Development of a polyamine gene expression score for predicting prognosis and treatment response in clear cell renal cell carcinoma

Identification and verification of a polyamine metabolism-related gene signature for predicting prognosis and immune infiltration in osteosarcoma

BACKGROUND

Although an established correlation exists between tumor cell proliferation and elevated polyamine levels, research on polyamine metabolism in osteosarcoma (OS) remains limited. This study aimed to identify polyamine metabolism-related genes (PMRGs) associated with OS prognosis and develop a prognostic model, thereby offering novel insights into targeted therapies for patients with OS.

METHODS

Datasets related to OS and PMRGs were sourced from publicly accessible databases. Candidate genes were initially identified through differential expression and weighted gene co-expression network analyses. Subsequently, prognostic genes were screened using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, leading to the development of a risk model. Furthermore, a nomogram model was developed using variables selected through univariate Cox regression analysis. The relationship between the signature and immune landscape was also analyzed. Following the pre-processing of single-cell RNA sequencing data, a cell communication analysis was conducted based on the identified cell types. Finally, the expression levels of prognostic genes in clinical samples were verified using reverse transcription quantitative polymerase chain reaction, western blotting and immunohistochemistry.

RESULTS

Ninety-six candidate genes were selected for univariate Cox and LASSO regression analyses, leading to the identification of eight prognostic genes: FAM162A, SIGMAR1, SQLE, PYCR1, DDI1, PAQR6, GRIA1, and TNFRSF12A. The risk model constructed from these genes demonstrated strong predictive accuracy and classified patients into two risk groups based on the median cut-off. A nomogram model was developed, incorporating the risk score as an independent prognostic factor. The high-risk cohort exhibited lower single-sample gene set enrichment analysis scores for 17 immune cell types and reduced expression levels of seven immune checkpoint-related genes. Furthermore, eight cell types were identified, among which endothelial cells, cancer-associated fibroblasts, osteoclasts, myeloid cells, and osteoblast OS cells showed significant interactions with NK/T, B, and plasma cells. Eight prognostic genes were confirmed to be overexpressed in OS tissues.

CONCLUSION

The identification of FAM162A, SIGMAR1, SQLE, PYCR1, DDI1, PAQR6, GRIA1, and TNFRSF12A as prognostic genes associated with PMRGs in OS provides valuable references for prognostic assessment and personalized treatment in patients with OS.

Identification of Immune Characteristics of 2 Subtypes of Breast Cancer by Combining Polyamine Metabolism-related Genes to Help With Immunotherapy

This project aims to explore the clustering value of polyamine metabolism-related genes (PMRGs) in breast cancer (BC) to assist treatment. ConsensusClusterPlus R package was employed to cluster BC patients based on the expression of PMRGs. Using the edgeR R package, we analyzed differentially expressed genes (DEGs) of different molecular clusters. Core genes were screened and enriched by the PPI network. Univariate COX was applied to determine genes tightly linked with survival. ConsensusClusterPlus R package was employed to cluster PMRGs. Differences in immune infiltration and expression of immune checkpoints between 2 subgroups were analyzed. Response to immunotherapy was assessed based on the expression level of immunophenoscore (IPS). Drug sensitivity of different PMRG clusters was assessed by pRRophitic R package. We clustered BC patients into 2 different subtypes with different survival rates and biological functions based on the expression of 16 PMRGs. Application of univariate COX analysis identified genes greatly associated with survival and divided BC patients into 2 different PMRG clusters. Patients in the 2 clusters exhibited differences in overall survival rate and immune cell infiltration levels, with multiple immune cells displaying higher immune levels in PMRG cluster 2. PMRG cluster 2 demonstrated higher expression of HLA and IC as well as IPS. Cluster 1 exhibited higher sensitivity to (5Z)-7-Oxozeaenol, 5-Fluorouracil, and 681640, while cluster 2 exhibited higher sensitivity to A-443654 and A-770041. We identified 2 clusters of PMRG with significant differences in the immune microenvironment in BC and predicted potential drugs, aiming to find new directions for clinical treatment of BC.

Leveraging Single-Cell Multi-Omics to Decode Tumor Microenvironment Diversity and Therapeutic Resistance

Recent developments in single-cell multi-omics technologies have provided the ability to identify diverse cell types and decipher key components of the tumor microenvironment (TME), leading to important advancements toward a much deeper understanding of how tumor microenvironment heterogeneity contributes to cancer progression and therapeutic resistance. These technologies are able to integrate data from molecular genomic, transcriptomic, proteomics, and metabolomics studies of cells at a single-cell resolution scale that give rise to the full cellular and molecular complexity in the TME. Understanding the complex and sometimes reciprocal relationships among cancer cells, CAFs, immune cells, and ECs has led to novel insights into their immense heterogeneity in functions, which can have important consequences on tumor behavior. In-depth studies have uncovered immune evasion mechanisms, including the exhaustion of T cells and metabolic reprogramming in response to hypoxia from cancer cells. Single-cell multi-omics also revealed resistance mechanisms, such as stromal cell-secreted factors and physical barriers in the extracellular matrix. Future studies examining specific metabolic pathways and targeting approaches to reduce the heterogeneity in the TME will likely lead to better outcomes with immunotherapies, drug delivery, etc., for cancer treatments. Future studies will incorporate multi-omics data, spatial relationships in tumor micro-environments, and their translation into personalized cancer therapies. This review emphasizes how single-cell multi-omics can provide insights into the cellular and molecular heterogeneity of the TME, revealing immune evasion mechanisms, metabolic reprogramming, and stromal cell influences. These insights aim to guide the development of personalized and targeted cancer therapies, highlighting the role of TME diversity in shaping tumor behavior and treatment outcomes.

Development of a novel centrosome-related risk signature to predict prognosis and treatment response in lung adenocarcinoma

BACKGROUND

Abnormalities of centrosomes, the major microtubular organizing centers of animal cells and regulators of cell cycle progression, usually accelerate tumor progression, but their prognostic value in lung adenocarcinoma (LUAD) remains insufficiently explored.

METHODS

We collected centrosome genes from the literature and identified LUAD-specific centrosome-related genes (CRGs) using the single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA). Univariate Cox was performed to screen prognostic CRGs. Consistent clustering was performed to classify LUAD patients into two subgroups, and centrosome-related risk score signatures were constructed by Lasso and multivariate Cox regression to predict overall survival (OS). We further explored the correlation between CRS and patient prognosis, clinical manifestations, mutation status, tumor microenvironment, and response to different treatments.

RESULTS

We constructed centrosome-associated prognostic features and verified that CRS could effectively predict 1-, 3-, and 5-year survival in LUAD patients. In addition, patients in the high-risk group exhibited elevated tumor mutational loads and reduced levels of immune infiltration, particularly of T and B cells. Patients in the high-risk group were resistant to immunotherapy and sensitive to 5-fluoropyrimidine and gefitinib. The key gene spermine synthase (SRM) is highly expressed at the mRNA and protein levels in LUAD.

DISCUSSION

Our work develops a novel centrosome-related prognostic signature that accurately predicts OS in LUAD and can assist in clinical diagnosis and treatment.

Polyamine Catabolism Revisited: Acetylpolyamine Oxidase Plays a Minor Role due to Low Expression

Biogenic polyamines are ubiquitous compounds. Dysregulation of their metabolism is associated with the development of various pathologies, including cancer, hyperproliferative diseases, and infections. The canonical pathway of polyamine catabolism includes acetylation of spermine and spermidine and subsequent acetylpolyamine oxidase (PAOX)-mediated oxidation of acetylpolyamines (back-conversion) or their direct efflux from the cell. PAOX is considered to catalyze a non-rate-limiting catabolic step. Here, we show that PAOX transcription levels are extremely low in various tumor- and non-tumor cell lines and, in most cases, do not change in response to altered polyamine metabolism. Its enzymatic activity is undetectable in the majority of cell lines except for neuroblastoma and low passage glioblastoma cell lines. Treatment of A549 cells with N1,N11-diethylnorspermine leads to PAOX induction, but its contribution to polyamine catabolism remains moderate. We also describe two alternative enzyme isoforms and show that isoform 4 has diminished oxidase activity and isoform 2 is inactive. PAOX overexpression correlates with the resistance of cancer cells to genotoxic antitumor drugs, indicating that PAOX may be a useful therapeutic target. Finally, PAOX is dispensable for the replication of various viruses. These data suggest that a decrease in polyamine levels is achieved predominantly by the secretion of acetylated spermine and spermidine rather than by back-conversion.

Polyamines: the pivotal amines in influencing the tumor microenvironment

Cellular proliferation, function and survival is reliant upon maintaining appropriate intracellular polyamine levels. Due to increased metabolic needs, cancer cells elevate their polyamine pools through coordinated metabolism and uptake. High levels of polyamines have been linked to more immunosuppressive tumor microenvironments (TME) as polyamines support the growth and function of many immunosuppressive cell types such as MDSCs, macrophages and regulatory T-cells. As cancer cells and other pro-tumorigenic cell types are highly dependent on polyamines for survival, pharmacological modulation of polyamine metabolism is a promising cancer therapeutic strategy. This review covers the roles of polyamines in various cell types of the TME including both immune and stromal cells, as well as how competition for nutrients, namely polyamine precursors, influences the cellular landscape of the TME. It also details the use of polyamines as biomarkers and the ways in which polyamine depletion can increase the immunogenicity of the TME and reprogram tumors to become more responsive to immunotherapy.

Identification and Validation of eRNA as a Prognostic Indicator for Cervical Cancer

The survival of CESC patients is closely related to the expression of enhancer RNA (eRNA). In this work, we downloaded eRNA expression, clinical, and gene expression data from the TCeA and TCGA portals. A total of 7936 differentially expressed eRNAs were discovered by limma analysis, and the relationship between these eRNAs and survival was analyzed by univariate Cox hazard analysis, LASSO regression, and multivariate Cox hazard analysis to obtain an 8-eRNA model. Risk score heat maps, KM curves, ROC analysis, robustness analysis, and nomograms further indicate that this 8-eRNA model is a novel indicator with high prognostic performance independent of clinicopathological classification. The model divided patients into high-risk and low-risk groups, compared pathway diversity between the two groups through GSEA analysis, and provided potential therapeutic agents for high-risk patients.