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Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer.
Background: Immunotherapy is a promising strategy for ovarian cancer (OC), and this study aims to identify biomarkers related to CD8+ T cell infiltration to further discover the potential therapeutic target. Methods: Three datasets with OC transcriptomic data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Two immunotherapy treated cohorts were obtained from the Single Cell Portal and Mariathasan's study. The infiltration fraction of immune cells was quantified using three different algorithms, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and microenvironment cell populations counter (MCPcounter), and single-sample GSEA (ssGSEA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the co-expression modules and related genes. The nonnegative matrix factorization (NMF) method was proposed for sample classification. The mutation analysis was conducted using the "maftools" R package. Key molecular markers with implications for prognosis were screened by univariate COX regression analysis and K-M survival analysis, which were further determined by the receiver operating characteristic (ROC) curve. Results: A total of 313 candidate CD8+ T cell-related genes were identified by taking the intersection from the TCGA-OV and GSE140082 cohorts. The NMF clustering analysis suggested that patients in the TCGA-OV cohort were divided into two clusters and the Cluster 1 group showed a worse prognosis. In contrast, Cluster 2 had higher amounts of immune cell infiltration, elevated ssGSEA scores in immunotherapy, and a higher mutation burden. CSMD3, MACF1, PDE4DIP, and OBSCN were more frequently mutated in Cluster 1, while SYNE2 was more frequently mutated in Cluster 2. CD38 and CXCL13 were identified by univariate COX regression analysis and K-M survival analysis in the TCGA-OV cohort, which were further externally validated in GSE140082 and GSE32062. Of note, patients with lower CXCL13 expression showed a worse prognosis and the CR/PR group had a higher expression of CXCL13 in two immunotherapy treated cohorts. Conclusion: OC patients with different CD8+ T cell infiltration had distinct clinical prognoses. CXCL13 might be a potential therapeutic target for the treatment of OC.
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Polyamine-related 4-gene prognostic signature in hepatocellular carcinoma.
This study aimed to explore the biological and clinical roles of polyamine-related genes (PRGs) in hepatocellular carcinoma (HCC) and to construct a PRG-based prognostic signature for prognostic assessment and precision therapy guidance in HCC. Twenty-two PRGs were retrieved from the Molecular Signatures Database. Transcriptome, clinical, and somatic mutation data of HCC were integrated from The Cancer Genome Atlas, International Cancer Genome Collaboratory, National Omics Data Encyclopedia, and Gene Expression Omnibus. Consensus clustering identified PRG subtypes, and gene set variation analysis screened differentially-expressed genes. Univariate Cox, Least Absolute Shrinkage and Selection Operator, and multivariate Cox regression analyses were used to construct a prognostic signature, validated in internal and 4 external cohorts. Immune infiltration was analyzed via single-sample gene set enrichment analysis, CIBERSORT, and ESTIMATE. The signature's predictive value for chemotherapy, transcatheter arterial chemoembolization, and immunotherapy was assessed with GDSC, tumor immune dysfunction and exclusion and relevant therapy cohorts, and its performance was compared with 4 published HCC signatures. The expression of signature genes was verified at the mRNA, protein, and single-cell levels. Two PRG subtypes were identified in HCC. Cluster A correlated with poor prognosis, high immune infiltration, and low metabolic activity. A 4-gene signature (KIF20A, SPP1, PON1, and SERPINE1) was established, effectively stratifying HCC patients into high- and low-risk groups with significantly different overall survival. It was an independent prognostic factor with better predictive performance than published signatures, and the high-risk group had a higher TP53 mutation rate, distinct immune infiltration patterns, and was predicted to have poor therapy responses. The 4 signature genes showed abnormal expression in HCC tissues at multiple levels. This study identified 2 PRG subtypes and a robust 4-gene prognostic signature for HCC, which accurately predicts prognosis, reflects tumor immune microenvironment features, and guides precision therapy selection, highlighting PRGs' potential as HCC biomarkers and therapeutic targets.
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Evidence for altered immune-structural cell crosstalk in cystic fibrosis revealed by single cell transcriptomics.
BACKGROUND: Chronic pulmonary inflammation strongly contributes to respiratory failure and mortality in patients with cystic fibrosis (pwCF). Effective anti-microbial immunity and maintaining lung homeostasis require continuous structural-immune cell communication. Whether and how this crosstalk is altered in CF remains poorly understood, obscuring potential new angles for therapy development to restore airway homeostasis in pwCF. METHODS: We performed droplet-based single cell RNA-sequencing on bronchial biopsies from pwCF to investigate structural-immune cell crosstalk. Computational analyses were used to compare these data to samples obtained from healthy controls. RESULTS: CF airway wall biopsies showed lower proportions and altered transcriptomes of basal cells, submucosal gland cells and endothelial cells, and a higher abundance of ciliated cells, monocytes, macrophages and T cells. Both B and T lymphocytes displayed aberrantly activated phenotypes with transcriptional changes linked to hypoxia and vascular endothelial growth factor signaling, indicative of crosstalk with endothelial cells. The CF lung displayed unique changes in intercellular communication potential involving ionocytes, macrophages, endothelial cells and lymphocytes. This included interactions between HLA-E on structural cells and the druggable CD94/NKG2A immune checkpoint on CD8+T cells. CONCLUSIONS: We report the first single cell transcriptome atlas of the CF lung containing the full spectrum of structural and immune cells, providing a valuable resource for investigating changes to cellular composition, phenotypes and crosstalk linked to CF. Our analyses highlight dysregulated basal cell function and adaptive immunity in pwCF - despite favorable responses to CFTR modulator therapy. We identify novel aspects of CF pathophysiology and potential entry points for therapeutic strategies.
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[The Expression of RTN1 in Lung Adenocarcinoma and Its Effect on Immune Microenvironment].
BACKGROUND: Reticulosome family gene 1 (RTN1) is a reticulosome-encoding gene associated with the endoplasmic reticulum. RTN1 plays a key role in membrane trafficking or neuroendocrine secretion of neuroendocrine cells, while RTN1 serves as a potential diagnostic/therapeutic marker for neurological diseases and cancer. However, the expression of RTN1 and its effect on the immune microenvironment in patients with lung adenocarcinoma have not been reported. In this study, we aimed to investigate the expression of RTN1 in lung adenocarcinoma and its correlation with immune infiltration and survival in lung adenocarcinoma using public databases and bioinformatics network tools. METHODS: Expression levels of RTN1 mRNA in tumor and normal tissues were analyzed using Tumor Immune Estimation Resource 2.0 (TIMER 2.0) and Gene Expression Profiling Interactive Analysis 2 (GEPIA 2). RTN1 protein expression was examined using the Human Protein Atlas. The clinical prognostic significance of RTN1 was analyzed using the GEPIA2 plotter database. To further confirm the potential function of RTN1, the data were analyzed using gene set enrichment analysis. In addition, We performed dimensionality-reduced clustering analysis at the single-cell sequencing level on two datasets from the Tumor Immune Single-cell Hub (TISCH) database to observe the cellular clustering of RTN1 in different types of immune cells. Using the TIMER online tool to analyze and predict the infiltration abundance of different types of immune cells in the immune microenvironment of lung adenocarcinoma patients in the TCGA cohort; TIMER and CIBERSORT were used to study the relationship between genes co-expressed with RTN1 and its associated tumor-infiltrating immune cells; finally, TIMER was used to analyze the relationship between RTN1 and immune correlations between immune checkpoints. RESULTS: We found that RTN1 expression was decreased in patients with lung adenocarcinoma and was closely related to patient prognosis. RTN1 is involved in the process of phagosome formation, hematopoietic cell formation and cell adhesion, and plays an important role in T cell activation. Using cBioPortal and TCGA data to analyze, it is found that RTN1 is significantly associated with BTK, CD4, ECSF1R, MNDA, NCKAP1L and SNX20. High expression of the above genes may cause significant upregulation of CD4+ T cells, mast cells, monocytes, myeloid dendritic cells and M1 macrophages. The expression of RTN1 is closely related to the common immune checkpoints CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT and SIGLEC15 immune checkpoints. CONCLUSIONS: RTN1 may act as a tumor suppressor gene and indicate better prognosis. Furthermore, RTN1 is associated with immune infiltration that may be involved in the immunotherapy response in LUAD. However, the related mechanism needs further research.
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Prognostic role of CDCA3 in lung adenocarcinoma with immune infiltration.
Lung cancer is a leading cause of cancer-related deaths, often resistant to conventional therapies like platinum-based chemotherapy. Identifying new biomarkers and therapeutic targets is essential for improving outcomes. In this study, we explored the role of CDCA3 in lung adenocarcinoma (LUAD) as a prognostic marker and its association with immune cell infiltration and immunotherapy. Using data from The Cancer Genome Atlas (TCGA), we evaluated CDCA3 expression and its prognostic significance through Kaplan-Meier survival analysis. Our results showed that CDCA3 was significantly upregulated in LUAD tumor tissues compared to normal tissues, with higher CDCA3 expression linked to poorer overall survival. Additionally, CDCA3 expression correlated with clinical features such as age, gender, cancer stage, and smoking status. Immune infiltration analysis revealed significant associations with CD4-activated memory T cells and macrophages. Single‑cell transcriptomics revealed that the proportions of CD8⁺ exhausted T cells, CD8⁺ cytotoxic T cells, M1 macrophages and M2 macrophages varied in association with CDCA3 expression levels in LUAD. CDCA3 expression also correlated with immune scores and immune checkpoint gene expression. Drug sensitivity analysis using the "oncoPredict" R package suggested that CDCA3 expression may influence chemotherapy responses. In vitro experiments in A549 cells showed that CDCA3 knockdown significantly reduced CDCA3 expression at both the mRNA and protein levels. While flow cytometry indicated increased apoptosis in CDCA3-knockdown cells, suggesting CDCA3's role in promoting LUAD cell proliferation and survival. These findings indicated that CDCA3 could serve as a valuable prognostic biomarker in LUAD, influencing prognosis, immune response, and drug sensitivity, potentially guiding therapeutic and immunotherapy decisions.
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Integrative multi-omics analysis identifies mitochondria- and ferroptosis-related prognostic genes in cervical cancer.
BACKGROUND: Mitochondria and ferroptosis are crucial in tumorigenesis. However, their specific role in cervical cancer (CC) remains unclear. This study aimed to identify and validate prognostic genes linked to mitochondrial function and ferroptosis in CC. METHODS: Publicly available datasets were analyzed, including 306 CC tumor samples from The Cancer Genome Atlas-Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC), with survival data for 293 samples, a training set of 24 normal and 33 tumor tissues (GSE9750), and a validation set of 300 tumor tissues (GSE44001). Prognostic genes associated with mitochondria-related genes (MRGs) and ferroptosis-related genes (FRGs) were identified through machine learning, univariate Cox regression, Weighted Gene Co-expression Network Analysis (WGCNA), Mendelian randomization (MR), differential expression analysis, and multivariate Cox analysis. A risk model was constructed and validated, with the High-Risk Group (HRG) and Low-Risk Group (LRG) defined by optimal risk score thresholds. Independent prognostic analysis, functional enrichment, immune infiltration profiling, and single-cell resolution studies were conducted to explore the underlying molecular mechanisms. Additionally, gene expression was validated in five paired clinical samples (5 tumor/5 normal tissues) using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: HSDL2, AMACR, and CBR3 were identified as prognostic genes. The risk model indicated significantly poorer survival rates in HRG patients (P < 0.05). It demonstrated strong predictive performance, with area under the curve (AUC) values exceeding 0.7 in both the training and validation sets. The risk score, tumor (T) stage, and lymph node (N) stage were identified as independent prognostic factors for a nomogram model (hazard ratio (HR ≠ 1, P < 0.5). Pathways co-enriched by these markers, such as allograft rejection, were investigated. Immune infiltration analysis revealed significant differences between HRG and LRG in M0 macrophages and resting myeloid dendritic cells (mDCs) (P < 0.5). Macrophages and epithelial/cancer cells were identified as key contributors to CC progression, exhibiting 13 and 7 distinct differentiation states, respectively, in pseudo-time analysis. Notably, HSDL2 and CBR3 expression levels were significantly different between normal and CC samples (P < 0.05). CONCLUSION: HSDL2, AMACR, and CBR3 were established as prognostic biomarkers for CC. The risk model demonstrated robust predictive accuracy, offering a scientific foundation for clinical prognosis prediction in CC.
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Intratumor APOL3 delineates a distinctive immunogenic ferroptosis subset with prognosis prediction in colorectal cancer.
With the essential role of lipid transporting signaling in cancer-related immunity, apolipoprotein L3 (APOL3), a member of the apolipoprotein L gene family, demonstrated significant modulation ability in immunity. However, the expression profile and critical role of APOL3 in colorectal cancer (CRC) remain unclear. This study aimed to investigate the prognostic significance of APOL3 expression and its biological predictive value in CRC. The study enrolled multiple cohorts, consisting of 911 tumor microarray specimens of CRC patients from Zhongshan Hospital, 412 transcriptional data from The Cancer Genome Atlas, and 30 single-cell RNA sequencing (scRNA-seq) from internal and external CRC patients. APOL3 mRNA expression was directly acquired from public datasets, and APOL3 protein expression was detected using immunohistochemistry. Finally, the associations of APOL3 expression with clinical outcomes, immune context, and genomic and ferroptotic features were analyzed. Low APOL3 expression predicted poor prognosis and inferior responsiveness to 5-fluorouracil-based adjuvant chemotherapy (ACT) and targeted therapy. APOL3 fosters an immune-active microenvironment characterized by the promotion of ferroptosis, downregulation of macrophages, and upregulation of CD8+ T cell infiltration. Moreover, the expression of APOL3 in CD8+ T cells is intrinsically linked to ferroptosis and immune activation in CRC. In summary, APOL3 serves as an independent prognosticator and predictive biomarker for immunogenic ferroptosis, ACT, and targeted therapy in CRC. Furthermore, the APOL3 signaling activator could be a novel agent alone or in combination with current therapeutic strategies for CRC.
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Identification of Essential Tumor-Infiltrating Immune Cells and Relevant Genes in Left-Sided and Right-Sided Colon Cancers.
BACKGROUNDS: Colorectal cancer is the third most prevalent cancer worldwide. A right-sided colon cancer patient typically has a worse prognosis than one who has a left-sided colon cancer. There is an unclear understanding of how left-sided colon cancer differs from right-sided colon cancer in tumor-infiltrating immune cells (TIICs) and relevant genes. METHODS: The Cancer Genome Atlas provided RNA-seq data and clinical information regarding colon adenocarcinoma. We conducted a single-sample gene set enrichment analysis (ssGSEA) to quantify the level of 24 immune cells infiltrating the tissues. Based on an analysis of univariate Cox regression, immune cell types associated with survival were identified. Weighted gene co-expression network analysis (WGCNA) was used to identify hub genes related to location and critical immune cells. Based on the Search Tool for the Retrieval of Interacting Genes (STRING), interaction potential was predicted among the hub genes. Hub genes that influence outcomes through immune infiltration were identified using the least absolute shrinkage and selection operator (LASSO). Then, we used the TISIDB database (a repository portal for tumor-immune system interactions) to validate the correlation between hub genes and immune cell infiltration. Finally, immunohistochemical assays were conducted to determine the levels of proteins expressed by critical TIICs and cancer cells. RESULTS: Colon cancers on the right side of the body had higher levels of myeloid-derived suppressor cells (MDSCs) than on the left side. There were three key genes: LCP1, ITGB2, and IKZF1. It was found that their expression was linked to poor prognosis and an increased level of MDSC infiltration. An immunohistochemical study confirmed these findings. CONCLUSIONS: There is a higher rate of MDSC infiltration in right-sided colon cancer when compared with left-sided colon cancer. COAD outcomes are associated with changes in MDSC infiltration, and therefore LCP1, ITGB2, and IKZF1 may be novel targets for immunotherapy.
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Prognosis and progression of phagocytic regulatory factor-related gene combinations in clear cell renal cell carcinoma (ccRCC).
BACKGROUND: Developing signatures based on specific characteristics to predict prognosis has become a research hotspot in oncology. However, the prognostic value of phagocytosis regulators in clear cell renal cell carcinoma (ccRCC) remains unclear. The aim of the present study was to investigate the prognostic significance of phagocytosis regulators in ccRCC by constructing a prognostic model related to phagocytosis regulators, and to use this model to evaluate the prognosis and treatment effects in ccRCC patients. METHODS: Firstly, kidney renal clear cell carcinoma (KIRC) transcriptome data (RNA-Seq) and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Based on literatures PMID 34497417 and PMID 30397336, 167 of the 173 phagocytosis regulator genes collected in the literature were expressed in TCGA-KIRC. The relationship between these regulators and macrophages was revealed through single-sample gene set enrichment analysis (ssGSEA), and their biological and pathway involvements were further analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) method were employed to further select phagocytosis regulators with prognostic potential, leading to the construction of a prognostic regression model. Additionally, univariate and multivariate Cox regression analyses were conducted to confirm the prognostic independence of genes associated with phagocytosis regulators. Finally, the relationship between phagocytosis regulator-related genes and patients' immune microenvironments and immunotherapy responses was explored. RESULTS: We have constructed a prognostic model of a combination of genes associated with phagocytosis regulators using LASSO Cox regression analysis of genes, and our combined model was shown to be an independent prognostic factor. The model had optimal performance in predicting long-term survival. Clinical features were significantly correlated with phagocytosis regulatory gene scores. Tumors with higher levels of grade and stage were more prone to have higher phagocytosis regulatory genes. And our study suggests that phagocytosis regulatory genes do not play an ideal role in predicting the efficacy of immunotherapy in patients. CONCLUSIONS: We have constructed a prognostic model using a combination of genes associated with phagocytosis regulators, providing new insights into the prognosis and progression of ccRCC.
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Comprehensive analysis based on IFN-γ and SASP related genes, bulk RNA and single-cell sequencing to evaluate the prognosis and immune landscape of stomach adenocarcinoma.
BACKGROUND: Stomach adenocarcinoma (STAD) represents the predominant subtype of gastric cancer, known for its drug resistance, unfavorable prognosis, and low cure rates. IFN-γ serves as a cytokine generated by immune cells, instrumental in tumor immune clearance and essential to the tumor microenvironment. The aging-associated secretory phenotype (SASP) can modify the local tissue environment, facilitating gastric cancer progression and chemotherapy resistance. OBJECTIVE: This study intends to identify STAD subtypes based on IFN-γ and SASP-related genes and to develop a risk prognostic model for predicting patient survival, tumor immune microenvironment, and responses to drug treatment. METHODS: The genomic and clinical datasets originate from the Cancer Genome Atlas (TCGA) database, while the genes associated with IFN-γ and SASP come from pertinent scholarly articles. We discovered the prognostic genes linked to IFN-γ and SASP in STAD using Cox regression analysis. Next, we applied non-negative matrix factorization (NMF) to categorize LIHC into distinct molecular subtypes, identifying differentially expressed genes across these subtypes. Following this, we developed a predictive model using Cox and LASSO regression analyses to stratify patients into specific risk categories, validating the model to assess the prognostic significance of the identified signatures. Lastly, we integrated single-cell data to elucidate the immune landscape of STAD and identified potential drugs along with their sensitivity profiles. RESULTS: We identified 17 prognostic genes related to IFN-γ and SASP, successfully classifying patients into two distinct molecular subtypes. These subtypes exhibited notable differences in immune profiles and prognostic outcomes. We pinpointed three differentially expressed genes to establish risk characteristics and created a prognostic model capable of accurately predicting patient outcomes. Our findings revealed a strong association between STAD and the extracellular matrix, low-risk group exhibited favorable prognosis, and may derive greater benefits from immunotherapy. CONCLUSION: We developed a risk model using IFN-γ and SASP-associated genes to predict the prognosis of STAD patients more accurately. Additionally, we assessed the immune landscape of STAD by integrating bulk RNA and single-cell sequencing analyses. This approach may yield valuable insights for clinical decision-making and immunotherapy strategies in STAD.