Identification and validation of a novel signature for prediction the prognosis and immunotherapy benefit in bladder cancer

Identification of bladder cancer subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes

Immunotherapy based on immune checkpoint genes (ICGs) has recently made significant progress in the treatment of bladder cancer patients, but many patients still cannot benefit from it. In the present study, we aimed to perform a comprehensive analysis of ICGs in bladder cancer tissues with the aim of evaluating patient responsiveness to immunotherapy and prognosis. We scored ICGs in each BLCA patient from TCGA and GEO databases by using ssGSEA and selected genes that were significantly associated with ICGs scores by using the WCGNA algorithm. NMF clustering analysis was performed to identify different bladder cancer molecular subtypes based on the expression of ICGs-related genes. Based on the immune related genes differentially expressed among subgroups, we further constructed a novel stratified model containing nine genes by uni-COX regression, LASSO regression, SVM algorithm and multi-COX regression. The model and the nomogram constructed based on the model can accurately predict the prognosis of bladder cancer patients. Besides, the patients classified based on this model have large differences in sensitivity to immunotherapy and chemotherapy, which can provide a reference for individualized treatment of bladder cancer.

Identification of CNKSR1 as a biomarker for "cold" tumor microenvironment in lung adenocarcinoma: An integrative analysis based on a novel workflow

Background

Therapies targeting PD1/PD-L1 pathway have revolutionized the treatment of lung cancer. However, anti-PD1/PD-L1 therapies have proven beneficial for only a select group of lung adenocarcinoma (LUAD) patients and generally do not work for immuno-cold tumors characterized by a lack of immune cell infiltration. Identifying novel biomarkers is vital to broad therapeutic options for LUAD patients with no response to anti-PD1/PD-L1 immunotherapies.

Methods

Our study has developed a novel strategy to identify a promising biomarker that addresses the limitations of anti-PD1/PD-L1 immunotherapy in treating immunological cold tumors. We exacted LUAD RNA-seq data from the Cancer Genome Atlas database (TCGA). Using several machine learning methods, we identified the candidate biomarker. Based on the expression level of PD-L1 and the identified biomarker, samples were categorized into four groups. We further used ESTIMATE, ssGSEA, and CIBERSORT algorithms to calculate the immune infiltration level of each group. The results were validated in three independent bulk datasets and one scRNA-seq dataset. Immunohistochemistry (IHC) assessments were performed in clinical samples to further evaluate the coexpression of CNKSR1 and PD-L1, and to compare CD8 + T cell infiltration among groups.

Results

After comprehensive analyses, CNKSR1 was identified as a novel promising biomarker for immuno-cold LUAD. CNKSR1 mRNA expression levels exhibited a negative correlation with both PD-L1 mRNA expression and the extent of immune cell infiltration in LUAD. Besides, in contrast to the significant association between the expression of PD-L1 and the majority of other well-established or widely studied immune checkpoint molecules, a mutually exclusive expression pattern is observed between CNKSR1 and these molecules. The aforementioned results were consistent in validation datasets. The prognostic model built based on the CNKSR1 coexpression module also showed robust predictive performance. Additionally, IHC assessments have confirmed that the coexpression of CNKSR1 and PD-L1 is rare in LUAD samples. Notably, LUADs in the high-CNKSR1 group, characterized by high CNKSR1 but low PD- L1 expression, demonstrated reduced infiltration of CD8+ T cells.

Conclusions

In summary, CNKSR1 emerges as a promising biomarker for immune-cold LUADs, and the study into CNKSR1 modulating T-cell infiltration may lead to the identification of compensatory molecules to enhance the effectiveness of current immunotherapy for LUAD.

Exploring Retrograde Trafficking: Mechanisms and Consequences in Cancer and Disease

Retrograde trafficking (RT) orchestrates the intracellular movement of cargo from the plasma membrane, endosomes, Golgi or endoplasmic reticulum (ER)-Golgi intermediate compartment (ERGIC) in an inward/ER-directed manner. RT works as the opposing movement to anterograde trafficking (outward secretion), and the two work together to maintain cellular homeostasis. This is achieved through maintaining cell polarity, retrieving proteins responsible for anterograde trafficking and redirecting proteins that become mis-localised. However, aberrant RT can alter the correct location of key proteins, and thus inhibit or indeed change their canonical function, potentially causing disease. This review highlights the recent advances in the understanding of how upregulation, downregulation or hijacking of RT impacts the localisation of key proteins in cancer and disease to drive progression. Cargoes impacted by aberrant RT are varied amongst maladies including neurodegenerative diseases, autoimmune diseases, bacterial and viral infections (including SARS-CoV-2), and cancer. As we explore the intricacies of RT, it becomes increasingly apparent that it holds significant potential as a target for future therapies to offer more effective interventions in a wide range of pathological conditions.

A risk model based on lncRNA-miRNA-mRNA gene signature for predicting prognosis of patients with bladder cancer

OBJECTIVES

We aimed to analyze lncRNAs, miRNAs, and mRNA expression profiles of bladder cancer (BC) patients, thereby establishing a gene signature-based risk model for predicting prognosis of patients with BC.

METHODS

We downloaded the expression data of lncRNAs, miRNAs and mRNA from The Cancer Genome Atlas (TCGA) as training cohort including 19 healthy control samples and 401 BC samples. The differentially expressed RNAs (DERs) were screened using limma package, and the competing endogenous RNAs (ceRNA) regulatory network was constructed and visualized by the cytoscape. Candidate DERs were screened to construct the risk score model and nomogram for predicting the overall survival (OS) time and prognosis of BC patients. The prognostic value was verified using a validation cohort in GSE13507.

RESULTS

Based on 13 selected. lncRNAs, miRNAs and mRNA screened using L1-penalized algorithm, BC patients were classified into two groups: high-risk group (including 201 patients ) and low risk group (including 200 patients). The high-risk group's OS time ( hazard ratio [HR], 2.160; 95% CI, 1.586 to 2.942; P= 5.678e-07) was poorer than that of low-risk groups' (HR, 1.675; 95% CI, 1.037 to 2.713; P= 3.393 e-02) in the training cohort. The area under curve (AUC) for training and validation datasets were 0.852. Younger patients (age ⩽ 60 years) had an improved OS than the patients with advanced age (age > 60 years) (HR 1.033, 95% CI 1.017 to 1.049; p= 2.544E-05). We built a predictive model based on the TCGA cohort by using nomograms, including clinicopathological factors such as age, recurrence rate, and prognostic score.

CONCLUSIONS

The risk model based on 13 DERs patterns could well predict the prognosis for patients with BC.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals dynamic changes in the tumor immune microenvironment of bladder cancer and establishes a prognostic model

BACKGROUND

The prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA.

METHODS

BLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated.

RESULTS

scRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy.

CONCLUSION

By integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.

Fatty Acid Synthase Mutations Predict Favorable Immune Checkpoint Inhibitor Outcome and Response in Melanoma and Non-Small Cell Lung Cancer Patients

Fatty acid synthase (FASN) acts as the central member in fatty acid synthesis and metabolism processes, which regulate oncogenic signals and tumor immunogenicity. To date, no studies have reported the connection of FASN mutations with ICI efficacy. In this study, from 631 melanoma and 109 NSCLC patients who received ICI treatments, we retrospectively curated multiomics profiles and ICI treatment data. We also explored the potential molecular biological mechanisms behind FASN alterations. In melanoma patients, FASN mutations were observed to associate with a preferable immunotherapeutic prognosis and response rate (both p < 0.01). These connections were further corroborated by the NSCLC patients (both p < 0.01). Further analyses showed that a favorable tumor immunogenicity and immune microenvironment were involved in FASN mutations. This work confirms the clinical immunotherapy implications of FASN mutation-mediated fatty acid metabolism and provides a possible indicator for immunotherapy prognosis prediction.

Hypoxia-Inducible Factor-2-Altered Urothelial Carcinoma: Clinical and Genomic Features

Background: Hypoxia is recognized as a key feature of cancer growth and is involved in various cellular processes, including proliferation, angiogenesis, and immune surveillance. Besides hypoxia-inducible factor 1-alpha (HIF-1α), which is the main mediator of hypoxia effects and can also be activated under normoxic conditions, little is known about its counterpart, HIF-2. This study focused on investigating the clinical and molecular landscape of HIF-2-altered urothelial carcinoma (UC). Methods: Publicly available next-generation sequencing (NGS) data from muscle-invasive UC cell lines and patient tumor samples from the MSK/TCGA 2020 cohort (n = 476) were interrogated for the level of expression (mRNA, protein) and presence of mutations, copy number variations, structural variants in the EPAS1 gene encoding HIF-2, and findings among various clinical (stage, grade, progression-free and overall survival) and molecular (tumor mutational burden, enriched gene expression) parameters were compared between altered and unaltered tumors. Results: 19% (7/37) of UC cell lines and 7% (27/380) of patients with muscle-invasive UC display high EPAS1 mRNA and protein expression or/and EPAS1 alterations. EPAS1-altered tumors are associated with higher stage, grade, and lymph node metastasis as well as with shorter PFS (14 vs. 51 months, q = 0.01) and OS (15 vs. 55 months, q = 0.01). EPAS1 mRNA expression is directly correlated with that of its target-genes, including VEGF, FLT1, KDR, DLL4, CDH5, ANGPT1 (q < 0.001). While there is a slightly higher tumor mutational burden in EPAS1-altered tumors (9.9 vs. 4.9 mut/Mb), they are enriched in and associated with genes promoting immune evasion, including ARID5B, SPINT1, AAK1, CLIC3, SORT1, SASH1, and FGFR3, respectively (q < 0.001). Conclusions: HIF-2-altered UC has an aggressive clinical and a distinct genomic and immunogenomic profile enriched in angiogenesis- and immune evasion-promoting genes.