Prognostic significance of ferroptosis pathway gene signature and correlation with macrophage infiltration in cervical squamous cell carcinoma

EGFR overexpression and macrophage infiltration correlate with poorer prognosis in HPV-negative oropharyngeal cancer via STAT6 signaling

BACKGROUND

The prevalence of HPV-negative oropharyngeal cancer (OPC) is higher in Asian countries. Patients with HPV-negative OPC suffer poor outcomes. Multi-omics analysis could provide researchers and clinicians with more treatment targets for this high-risk group. We aimed to explore the prognostic significance of EGFR overexpression and macrophage infiltration in OPC, especially HPV-negative OPC in this study.

METHODS

EGFR alternation was evaluated with TCGA, PanCancer Atlas through cBioProtal. EGFR mRNA expression in HPV-negative head and neck squamous cell carcinoma was analyzed using the Tumor Immune Estimation Resource (TIMER 2.0). We also examined EGFR/STAT6/MRC1 expression in paraffin-embedded tissues from a p16-negative OPC cohort. The correlation between EGFR expression and macrophage activation was explored using Person's correlation coefficient. The impact of biomarkers or macrophage infiltration on 5-year overall survival and recurrence-free survival were analyzed using Kaplan-Meier survival curves.

RESULTS

EGFR alteration rate was 15%, 13%, and 0% for HPV-negative HNSCC (excluding OPC), HPV-negative OPC, and HPV-positive OPC. High EGFR expression was associated with increased tumor infiltration of immune cells, such as macrophages. We observed positive correlations between EGFR, STAT6, and MRC1 expression in p16-negative OPC. Higher MRC1 expression was associated with poorer survival rates.

CONCLUSIONS

There is strong correlation between EGFR overexpression and M2 polarization in patients with p16-negative OPC. Immunotherapy with or without EGFR inhibitor could be considered in these high-risk patients.

Cuproptosis-Related Gene FDX1 Identified as a Potential Target for Human Ovarian Aging

Cuproptosis is a recently discovered mode of cell death that has garnered attention due to its association with various diseases. However, the intricate genetic relationship between cuproptosis and ovarian aging has remained largely unexplored. This study aimed to bridge this knowledge gap by leveraging data sets related to ovarian aging and cuproptosis. Through comprehensive bioinformatics analyses, facilitated by R software, we uncovered FDX1 as a potential cuproptosis-related gene with relevance to ovarian aging. To gain insights into FDX1's role, we conducted spatial transcriptome analyses in the ovaries of both young and aged female mice. These experiments revealed a significant reduction in FDX1 expression in the aging group compared to the young group. To substantiate these findings at the genetic level, we turned to clinical infertility biopsies. Impressively, we observed consistent results in biopsies from elderly infertile patients, reinforcing the link between FDX1 and ovarian aging. Moreover, we delved into the pharmacogenomics of ovarian cell lines and discovered that FDX1 expression levels were intricately associated with heightened sensitivity to specific small molecule drugs. This observation suggests that modulating FDX1 could potentially be a strategy to influence drug responses in ovarian-related therapies. In sum, this study marks a pioneering effort in identifying FDX1 as a cuproptosis-related gene implicated in ovarian aging. These findings hold substantial promise, not only in shedding light on the underlying mechanisms of ovarian aging but also in positioning FDX1 as a potential diagnostic biomarker and therapeutic target. With further research, FDX1 could play a pivotal role in advancing precision medicine and therapies for ovarian-related conditions.

Spatial and single-cell analyses uncover links between ALKBH1 and tumor-associated macrophages in gastric cancer

BACKGROUND

AlkB homolog 1, histone H2A dioxygenase (ALKBH1), a crucial enzyme involved in RNA demethylation in humans, plays a significant role in various cellular processes. While its role in tumor progression is well-established, its specific contribution to stomach adenocarcinoma (STAD) remains elusive. This study seeks to explore the clinical and pathological relevance of ALKBH1, its impact on the tumor immune microenvironment, and its potential for precision oncology in STAD.

METHODS

We adopted a comprehensive multi-omics approach to identify ALKBH1 as an potential diagnostic biomarker for STAD, demonstrating its association with advanced clinical stages and reduced overall survival rates. Our analysis involved the utilization of publicly available datasets from GEO and TCGA. We identified differentially expressed genes in STAD and scrutinized their relationships with immune gene expression, overall survival, tumor stage, gene mutation profiles, and infiltrating immune cells. Moreover, we employed spatial transcriptomics to investigate ALKBH1 expression across distinct regions of STAD. Additionally, we conducted spatial transcriptomic and single-cell RNA-sequencing analyses to elucidate the correlation between ALKBH1 expression and immune cell populations. Our findings were validated through immunohistochemistry and bioinformatics on 60 STAD patient samples.

RESULTS

Our study unveiled crucial gene regulators in STAD linked with genetic variations, deletions, and the tumor microenvironment. Mutations in these regulators demonstrated a positive association with distinct immune cell populations across six immune datasets, exerting a substantial influence on immune cell infiltration in STAD. Furthermore, we established a connection between elevated ALKBH1 expression and macrophage infiltration in STAD. Pharmacogenomic analysis of gastric cancer cell lines further indicated that ALKBH1 inactivation correlated with heightened sensitivity to specific small-molecule drugs.

CONCLUSION

In conclusion, our study highlights the potential role of ALKBH1 alterations in the advancement of STAD, shedding light on novel diagnostic and prognostic applications of ALKBH1 in this context. We underscore the significance of ALKBH1 within the tumor immune microenvironment, suggesting its utility as a precision medicine tool and for drug screening in the management of STAD.

Unraveling the Clinical Relevance of Ferroptosis-Related Genes in Human Ovarian Aging

Ferroptosis, a recently discovered form of cell death, has been implicated in various diseases. However, the genetic relationship between ferroptosis and ovarian aging has not been thoroughly investigated through informatics analysis. In this study, we conducted bioinformatics analysis using ovarian aging and ferroptosis datasets to identify potential ferroptosis-related genes using R software. The expression levels of these genes at different ages were analyzed using the GTEx public database. To validate these findings at the genetic level, we performed clinical infertility biopsies. Bioinformatics analysis of a mouse ovary dataset revealed significantly higher expression of Tfrc, Ncoa4, and Slc3a2 in the aging group compared to the young group, while Gpx4 showed the opposite pattern. Consistent results were observed in biopsies from clinically aged infertile patients. This study is the first to identify a ferroptosis-related gene associated with ovarian aging, highlighting its potential as a diagnostic biomarker.

The role of LSM1 in breast cancer: Shaping metabolism and tumor-associated macrophage infiltration

LSM1 is part of the cytoplasmic protein complex Lsm1-7-Pat1 and is likely involved in pre-mRNA degradation by aiding U4/U6 snRNP formation. More research is needed to uncover LSM1's potential in breast cancer (BRCA) clinical pathology, the tumor immune microenvironment, and precision oncology. We discovered LSM1 as a diagnostic marker for advanced BRCA with poor survival, using a multi-omics approach. We studied LSM1 expression across BRCA regions and its link to immune cells through various methods, including spatial transcriptomics and single-cell RNA-sequencing. We also examined how silencing LSM1 affects mitochondrial function and energy metabolism in the tumor environment. These findings were confirmed using 54 BRCA patient biopsies and tissue microarrays. Immunofluorescence and bioinformatics assessed LSM1's connection to clinicopathological features and prognosis. This study uncovers gene patterns linked to breast cancer, with LSM1 linked to macrophage energy processes. Silencing LSM1 in breast cancer cells disrupts mitochondria and energy metabolism. Spatial analysis aligns with previous results, showing LSM1's connection to macrophages. Biopsies confirm LSM1 elevation in advanced breast cancer with increased macrophage presence. To summarize, LSM1 changes may drive BRCA progression, making it a potential diagnostic and prognostic marker. It also influences energy metabolism and the tumor's immune environment during metastasis, showing promise for precision medicine and drug screening in BRCA.

Emerging trends in clinical research on Janus kinase inhibitors for atopic dermatitis treatment

Atopic dermatitis (AD) is a chronic inflammatory skin disease that affects millions of people worldwide, characterized by immune function imbalance and impaired epidermal barrier function. It is a complex disorder that involves multiple pathogenic pathways, including the JAK/STAT signaling pathway, which plays a critical role in regulating immune and inflammatory responses. The therapeutic potential of Janus kinase inhibitors (JAKi) in the management of atopic dermatitis (AD) has garnered significant interest in recent years. AD is a chronic inflammatory skin condition characterized by impaired epidermal barrier function and immune function imbalance, and its pathogenesis is closely associated with dysregulated JAK/signal transducer and activator of transcription (STAT) signaling pathways. JAKi offer a novel therapeutic approach by selectively inhibiting JAK enzymes, thereby blocking downstream STAT signaling and preventing the expression of cytokines involved in AD pathogenesis. This review will focus on several JAKi including tofacitinib, baricitinib, ruxolitinib and upadacitinib, and provide a comprehensive overview of the latest research on the application of JAKi in AD treatment, including its mechanism of action, clinical trial results and safety profile.

Single-cell landscape and spatial transcriptomic analysis reveals macrophage infiltration and glycolytic metabolism in kidney renal clear cell carcinoma

The present study investigates the clinical relevance of glycolytic factors, specifically PGAM1, in the tumor microenvironment of kidney renal clear cell carcinoma (KIRC). Despite the established role of glycolytic metabolism in cancer pathophysiology, the prognostic implications and key targets in KIRC remain elusive. We analyzed GEO and TCGA datasets to identify DEGs in KIRC and studied their relationship with immune gene expression, survival, tumor stage, gene mutations, and infiltrating immune cells. We explored Pgam1 gene expression in different kidney regions using spatial transcriptomics after mouse kidney injury analysis. Single-cell RNA-sequencing was used to assess the association of PGAM1 with immune cells. Findings were validated with tumor specimens from 60 KIRC patients, correlating PGAM1 expression with clinicopathological features and prognosis using bioinformatics and immunohistochemistry. We demonstrated the expression of central gene regulators in renal cancer in relation to genetic variants, deletions, and tumor microenvironment. Mutations in these hub genes were positively associated with distinct immune cells in six different immune datasets and played a crucial role in immune cell infiltration in KIRC. Single-cell RNA-sequencing revealed that elevated PGAM1 was associated with immune cell infiltration, specifically macrophages. Furthermore, pharmacogenomic analysis of renal cancer cell lines indicated that inactivation of PGAM1 was associated with increased sensitivity to specific small-molecule drugs. Altered PGAM1 in KIRC is associated with disease progression and immune microenvironment. It has diagnostic and prognostic implications, indicating its potential in precision medicine and drug screening.

Dual identity of tumor-associated macrophage in regulated cell death and oncotherapy

Tumor-associated macrophage (TAM) affects the intrinsic properties of tumor cells and the tumor microenvironment (TME), which can stimulate tumor cell proliferation, migration, and genetic instability, and macrophage diversity includes the diversity of tumors with different functional characteristics. Macrophages are now a central drug target in various diseases, especially in the TME, which, as "tumor promoters" and "immunosuppressors", have different responsibilities during tumor development and accompany by significant dynamic alterations in various subpopulations. Remodelling immunosuppression of TME and promotion of pre-existing antitumor immune responses is critical by altering TAM polarization, which is relevant to the efficacy of immunotherapy, and uncovering the exact mechanism of action of TAMs and identifying their specific targets is vital to optimizing current immunotherapies. Hence, this review aims to reveal the triadic interactions of macrophages with programmed death and oncotherapy, and to integrate certain relationships in cancer treatment.

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

BACKGROUND

Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine learning, the latest tool for the analysis of biological samples, is still relatively rarely used for in-depth analysis and prediction of diseases.

METHODS AND RESULTS

First, the differential expression of cuproptosis-related genes (CRGs) in the GSE108754 dataset was extracted and the heat map showed that the expression of NFE2L2 gene was significantly higher in the control group whereas the expression of GLS gene was significantly higher in the treatment group. Chromosome location analysis showed that both the genes were positively correlated and associated with chromosome 2. The results of immune infiltration and immune cell differential analysis showed differences in the four immune cells, significantly in Monocytes cells. Five new pathways were analyzed through two subgroups based on consistent clustering of CRG expression. Weighted correlation network analysis (WGCNA) set the screening condition to the top 25% to obtain the disease signature genes. Four machine learning algorithms: Generalized Linear Models (GLM), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) were used to screen the disease signature genes, and the final five marker genes for disease prediction. The models constructed by GLM method were proved to be more accurate in the validation of two datasets, GSE190215 and GSE188944.

CONCLUSION

We eventually identified two copper death-associated genes, NFE2L2 and GLS. A machine learning model-GLM was constructed to predict the prevalence of BPD disease, and five disease signature genes NFATC3, ERMN, PLA2G4A, MTMR9LP and LOC440700 were identified. These genes that were bioinformatics analyzed could be potential targets for identifying BPD disease and treatment.