Intratumoral CD4+ T Cells Mediate Anti-tumor Cytotoxicity in Human Bladder Cancer.

Responses to anti-PD-1 immunotherapy occur but are infrequent in bladder cancer. The specific T cells that mediate tumor rejection are unknown. T cells from human bladder tumors and non-malignant tissue were assessed with single-cell RNA and paired T cell receptor (TCR) sequencing of 30,604 T cells from 7 patients. We find that the states and repertoires of CD8+ T cells are not distinct in tumors compared with non-malignant tissues. In contrast, single-cell analysis of CD4+ T cells demonstrates several tumor-specific states, including multiple distinct states of regulatory T cells. Surprisingly, we also find multiple cytotoxic CD4+ T cell states that are clonally expanded. These CD4+ T cells can kill autologous tumors in an MHC class II-dependent fashion and are suppressed by regulatory T cells. Further, a gene signature of cytotoxic CD4+ T cells in tumors predicts a clinical response in 244 metastatic bladder cancer patients treated with anti-PD-L1.

Estrogen-related receptors are targetable ROS sensors.

Excessive reactive oxygen species (ROS) can cause oxidative stress and consequently cell injury contributing to a wide range of diseases. Addressing the critical gaps in our understanding of the adaptive molecular events downstream ROS provocation holds promise for the identification of druggable metabolic vulnerabilities. Here, we unveil a direct molecular link between the activity of two estrogen-related receptor (ERR) isoforms and the control of glutamine utilization and glutathione antioxidant production. ERRα down-regulation restricts glutamine entry into the TCA cycle, while ERRγ up-regulation promotes glutamine-driven glutathione production. Notably, we identify increased ERRγ expression/activation as a hallmark of oxidative stress triggered by mitochondrial disruption or chemotherapy. Enhanced tumor antioxidant capacity is an underlying feature of human breast cancer (BCa) patients that respond poorly to treatment. We demonstrate that pharmacological inhibition of ERRγ with the selective inverse agonist GSK5182 increases antitumor efficacy of the chemotherapeutic paclitaxel on poor outcome BCa tumor organoids. Our findings thus underscore the ERRs as novel redox sensors and effectors of a ROS defense program and highlight the potential therapeutic advantage of exploiting ERRγ inhibitors for the treatment of BCa and other diseases where oxidative stress plays a central role.

Defining and targeting tumor-associated macrophages in malignant mesothelioma.

Defining the ontogeny of tumor-associated macrophages (TAM) is important to develop therapeutic targets for mesothelioma. We identified two distinct macrophage populations in mouse peritoneal and pleural cavities, the monocyte-derived, small peritoneal/pleural macrophages (SPM), and the tissue-resident large peritoneal/pleural macrophages (LPM). SPM rapidly increased in tumor microenvironment after tumor challenge and contributed to the vast majority of M2-like TAM. The selective depletion of M2-like TAM by conditional deletion of the Dicer1 gene in myeloid cells (D-/-) promoted tumor rejection. Sorted SPM M2-like TAM initiated tumorigenesis in vivo and in vitro, confirming their capacity to support tumor development. The transcriptomic and single-cell RNA sequencing analysis demonstrated that both SPM and LPM contributed to the tumor microenvironment by promoting the IL-2-STAT5 signaling pathway, inflammation, and epithelial-mesenchymal transition. However, while SPM preferentially activated the KRAS and TNF-α/NFkB signaling pathways, LPM activated the IFN-γ response. The importance of LPM in the immune response was confirmed by depleting LPM with intrapleural clodronate liposomes, which abrogated the antitumoral memory immunity. SPM gene signature could be identified in pleural effusion and tumor from patients with untreated mesothelioma. Five genes, TREM2, STAB1, LAIR1, GPNMB, and MARCO, could potentially be specific therapeutic targets. Accordingly, Trem2 gene deletion led to reduced SPM M2-like TAM with compensatory increase in LPM and slower tumor growth. Overall, these experiments demonstrate that SPM M2-like TAM play a key role in mesothelioma development, while LPM more specifically contribute to the immune response. Therefore, selective targeting of monocyte-derived TAM may enhance antitumor immunity through compensatory expansion of tissue-resident TAM.

Common activities and predictive gene signature identified for genetic hypomorphs of TP53.

Missense mutations that inactivate p53 occur commonly in cancer, and germline mutations in TP53 cause Li Fraumeni syndrome, which is associated with early-onset cancer. In addition, there are over two hundred germline missense variants of p53 that remain uncharacterized. In some cases, these germline variants have been shown to encode lesser-functioning, or hypomorphic, p53 protein, and these alleles are associated with increased cancer risk in humans and mouse models. However, most hypomorphic p53 variants remain un- or mis-classified in clinical genetics databases. There thus exists a significant need to better understand the behavior of p53 hypomorphs and to develop a functional assay that can distinguish hypomorphs from wild-type p53 or benign variants. We report the surprising finding that two different African-centric genetic hypomorphs of p53 that occur in distinct functional domains of the protein share common activities. Specifically, the Pro47Ser variant, located in the transactivation domain, and the Tyr107His variant, located in the DNA binding domain, both share increased propensity to misfold into a conformation specific for mutant, misfolded p53. Additionally, cells and tissues containing these hypomorphic variants show increased NF-κB activity. We identify a common gene expression signature from unstressed lymphocyte cell lines that is shared between multiple germline hypomorphic variants of TP53, and which successfully distinguishes wild-type p53 and a benign variant from lesser-functioning hypomorphic p53 variants. Our findings will allow us to better understand the contribution of p53 hypomorphs to disease risk and should help better inform cancer risk in the carriers of p53 variants.

Prelude to malignancy: A gene expression signature in normal mammary gland from breast cancer patients suggests pre-tumorous alterations and is associated with adverse outcomes.

Despite advances in early detection and treatment strategies, breast cancer recurrence and mortality remain a significant health issue. Recent insights suggest the prognostic potential of microscopically healthy mammary gland, in the vicinity of the breast lesion. Nonetheless, a comprehensive understanding of the gene expression profiles in these tissues and their relationship to patient outcomes remain missing. Furthermore, the increasing trend towards breast-conserving surgery may inadvertently lead to the retention of existing cancer-predisposing mutations within the normal mammary gland. This study assessed the transcriptomic profiles of 242 samples from 83 breast cancer patients with unfavorable outcomes, including paired uninvolved mammary gland samples collected at varying distances from primary lesions. As a reference, control samples from 53 mammoplasty individuals without cancer history were studied. A custom panel of 634 genes linked to breast cancer progression and metastasis was employed for expression profiling, followed by whole-transcriptome verification experiments and statistical analyses to discern molecular signatures and their clinical relevance. A distinct gene expression signature was identified in uninvolved mammary gland samples, featuring key cellular components encoding keratins, CDH1, CDH3, EPCAM cell adhesion proteins, matrix metallopeptidases, oncogenes, tumor suppressors, along with crucial genes (FOXA1, RAB25, NRG1, SPDEF, TRIM29, and GABRP) having dual roles in cancer. Enrichment analyses revealed disruptions in epithelial integrity, cell adhesion, and estrogen signaling. This signature, named KAOS for Keratin-Adhesion-Oncogenes-Suppressors, was significantly associated with reduced tumor size but increased mortality rates. Integrating molecular assessment of non-malignant mammary tissue into disease management could enhance survival prediction and facilitate personalized patient care.

Identifying high-risk multiple myeloma patients: A novel approach using a clonal gene signature.

Multiple myeloma (MM) is a heterogeneous disease with a small subset of high-risk patients having poor prognoses. Identifying these patients is crucial for treatment management and strategic decisions. In this study, we developed a novel computational framework to define prognostic gene signatures by selecting genes with expression driven by clonal copy number alterations. We applied this framework to MM and developed a clonal gene signature (CGS) consisting of 22 genes and evaluated in five independent datasets. The CGS provided significant prognostic values after adjusting for well-established factors including cytogenetic abnormalities, International Staging System (ISS), and Revised ISS (R-ISS). Importantly, CGS demonstrated higher performance in identifying high-risk patients compared to the GEP70 and SKY92 signatures recommended for prognostic stratification of MM. CGS can further stratify patients into subgroups with significantly differential prognoses when applied to the high- and low-risk groups identified by GEP70 and SKY92. Additionally, CGS scores are significantly associated with patient response to dexamethasone, a commonly used treatment for MM. In summary, we proposed a computational framework that requires only gene expression data to identify CGSs for prognosis prediction. CGS provides a useful biomarker for improving prognostic stratification in MM, especially for identifying the highest-risk patients.

Prostate cancer cancer-associated fibroblasts with stable markers post-androgen deprivation therapy associated with tumor progression and castration resistant prostate cancer.

The specificity and clinical relevance of cancer-associated fibroblasts (CAFs) in prostate cancer (PCa), as well as the effect of androgen deprivation therapy (ADT) on CAFs, remain to be fully elucidated. Using cell lineage diversity and weighted gene co-expression network analysis (WGCNA), we pinpointed a unique CAF signature exclusive to PCa. The specificity of this CAF signature was validated through single-cell RNA sequencing (scRNA-seq), cell line RNA sequencing, and immunohistochemistry. This signature associates CAFs with tumor progression, elevated Gleason scores, and the emergence of castration resistant prostate cancer (CRPC). Using scRNA-seq on collected samples, we demonstrated that the CAF-specific signature is not altered by ADT, maintaining its peak signal output. Identifying a PCa-specific CAF signature and observing signaling changes in CAFs after ADT lay essential groundwork for further PCa studies.

Clinical and prognostic significance analysis of glycolysis-related genes in HNSCC.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) represents one of the most malignant cancers worldwide, with poor survival. Experimental evidence implies that glycolysis/hypoxia is associated with HNSCC. In this study, we aimed to construct a novel glycolysis-/hypoxia-related gene (GHRG) signature for survival prediction of HNSCC. METHODS: A multistage screening strategy was used to establish the GHRG prognostic model by univariate/least absolute shrinkage and selection operator (LASSO)/step multivariate Cox regressions from The Cancer Genome Atlas cohort. A nomogram was constructed to quantify the survival probability. Correlations between risk score and immune infiltration and chemotherapy sensitivity were explored. RESULTS: We established a 12-GHRG mRNA signature to predict the prognosis in HNSCC patients. Patients in the high-risk score group had a much worse prognosis. The predictive power of the model was validated by external HNSCC cohorts, and the model was identified as an independent factor for survival prediction. Immune infiltration analysis showed that the high-risk score group had an immunosuppressive microenvironment. Finally, the model was effective in predicting chemotherapeutic sensitivity. CONCLUSIONS: Our study demonstrated that the GHRG model is a robust prognostic tool for survival prediction of HNSCC. Findings of this work provide novel insights for immune infiltration and chemotherapy of HNSCC, and may be applied clinically to guide therapeutic strategies.

A comprehensive study of oxidative stress-related effects on the prognosis and drug therapy of cervical cancer.

BACKGROUND: Cervical cancer (CC) is a serious global disease with poor prognoses and a significant recurrence rate in patients with advanced disease. Oxidative stress (OS) greatly influences many types of human cancers, making it crucial to understand the functional mechanisms of OS-related genes in CC. METHODS: The transcriptome and clinical data of three normal samples and 306 patients with CC were obtained from The Cancer Genome Atlas dataset. The GSE44001 dataset was acquired from the Gene Expression Omnibus database. OS-related subtypes in the cohort with CC were identified using unsupervised hierarchical clustering, univariate Cox analysis, gene set enrichment analysis (GSEA), and least absolute shrinkage and selection operator regression analysis. Additionally, molecular pathways that differ across subtypes were determined and OS-related genes linked to the prognosis of patients of CC were determined. Finally, a clinical prognostic gene signature was developed and validated. The relative infiltration level of immune cell subpopulations in different risk groups and subtypes was evaluated using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERPORT) algorithm and single-sample GSEA (ssGSEA) techniques. RESULTS: The present study established two distinct OS subtypes (OS clusters A and B). Analysis using ssGSEA and CIBERSPORT revealed that OS cluster B exhibited a significant level of immune infiltration. A clinical prognostic gene signature was established using OS-related characteristic genes identified by examining the differentially expressed genes across both subtypes. Furthermore, patients with CC were grouped into high- and low-risk groups, with the low-risk group showing higher survival rates. Additionally, these individuals exhibited significant advantages in terms of survival and immunotherapy. Receiver operating characteristic curve analysis demonstrated the higher predictive value of the clinical prognostic gene signature. The outcomes of the validation group depicted congruence with those recorded in the training group. CONCLUSIONS: A new model was constructed based on eight OS-related characteristic genes to aid the prediction of the survival rates of individuals with CC. The present study contributes to the existing literature on the mechanisms of OS genes in CC and offers a fresh perspective for future advancements in immunotherapy for such individuals.

Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia.

The advances in single-cell RNA sequencing (scRNA-seq) technologies enable the characterization of transcriptomic profiles at the cellular level and demonstrate great promise in bulk sample analysis thereby offering opportunities to transfer gene signature from scRNA-seq to bulk data. However, the gene expression signatures identified from single cells are typically inapplicable to bulk RNA-seq data due to the profiling differences of distinct sequencing technologies. Here, we propose single-cell pair-wise gene expression (scPAGE), a novel method to develop single-cell gene pair signatures (scGPSs) that were beneficial to bulk RNA-seq classification to transfer knowledge across platforms. PAGE was adopted to tackle the challenge of profiling differences. We applied the method to acute myeloid leukemia (AML) and identified the scGPS from mouse scRNA-seq that allowed discriminating between AML and control cells. The scGPS was validated in bulk RNA-seq datasets and demonstrated better performance (average area under the curve [AUC] = 0.96) than the conventional gene expression strategies (average AUC$\le$ 0.88) suggesting its potential in disclosing the molecular mechanism of AML. The scGPS also outperformed its bulk counterpart, which highlighted the benefit of gene signature transfer. Furthermore, we confirmed the utility of scPAGE in sepsis as an example of other disease scenarios. scPAGE leveraged the advantages of single-cell profiles to enhance the analysis of bulk samples revealing great potential of transferring knowledge from single-cell to bulk transcriptome studies.

Cancer-associated fibroblasts (CAFs) gene signatures predict outcomes in breast and prostate tumor patients.

BACKGROUND: Over the last two decades, tumor-derived RNA expression signatures have been developed for the two most commonly diagnosed tumors worldwide, namely prostate and breast tumors, in order to improve both outcome prediction and treatment decision-making. In this context, molecular signatures gained by main components of the tumor microenvironment, such as cancer-associated fibroblasts (CAFs), have been explored as prognostic and therapeutic tools. Nevertheless, a deeper understanding of the significance of CAFs-related gene signatures in breast and prostate cancers still remains to be disclosed. METHODS: RNA sequencing technology (RNA-seq) was employed to profile and compare the transcriptome of CAFs isolated from patients affected by breast and prostate tumors. The differentially expressed genes (DEGs) characterizing breast and prostate CAFs were intersected with data from public datasets derived from bulk RNA-seq profiles of breast and prostate tumor patients. Pathway enrichment analyses allowed us to appreciate the biological significance of the DEGs. K-means clustering was applied to construct CAFs-related gene signatures specific for breast and prostate cancer and to stratify independent cohorts of patients into high and low gene expression clusters. Kaplan-Meier survival curves and log-rank tests were employed to predict differences in the outcome parameters of the clusters of patients. Decision-tree analysis was used to validate the clustering results and boosting calculations were then employed to improve the results obtained by the decision-tree algorithm. RESULTS: Data obtained in breast CAFs allowed us to assess a signature that includes 8 genes (ITGA11, THBS1, FN1, EMP1, ITGA2, FYN, SPP1, and EMP2) belonging to pro-metastatic signaling routes, such as the focal adhesion pathway. Survival analyses indicated that the cluster of breast cancer patients showing a high expression of the aforementioned genes displays worse clinical outcomes. Next, we identified a prostate CAFs-related signature that includes 11 genes (IL13RA2, GDF7, IL33, CXCL1, TNFRSF19, CXCL6, LIFR, CXCL5, IL7, TSLP, and TNFSF15) associated with immune responses. A low expression of these genes was predictive of poor survival rates in prostate cancer patients. The results obtained were significantly validated through a two-step approach, based on unsupervised (clustering) and supervised (classification) learning techniques, showing a high prediction accuracy (≥ 90%) in independent RNA-seq cohorts. CONCLUSION: We identified a huge heterogeneity in the transcriptional profile of CAFs derived from breast and prostate tumors. Of note, the two novel CAFs-related gene signatures might be considered as reliable prognostic indicators and valuable biomarkers for a better management of breast and prostate cancer patients.

Natural killer cell-associated prognosis model characterizes immune landscape and treatment efficacy of diffuse large B cell lymphoma.

PURPOSE: NK cells are essential for the detection, identification and prediction of cancer. However, so far, there is no prognostic risk model based on NK cell-related genes to predict the prognosis and treatment outcome of DLBCL patients. This study aimed to explore a risk assessment model that could accurately predict the prognosis and treatment efficacy of DLBCL. METHODS: Bioinformatics analysis of the expression profiles of DLBCL samples in the GEO database was performed. Cox regression and LASSO regression analysis were used to determine NK cell-related genes associated with patient's prognosis. Based on these genes, a risk assessment model was constructed to predict the prognosis of patients and the effectiveness of treatment. Finally, qRT-PCR was used to verify the expression of gene tags in clinical samples. RESULTS: We identified seven prognosis-related NK cell-related genes (MAP2K1, PRKCB, TNFRSF10B, IL18, LAMP1, RASGRP1, and SP110), and DLBCL patients were divided into low- and high-risk groups based on these genes. Survival analysis showed that the prognosis of patients with low-risk group was better. Pathway enrichment analysis showed that the differentially expressed genes between the two risk groups were related to immune response pathways. Compared with the high-risk group, the low-risk group had higher infiltration of immune cells in tumor tissues. Besides, compared with high-risk group, low-risk patients by immunotherapy or other commonly used anti-tumor drugs might have better efficacy after treatment. In addition, qRT-PCR showed that the expression of risk genes including TNFRSF10B, IL18 and LAMP1 were significantly increased in most DLBCL samples compared to control samples, while the expression of protective genes including MAP2K1, PRKCB, RASGRP1 and SP110 were significantly decreased. CONCLUSION: The NK cell-related gene signatures were proved to be a reliable indicator of the success of immunotherapy in patients with DLBCL, thus providing a unique evaluation method.

Development of a Novel Prognostic Model for Esophageal Squamous Cell Carcinoma: Insights into Immune Cell Interactions and Drug Sensitivity.

Esophageal squamous cell carcinoma (ESCC) presents a five-year survival rate below 20%, underscoring the need for improved prognostic markers. Our study analyzed ESCC-specific datasets to identify consistently differentially expressed genes. A Venn analysis followed by gene network interactions revealed 23 key genes, from which we built a prognostic model using the COX algorithm (p = 0.000245, 3-year AUC = 0.967). This model stratifies patients into risk groups, with high-risk individuals showing worse outcomes and lower chemotherapy sensitivity. Moreover, a link between risk scores and M2 macrophage infiltration, as well as significant correlations with immune checkpoint genes (e.g., SIGLEC15, PDCD1LG2, and HVCR2), was discovered. High-risk patients had lower Tumor Immune Dysfunction and Exclusion (TIDE) values, suggesting potential responsiveness to immune checkpoint blockade (ICB) therapy. Our efficient 23-gene prognostic model for ESCC indicates a dual utility in assessing prognosis and guiding therapeutic decisions, particularly in the context of ICB therapy for high-risk patients.

Construction and validation of a hypoxia-related gene signature to predict the prognosis of breast cancer.

BACKGROUND: Among the most common forms of cancer worldwide, breast cancer posed a serious threat to women. Recent research revealed a lack of oxygen, known as hypoxia, was crucial in forming breast cancer. This research aimed to create a robust signature with hypoxia-related genes to predict the prognosis of breast cancer patients. The function of hypoxia genes was further studied through cell line experiments. MATERIALS AND METHODS: In the bioinformatic part, transcriptome and clinical information of breast cancer were obtained from The Cancer Genome Atlas(TCGA). Hypoxia-related genes were downloaded from the Genecards Platform. Differentially expressed hypoxia-related genes (DEHRGs) were identified. The TCGA filtered data was evenly split, ensuring a 1:1 distribution between the training and testing sets. Prognostic-related DEHRGs were identified through Cox regression. The signature was established through the training set. Then, it was validated using the test set and external validation set GSE131769 from Gene Expression Omnibus (GEO). The nomogram was created by incorporating the signature and clinicopathological characteristics. The predictive value of the nomogram was evaluated by C-index and receiver operating characteristiccurve. Immune microenvironment and mutation burden were also examined. In the experiment part, the function of the two most significant hypoxia-related genes were further explored by cell-line experiments. RESULTS: In the bioinformatic part, 141 up-regulated and 157 down-regulated DEHRGs were screened out. A prognostic signature was constructed containing nine hypoxia genes (ALOX15B, CA9, CD24, CHEK1, FOXM1, HOTAIR, KCNJ11, NEDD9, PSME2) in the training set. Low-risk patients exhibited a much more favorable prognosis than higher-risk ones (P < 0.001). The signature was double-validated in the test set and GSE131769 (P = 0.006 and P = 0.001). The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817. Patients in the high-risk group had a higher tumor mutation burden when compared to the low-risk group. In the experiment part, the down-regulation of PSME2 inhibited cell growth ability and clone formation capability of breast cancer cells, while the down-regulation of KCNJ11 did not have any functions. CONCLUSION: Based on 9 DEHRGs, a reliable signature was established through the bioinformatic method. It could accurately predict the prognosis of breast cancer patients. Cell line experiment indicated that PSME2 played a protective role. Summarily, we provided a new insight to predict the prognosis of breast cancer by hypoxia-related genes.

Comprehensive analysis of T-cell regulatory factors and tumor immune microenvironment in stomach adenocarcinoma.

BACKGROUND: Gastric cancer (GC) is one of the most prevalent malignant tumors worldwide and is associated with high morbidity and mortality rates. However, the specific biomarkers used to predict the postoperative prognosis of patients with gastric cancer remain unknown. Recent research has shown that the tumor microenvironment (TME) has an increasingly positive effect on anti-tumor activity. This study aims to build signatures to study the effect of certain genes on gastric cancer. METHODS: Expression profiles of 37 T cell-related genes and their TME characteristics were comprehensively analyzed. A risk signature was constructed and validated based on the screened T cell-related genes, and the roles of hub genes in GC were experimentally validated. RESULTS: A novel T cell-related gene signature was constructed based on CD5, ABCA8, SERPINE2, ESM1, SERPINA5, and NMU. The high-risk group indicated lower overall survival (OS), poorer immune efficacy, and higher drug resistance, with SERPINE2 promoting GC cell proliferation, according to experiments. SERPINE2 and CXCL12 were significantly correlated, indicating poor OS via the Youjiang cohort. CONCLUSIONS: This study identified T cell-related genes in patients with stomach adenocarcinoma (STAD) for prognosis estimation and proposed potential immunotherapeutic targets for STAD.

Identification of the cytoplasmic DNA-Sensing cGAS-STING pathway-mediated gene signatures and molecular subtypes in prostate cancer.

BACKGROUND: Considering the age relevance of prostate cancer (PCa) and the involvement of the cGAS-STING pathway in aging and cancer, we aim to classify PCa into distinct molecular subtypes and identify key genes from the novel perspective of the cGAS-STING pathway. It is of significance to guide personalized intervention of cancer-targeting therapy based on genetic evidence. METHODS: The 430 patients with PCa from the TCGA database were included. We integrated 29 key genes involved in cGAS-STING pathway and analyzed differentially expressed genes and biochemical recurrence (BCR)-free survival-related genes. The assessments of tumor stemness and heterogeneity and tumor microenvironment (TME) were conducted to reveal potential mechanisms. RESULTS: PCa patients were classified into two distinct subtypes using AURKB, TREX1, and STAT6, and subtype 1 had a worse prognosis than subtype 2 (HR: 21.19, p < 0.001). The findings were validated in the MSKCC2010 cohort. Among subtype 1 and subtype 2, the top ten mutation genes were MUC5B, DNAH9, SLC5A10, ZNF462, USP31, SIPA1L3, PLEC, HRAS, MYOM1, and ITGB6. Gene set variation analysis revealed a high enrichment of the E2F target in subtype 1, and gene set enrichment analysis showed significant enrichment of base excision repair, cell cycle, and DNA replication in subtype 1. TME evaluation indicated that subtype 1 had a significantly higher level of T cells follicular helper and a lower level of plasma cells than subtype 2. CONCLUSIONS: The molecular subtypes mediated by the cGAS-STING pathway and the genetic risk score may aid in identifying potentially high-risk PCa patients who may benefit from pharmacologic therapies targeting the cGAS-STING pathway.

Comment on "A novel super-enhancer-related gene signature predicts prognosis and immune microenvironment for breast cancer".

The primary aim of this study is to critically evaluate and comment on the research presented in the article titled "A Novel Super-Enhancer-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer" by Wu et al. Our specific objectives include assessing the methodology employed by the authors, particularly in regard to the utilization of a super-enhancer-related gene signature for breast cancer prognosis prediction. We propose the necessity of subgroup analysis to effectively address the heterogeneity in breast cancer subtypes, which is crucial for the applicability of the SERGs across diverse breast cancer cases. Additionally, we suggest conducting a more comprehensive immune panel study to deepen the understanding of how the immune microenvironment impacts breast cancer prognosis. Our commentary seeks to provide valuable insights into the strengths and weaknesses of the study, contributing to a more comprehensive understanding of its findings and potential clinical implications.

Predictive Biomarkers for Response to TGF- ß Inhibition in Resensitizing Chemo(radiated) Esophageal Adenocarcinoma.

Epithelial-mesenchymal transition (EMT) has been identified as a driver of therapy resistance, particularly in esophageal adenocarcinoma (EAC), where transforming growth factor beta (TGF-ß) can induce this process. Inhibitors of TGF-ß may counteract the occurrence of mesenchymal, resistant tumor cell populations following chemo(radio)therapy and improve treatment outcomes in EAC. Here, we aimed to identify predictive biomarkers for the response to TGF-ß targeting. In vitro approximations of neoadjuvant treatment were applied to publicly available primary EAC cell lines. TGF-ß inhibitors fresolimumab and A83-01 were employed to inhibit EMT, and mesenchymal markers were quantified via flow cytometry to assess efficacy. Our results demonstrated a robust induction of mesenchymal cell states following chemoradiation, with TGF-ß inhibition leading to variable reductions in mesenchymal markers. The cell lines were clustered into responders and non-responders. Genomic expression profiles were obtained through RNA-seq analysis. Differentially expressed gene (DEG) analysis identified 10 positively- and 23 negatively-associated hub genes, which were bioinformatically identified. Furthermore, the correlation of DEGs with response to TGF-ß inhibition was examined using public pharmacogenomic databases, revealing 9 positively associated and 11 negatively associated DEGs. Among these, ERBB2, EFNB1, and TNS4 were the most promising candidates. Our findings reveal a distinct gene expression pattern associated with the response to TGF-ß inhibition in chemo(radiated) EAC. The identified DEGs and predictive markers may assist patient selection in clinical studies investigating TGF-ß targeting.

Identification and characterization of in vitro expanded hematopoietic stem cells.

Hematopoietic stem cells (HSCs) cultured outside the body are the fundamental component of a wide range of cellular and gene therapies. Recent efforts have achieved > 200-fold expansion of functional HSCs, but their molecular characterization has not been possible since the majority of cells are non-HSCs and single cell-initiated cultures have substantial clone-to-clone variability. Using the Fgd5 reporter mouse in combination with the EPCR surface marker, we report exclusive identification of HSCs from non-HSCs in expansion cultures. By directly linking single-clone functional transplantation data with single-clone gene expression profiling, we show that the molecular profile of expanded HSCs is similar to proliferating fetal HSCs and reveals a gene expression signature, including Esam, Prdm16, Fstl1, and Palld, that can identify functional HSCs from multiple cellular states. This "repopulation signature" (RepopSig) also enriches for HSCs in human datasets. Together, these findings demonstrate the power of integrating functional and molecular datasets to better derive meaningful gene signatures and opens the opportunity for a wide range of functional screening and molecular experiments previously not possible due to limited HSC numbers.

Role of IFN-α in Rheumatoid Arthritis.

PURPOSE OF REVIEW: Type 1 interferons (IFN-I) are of increasing interest across a wide range of autoimmune rheumatic diseases. Historically, research into their role in rheumatoid arthritis (RA) has been relatively neglected, but recent work continues to highlight a potential contribution to RA pathophysiology. RECENT FINDINGS: We emphasise the importance of disease stage when examining IFN-I in RA and provide an overview on how IFN-I may have a direct role on a variety of relevant cellular functions. We explore how clinical trajectory may be influenced by increased IFN-I signalling, and also, the limitations of scores composed of interferon response genes. Relevant environmental triggers and inheritable RA genetic risk relating to IFN-I signalling are explored with emphasis on intriguing data potentially linking IFN-I exposure, epigenetic changes, and disease relevant processes. Whilst these data cumulatively illustrate a likely role for IFN-I in RA, they also highlight the knowledge gaps, particularly in populations at risk for RA, and suggest directions for future research to both better understand IFN-I biology and inform targeted therapeutic strategies.