Single-cell sequencing and bulk RNA data reveal the tumor microenvironment infiltration characteristics of disulfidptosis related genes in breast cancer

Single-cell landscape of dynamic changes in CD8+ T cells, CD4+ T cells and exhausted T cells in hepatocellular carcinoma

Hepatocellular carcinoma has a high incidence and poor prognosis. In this study, we investigated the value of T-cell-related genes in prognosis by single-cell sequencing data in hepatocellular carcinoma. Twelve cases of hepatocellular carcinoma single-cell sequencing were included in the study. The high dimensional weighted gene co-expression network analysis (hdWGCNA) was used to identify gene modules associated with CD4+ T cells, CD8+ T cells and exhausted T cells. Altered signaling pathway activity in exhausted T cells was uncovered by the AUCell algorithm. xCELL, TIMER, QUANTISEQ, CIBERSORT and CIBERSORT-abs were performed to explore immune cell infiltration. Immune checkpoint inhibitor genes and TIDE methods were used to predict immunotherapy response. Finally, immunohistochemistry and real-time PCR were used to verify gene expression. The hdWGCNA algorithm identified 40 genes strongly associated with CD4+ T cells, CD8+ T cells and exhausted T cells. Seven genes were finally selected for transcriptome data modeling. The results of the three independent datasets suggested that the model had strong prognostic value. Model genes were critical factors influencing CD4+ T cell and CD8+ T cell infiltration in patients. The efficacy of PD-1 immunotherapy was higher in patients belonging to the low-risk group. Alterations in signaling pathways' activity within exhausted T cells were crucial factors contributing to the decline in immune function. Differential expression of seven genes in CD8+ T cells, CD4+ T cells and exhausted T cells were key targets for improving immunotherapy response in HCC.

Comprehensive identification of a disulfidptosis-associated long non-coding RNA signature to predict the prognosis and treatment options in ovarian cancer

Purpose

Distinguished from cuproptosis and ferroptosis, disulfidptosis has been described as a newly discovered form of non-programmed cell death tightly associated with glucose metabolism. However, the prognostic profile of disulfidptosis-related lncRNAs (DRLRs) in ovarian cancer (OC) and their biological mechanisms need to be further elucidated.

Materials and methods

First, we downloaded the profiles of RNA transcriptome, clinical information for OC patients from the TCGA database. Generated from Cox regression analysis, prognostic lncRNAs were utilized to identify the risk signature by least absolute shrinkage and selection operator analysis. Then, we explored the intimate correlations between disulfidptosis and lncRNAs. What's more, we performed a series of systemic analyses to assess the robustness of the model and unravel its relationship with the immune microenvironment comprehensively.

Results

We identified two DRLR clusters, in which OC patients with low-risk scores exhibited a favorable prognosis, up-regulated immune cell infiltrations and enhanced sensitivity to immunotherapy. Furthermore, validation of the signature by clinical features and Cox analysis demonstrated remarkable consistency, suggesting the universal applicability of our model. It's worth noting that high-risk patients showed more positive responses to immune checkpoint inhibitors and potential chemotherapeutic drugs.

Conclusion

Our findings provided valuable insights into DRLRs in OC for the first time, which indicated an excellent clinical value in the selection of management strategies, spreading brilliant horizons into individualized therapy.

Integrated analysis of disulfidoptosis-related genes identifies NRP1 as a novel biomarker promoting proliferation of gastric cancer via glutamine mediated energy metabolism

The incidence and mortality of gastric cancer rank fifth and fourth worldwide among all malignancies, respectively. Additionally, disulfidoptosis, a recently identified form of cellular demise, is closely linked to the initiation and advancement of malignancies. This study aims to create a novel signature of disulfidptosis-related genes (DRGs) and to further explore its association with the tumor immune microenvironment. Based on our comprehensive study, a prognostic signature consisting of 31 DRGs in stomach adenocarcinoma (STAD) was identified and characterized. Through the integrative analyses involving gene expression profiling, machine learning algorithms, and Cox regression models, the prognostic significance of these DRGs was demonstrated. Our findings highlight their strong predictive power in assessing overall survival across diverse patient datasets, and their better performance than traditional clinicopathological factors. Moreover, the DRGs signature showed association with the characteristics of the tumor microenvironment, which has implications for the immune modulation and therapeutic strategies in STAD. Specifically, NRP1 emerged as a key DRG with elevated expression in STAD, showing correlation with the advanced stages of diseases and poorer outcomes. Functional studies further revealed the role of NRP1 in promoting STAD cell proliferation through the modulation of glutamine metabolism. Overall, our study underscores the clinical relevance of DRGs as biomarker and potential therapeutic targets in STAD management, providing insights into disease biology and personalized treatments.

Integrated multi-omics and artificial intelligence to explore new neutrophils clusters and potential biomarkers in sepsis with experimental validation

Background

Sepsis, causing serious organ and tissue damage and even death, has not been fully elucidated. Therefore, understanding the key mechanisms underlying sepsis-associated immune responses would lead to more potential therapeutic strategies.

Methods

Single-cell RNA data of 4 sepsis patients and 2 healthy controls in the GSE167363 data set were studied. The pseudotemporal trajectory analyzed neutrophil clusters under sepsis. Using the hdWGCNA method, key gene modules of neutrophils were explored. Multiple machine learning methods were used to screen and validate hub genes for neutrophils. SCENIC was then used to explore transcription factors regulating hub genes. Finally, quantitative reverse transcription-polymerase chain reaction was to validate mRNA expression of hub genes in peripheral blood neutrophils of two mice sepsis models.

Results

We discovered two novel neutrophil subtypes with a significant increase under sepsis. These two neutrophil subtypes were enriched in the late state during neutrophils differentiation. The hdWGCNA analysis of neutrophils unveiled that 3 distinct modules (Turquoise, brown, and blue modules) were closely correlated with two neutrophil subtypes. 8 machine learning methods revealed 8 hub genes with high accuracy and robustness (ALPL, ACTB, CD177, GAPDH, SLC25A37, S100A8, S100A9, and STXBP2). The SCENIC analysis revealed that APLP, CD177, GAPDH, S100A9, and STXBP2 were significant associated with various transcriptional factors. Finally, ALPL, CD177, S100A8, S100A9, and STXBP2 significantly up regulated in peripheral blood neutrophils of CLP and LPS-induced sepsis mice models.

Conclusions

Our research discovered new clusters of neutrophils in sepsis. These five hub genes provide novel biomarkers targeting neutrophils for the treatment of sepsis.

Predictability of B cell clonal persistence and immunosurveillance in breast cancer

B cells and T cells are important components of the adaptive immune system and mediate anticancer immunity. The T cell landscape in cancer is well characterized, but the contribution of B cells to anticancer immunosurveillance is less well explored. Here we show an integrative analysis of the B cell and T cell receptor repertoire from individuals with metastatic breast cancer and individuals with early breast cancer during neoadjuvant therapy. Using immune receptor, RNA and whole-exome sequencing, we show that both B cell and T cell responses seem to coevolve with the metastatic cancer genomes and mirror tumor mutational and neoantigen architecture. B cell clones associated with metastatic immunosurveillance and temporal persistence were more expanded and distinct from site-specific clones. B cell clonal immunosurveillance and temporal persistence are predictable from the clonal structure, with higher-centrality B cell antigen receptors more likely to be detected across multiple metastases or across time. This predictability was generalizable across other immune-mediated disorders. This work lays a foundation for prioritizing antibody sequences for therapeutic targeting in cancer.

Disulfidptosis decoded: a journey through cell death mysteries, regulatory networks, disease paradigms and future directions

Cell death is an important part of the life cycle, serving as a foundation for both the orderly development and the maintenance of physiological equilibrium within organisms. This process is fundamental, as it eliminates senescent, impaired, or aberrant cells while also promoting tissue regeneration and immunological responses. A novel paradigm of programmed cell death, known as disulfidptosis, has recently emerged in the scientific circle. Disulfidptosis is defined as the accumulation of cystine by cancer cells with high expression of the solute carrier family 7 member 11 (SLC7A11) during glucose starvation. This accumulation causes extensive disulfide linkages between F-actins, resulting in their contraction and subsequent detachment from the cellular membrane, triggering cellular death. The RAC1-WRC axis is involved in this phenomenon. Disulfidptosis sparked growing interest due to its potential applications in a variety of pathologies, particularly oncology, neurodegenerative disorders, and metabolic anomalies. Nonetheless, the complexities of its regulatory pathways remain elusive, and its precise molecular targets have yet to be definitively identified. This manuscript aims to meticulously dissect the historical evolution, molecular underpinnings, regulatory frameworks, and potential implications of disulfidptosis in various disease contexts, illuminating its promise as a groundbreaking therapeutic pathway and target.

The development of prognostic gene markers associated with disulfidptosis in gastric cancer and their application in predicting drug response

Background

Gastric cancer (GC) is a malignancy known for its high fatality rate. Disulfidptosis, a potentially innovative therapeutic strategy for cancer treatment, has been proposed. Nevertheless, the specific involvement of disulfidptosis in the context of GC remains uncertain.

Methods

The mRNA expression profiles were obtained from the TCGA and GEO databases. Univariate and LASSO Cox regression analyses were employed to identify differentially expressed genes and develop a risk model for disulfidptosis-related genes. The performance of the model was evaluated using Kaplan-Meier curve, ROC curve, and nomogram. Univariate and multivariate Cox regression analyses were conducted to determine if the risk model could serve as an independent prognostic factor. The biological function of the identified genes was assessed through GO, KEGG, and GSEA analyses. The prediction of drug response was conducted employing the package "pRRophetic". Furthermore, gene expression was determined using qRT-PCR.

Results

An eight-gene signature were identified and utilized to categorize patients into low- and high-risk groups. Survival, receiver operating characteristic (ROC) curve, and Cox analyses provided clarification that these eight hub genes served as a favorable independent prognostic factor for patients with GC. A nomogram was constructed by integrating clinical parameters with the risk signatures, demonstrating high precision in predicting 1-, 3-, and 5-year survival rates. Additionally, drug sensitivity was different in the high-risk and low-risk groups, and the expression of three genes was verified by qRT-PCR.

Conclusion

The prognostic risk model developed in this study demonstrates the potential to accurately forecast the prognosis of patients with GC.

Single-cell disulfidptosis regulator patterns guide intercellular communication of tumor microenvironment that contribute to kidney renal clear cell carcinoma progression and immunotherapy

Background

Disulfidptosis, an emerging type of programmed cell death, plays a pivotal role in various cancer types, notably impacting the progression of kidney renal clear cell carcinoma (KIRC) through the tumor microenvironment (TME). However, the specific involvement of disulfidptosis within the TME remains elusive.

Methods

Analyzing 41,784 single cells obtained from seven samples of KIRC through single-cell RNA sequencing (scRNA-seq), this study employed nonnegative matrix factorization (NMF) to assess 24 disulfidptosis regulators. Pseudotime analysis, intercellular communication mapping, determination of transcription factor activities (TFs), and metabolic profiling of the TME subgroup in KIRC were conducted using Monocle, CellChat, SCENIC, and scMetabolism. Additionally, public cohorts were utilized to predict prognosis and immune responses within the TME subgroup of KIRC.

Results

Through NMF clustering and differential expression marker genes, fibroblasts, macrophages, monocytes, T cells, and B cells were categorized into four to six distinct subgroups. Furthermore, this investigation revealed the correlation between disulfidptosis regulatory factors and the biological traits, as well as the pseudotime trajectories of TME subgroups. Notably, disulfidptosis-mediated TME subgroups (DSTN+CD4T-C1 and FLNA+CD4T-C2) demonstrated significant prognostic value and immune responses in patients with KIRC. Multiple immunohistochemistry (mIHC) assays identified marker expression within both cell clusters. Moreover, CellChat analysis unveiled diverse and extensive interactions between disulfidptosis-mediated TME subgroups and tumor epithelial cells, highlighting the TNFSF12-TNFRSF12A ligand-receptor pair as mediators between DSTN+CD4T-C1, FLNA+CD4T-C2, and epithelial cells.

Conclusion

Our study sheds light on the role of disulfidptosis-mediated intercellular communication in regulating the biological characteristics of the TME. These findings offer valuable insights for patients with KIRC, potentially guiding personalized immunotherapy approaches.