 antibody, also demonstrated an OS benefit when compared to controls. Atezolizumab, Durvalumab, and Avelumab antibodies targeting PD-L1 have also received approval as second-line treatments for mUC with durable response for more than 1 year in selected patients. Atezolizumab and Pembrolizumab also received approval for first-line treatment of patients that are ineligible for cisplatin. A focus on the utility of ICIs in the adjuvant or neoadjuvant setting, or as combination with chemotherapy, is the basis of some ongoing trials. The identification of a clinically useful biomarker, single or in association, to determine the optimal ICIs treatment for patients with mUC is very much needed as emphasized by the current literature. In this review, we examined relevant clinical trial results with ICIs in patients with mUC alone or as part of drug combinations; emphasis is also placed on the adjuvant and neoadjuvant setting. The current landscape of selected biomarkers of response to ICIs including anti-PD-L1 immunohistochemistry is also briefly reviewed.

Dysregulation of the Immune Microenvironment Contributes to Malignant Progression and Has Prognostic Value in Bladder Cancer

Objective

The malignant progression from non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC) is common and has detrimental effect on patients. We aimed to elucidate the underlying mechanisms of the malignant progression from an immunological perspective and establish a reliable signature for prognostic prediction and immunotherapeutic strategies.

Methods

The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm was applied to the GSE32894 data set to identify the different tumor-infiltrating immune cells involved in NMIBC and MIBC. Using weighted gene correlation network analysis, survival analysis and least absolute shrinkage and selection operator Cox analysis, we established an immune prognostic signature (IPS) based on 14 overall survival-associated immune genes in The Cancer Genome Atlas (TCGA). Functional enrichment analyses and nomogram were performed to explore the potential effects and prognostic performance of the IPS. Furthermore, the RNA-sequence data from our center were used to validate the expression levels of the selected immune genes in BLCA samples.

Results

Diverse proportions of macrophage subtypes were observed between NMIBC and MIBC. Patients with high risk scores had a worse prognosis than patients with low risk scores in training (TCGA) and validation data sets (GSE32894, GSE13507, and GSE48277). The IPS was a useful prognostic factor for patients treated with immunotherapy in the IMvigor210 trial. Hallmarks of multiple oncogenic pathways were significantly enriched in the high risk group. A novel nomogram model was established for prognostic predictions. The dysregulated expression of the selected immune genes between NMIBC and MIBC was also validated in BLCA samples.

Conclusion

Dysregulation of the immune microenvironment promoted the malignant progression from NMIBC to MIBC. The IPS can stratify patients into different risk groups with distinct prognoses and immunotherapeutic susceptibility, thus facilitating personalized immunotherapy.