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Identification and validation of Kif15 as a potential diagnostic and prognostic biomarker in colon cancer.
OBJECTIVE: This study aimed to systematically identify and validate KIF15 as a potential diagnostic and prognostic biomarker in colon cancer (CC) using integrated bioinformatics analyses. We further explored its role in the tumor immune microenvironment and its potential value in immunotherapy, and validated its expression and clinical significance through immunohistochemical analysis. METHODS: Gene expression profiles of CC were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). A protein–protein interaction (PPI) network was constructed, and key genes were identified using the MCODE plugin in Cytoscape. External validation of candidate gene expression was performed using The Cancer Genome Atlas (TCGA) cohort. Kaplan–Meier survival analysis was conducted to evaluate prognostic significance. Receiver operating characteristic (ROC) and time-dependent ROC analyses were applied to assess diagnostic and prognostic performance. A nomogram model was established based on multivariate Cox regression, and its predictive accuracy and stability were evaluated using calibration curves and decision curve analysis (DCA). To investigate the role of KIF15 in the tumor microenvironment, single-cell RNA sequencing data from the TISCH database were analyzed to determine its expression distribution across immune cell subsets. Functional enrichment and immune correlation analyses were performed to elucidate the potential molecular mechanisms of KIF15 in CC progression and to identify downstream hub genes. Finally, immunohistochemistry (IHC) was conducted to validate the tissue expression pattern and clinical relevance of KIF15. RESULTS: A total of 611 DEGs were identified by integrating three GEO datasets (GSE24550, GSE21815, and GSE44076). Based on PPI network construction and MCODE analysis, 13 key genes, including KIF15, were identified. Combined univariate Cox regression and pan-cancer analysis ultimately determined KIF15 as the core gene of interest. TCGA analysis demonstrated that KIF15 was significantly upregulated in CC tissues (P < 0.0001), with a diagnostic ROC AUC of 0.874. Kaplan–Meier analysis showed that high KIF15 expression was significantly associated with poor prognosis (P = 0.004). Multivariate Cox regression confirmed that KIF15 was an independent prognostic factor. The nomogram model constructed based on KIF15 yielded a C-index of 0.783. Time-dependent ROCanalysis showed AUC values of 0.788, 0.777, and 0.712 for 1-, 3-, and 5-year survival, respectively. Calibrationcurves and DCA indicated good predictive consistency and clinical net benefit. Single-cell analysis revealedthat KIF15 was highly expressed in proliferating T cells (Tprolif). Functional enrichment analysis indicatedthat KIF15 was primarily involved in the cell cycle, DNA replication, mitosis, and the p53 signaling pathway.Immune correlation analysis showed significant associations between KIF15 expression and multipleimmune-infiltrating cells as well as immune checkpoint genes. Four downstream hub genes (TTK, CDK1,CHEK1, and KIF2C) were further identified and were all significantly upregulated in CC. IHC results confirmedthat KIF15 was highly expressed in CC tissues (P < 0.001) and was significantly associated with poorprognosis. Pearson correlation analysis demonstrated that KIF15 expression was positively correlated withimmune markers, including PD-1 and PD-L1. CONCLUSION: KIF15 is significantly overexpressed in CC tissues and is strongly associated with unfavorable prognosis, suggesting its potential clinical value in the diagnosis and prognostic assessment of CC. Notably, KIF15 expression is closely correlated with immune cell infiltration and multiple immune checkpoint molecules, indicating a potential role in regulating the tumor immune microenvironment. Therefore, KIF15 may serve as a promising biomarker and potential therapeutic target for immunotherapy, as well as a candidate marker for diagnosis, prognosis evaluation, and individualized treatment in CC.
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Identification and quantification of immune infiltration landscape on therapy and prognosis in left- and right-sided colon cancer.
BACKGROUND: The left-sided and right-sided colon cancer (LCCs and RCCs, respectively) have unique molecular features and clinical heterogeneity. This study aimed to identify the characteristics of immune cell infiltration (ICI) subtypes for evaluating prognosis and therapeutic benefits. METHODS: The independent gene datasets, corresponding somatic mutation and clinical information were collected from The Cancer Genome Atlas and Gene Expression Omnibus. The ICI contents were evaluated by "ESTIMATE" and "CIBERSORT." We performed two computational algorithms to identify the ICI landscape related to prognosis and found the unique infiltration characteristics. Next, principal component analysis was conducted to construct ICI score based on three ICI patterns. We analyzed the correlation between ICI score and tumor mutation burden (TMB), and stratified patients into prognostic-related high- and low- ICI score groups (HSG and LSG, respectively). The role of ICI scores in the prediction of therapeutic benefits was investigated by "pRRophetic" and verified by Immunophenoscores (IPS) (TCIA database) and an independent immunotherapy cohort (IMvigor210). The key genes were preliminary screened by weighted gene co-expression network analysis based on ICI scores. And they were further identified at various levels, including single cell, protein and immunotherapy response. The predictive ability of ICI score for prognosis was also verified in IMvigor210 cohort. RESULTS: The ICI features with a better prognosis were marked by high plasma cells, dendritic cells and mast cells, low memory CD4+ T cells, M0 macrophages, M1 macrophages, as well as M2 macrophages. A high ICI score was characterized by an increased TMB and genomic instability related signaling pathways. The prognosis, sensitivities of targeted inhibitors and immunotherapy, IPS and expression of immune checkpoints were significantly different in HSG and LSG. The genes identified by ICI scores and various levels included CA2 and TSPAN1. CONCLUSION: The identification of ICI subtypes and ICI scores will help gain insights into the heterogeneity in LCC and RCC, and identify patients probably benefiting from treatments. ICI scores and the key genes could serve as an effective biomarker to predict prognosis and the sensitivity of immunotherapy.
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IL-1 receptor-associated kinase 3 (IRAK3) in lung adenocarcinoma predicts prognosis and immunotherapy resistance: involvement of multiple inflammation-related pathways.
BACKGROUND: Lung cancer is a globally prevailing malignancy, and the predominant histological subtype is lung adenocarcinoma (LUAD). IL-1 receptor-associated kinase 3 (IRAK3) has been identified in connection with innate immune and inflammatory response. The aim of this study is to investigate the impact of IRAK3 on prognosis and immunotherapy efficacy in LUAD, which remains incompletely elucidated. METHODS: Our study delved into multiple online databases to find out expression, methylation and prognostic potentials of IRAK3 in LUAD and other malignancies. We employed tissue microarrays to assess IRAK3 protein levels in our LUAD cohort [National Cancer Center (NCC), China] and explore prognostic values. The correlations between IRAK3 and immune infiltration based on The Cancer Genome Atlas (TCGA) data were analyzed by corresponding algorithms. The contribution of IRAK3 to immunotherapy response was explored through the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Both LinkedOmics database and gene set enrichment analysis (GSEA) were applied to investigate how IRAK3 influences the tumor immune microenvironment and regulates immunotherapy response. We applied single-cell RNA sequencing datasets for the investigation of IRAK3 expression across diverse immune cells. Moreover, we employed genomics of drug sensitivity in cancer (GDSC) databases to examine how IRKA3 expression correlates with different drug responses. RESULTS: Compared with normal tissues, various tumor tissues had lower IRAK3 expression which could be regulated by its high methylation level. Reduced IRAK3 protein level was observed to correlate with advanced tumor stages and unfavorable prognosis among patients with LUAD, especially individuals with lymph node metastasis. Gene set enrichment analysis (GSEA) and tumor infiltration analysis proved that IRAK3 provoked immune infiltration. Macrophages/monocytes, CD4+ T cells, CD8+ T cells and neutrophils correlated significantly with IRAK3 expression. With TIDE algorithm, IRAK3 was verified to be related to poor immune checkpoint blockade (ICB) response. IRAK3 demonstrated positive associations with T-cell dysfunction score and immune checkpoint markers. Conversely, it exhibited negative correlations with microsatellite instability (MSI) and tumor mutation burden (TMB). High IRAK3 expression exacerbated cytotoxic T lymphocyte (CTL) dysfunction and predicted immunotherapy resistance by involvement of multiple inflammation-related pathways including IL-6/JAK/STAT3 signaling, inflammatory response and interferon-gamma (IFN-γ) response pathways. Additionally, elevated IRAK3 expression was predicted to be related with better responses to chemotherapeutic and molecular targeted drugs. CONCLUSIONS: Our findings indicated that IRAK3 could function as an independent prognostic predictor and an immunotherapeutic indicator in LUAD through involvement of multiple inflammation-related pathways.
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LncRNA SNHG6 plays an oncogenic role in colorectal cancer and can be used as a prognostic biomarker for solid tumors.
Small nucleolar RNA host gene 6 (SNHG6) has been recognized as an oncogene in numerous cancers and overexpression of SNHG6 was found to promote colorectal cancer (CRC). Hence, we performed a meta-analysis to examine the clinical importance of the long noncoding RNA (lncRNA) SNHG6. Moreover, comprehensive identification of RNA-binding proteins-mass spectrometry (ChIRP-MS) was conducted to explore the carcinogenic mechanism of lncRNA SNHG6 in CRC. Fourteen studies conducted on 1,139 patients were included in this meta-analysis. We also constructed the protein-protein interactive (PPI) network in string based on the ChIRP-MS results and cytoscape was used to identify core modules in the PPI network, which were then analyzed using the bioinformatics websites, cancer single-cell state atlas (CancerSEA) and G:profilter. The clinical outcomes of the meta-analysis indicated that higher expression of SNHG6 was related with a poorer survival outcome (overall survival: hazard ratio (HR) = 1.92; 95% confidence interval [Cl]: 1.48, 2.49; p < .0001; disease-free survival: HR = 1.84; 95% Cl: 1.02, 3.34; p = .044), higher tumor stage (odds ratio [OR] = 3.35; 95% Cl: 2.57, 4.37; p < .0001), distant metastasis (OR = 1.83; 95% Cl: 1.11, 2.99; p = .017) and lymph node metastasis (OR = 1.33; 95% Cl: 0.93, 1.89; p = .119). The ChIRP-MS results showed that core Module 1 of the PPI was significant in ribosomes and core Module 2 was mainly related to spliceosomes and messenger RNA processing. In conclusion, a higher expression of SNHG6 was found to be associated with a poorer survival outcome, high tumor stage, and distant metastasis in various solid tumors. SNHG6 was also found to be able to affect the processes of transcription and translation to promote CRC.
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Excitatory neurons and astrocytes-specific dysregulation and aberrant interactions are vulnerable to FCDI as suggested by single-cell spatial transcriptomics.
BACKGROUND: Focal cortical dysplasia (FCD) is a common neurodevelopmental disorder characterised by cortical malformations and is a major cause of drug-resistant epilepsy. FCD type I (FCDI) presents with architectural abnormalities of the neocortex but without cytological abnormalities. Currently, FCDI remains a significant clinical challenge. METHODS: Epileptogenic cortical tissues from three FCDI patients and three relatively normal neocortical tissues as controls were analysed using single-nucleus RNA sequencing and spatial transcriptomic for multi-omics integration. RESULTS: This study constructed a single-cell spatial transcriptomic atlas of the epileptogenic cortex from FCDI patients. Excitatory neurons (ENs) and astrocytes (Ast) exhibited the most prominent alterations in FCDI. Hub genes associated with FCDI were identified in ENs, and a transcription factor (TF)‒hub gene regulatory network was constructed. Notably, CBLN2highEx-1 was identified as being potentially involved in processes related to neuronal hyperexcitability and cortical development in FCDI. Western blot and immunofluorescence assays validated the altered expression of selected key genes and TFs at the protein level. Additionally, Ast exhibited increased heterogeneity, impaired differentiation and a higher proportion of immature Ast in FCDI, with predicted TFs regulating this process. Further analysis revealed aberrant signalling pathways and ligand‒receptor interactions between ENs and Ast in FCDI, with spatial co-localisation patterns that may contribute to disease progression. CONCLUSIONS: This study highlights the specific dysregulation of ENs and Ast, along with aberrant cellular communication, which may play a critical role in the pathogenesis of FCDI. These findings provide novel insights into the molecular mechanisms underlying FCDI and offer potential therapeutic targets for precision treatment and drug development.
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The glycolytic characteristics of hepatocellular carcinoma and its interaction with the microenvironment: a comprehensive omics study.
BACKGROUND: Hepatocellular carcinoma (HCC) is a common malignant tumor characterized by a high recurrence rate and poor prognosis. This study aimed to identify glycolysis-related prognostic markers and immunological abnormalities in patients with HCC. METHODS: We collected samples from cancerous and adjacent non-cancerous tissues for transcriptomic, metabolomic, and 16 S rRNA sequencing analyses. Glycolysis-related prognostic markers were identified by integrating public data from The Cancer Genome Atlas, GSE14520, and GSE76427 datasets. Additionally, single-cell sequencing data (GSE202642) were used to analyze the significantly infiltrated cellular subpopulations in HCC and investigate the expression of prognostic markers across different cell types. Spatial transcriptomics and mass cytometry (CyTOF) data were used to examine the expression differences in immune cells across tumor, peritumoral, and control tissues. Key prognostic markers were validated using reverse transcription-quantitative polymerase chain reaction, western blotting, and immunohistochemistry. RESULTS: Differentially expressed genes (DEGs) between HCC and control tissues were primarily clustered in cell cycle and metabolic pathways, particularly in the glycolysis pathway. Metabolomic analysis identified 175 differentially expressed metabolites that were mainly enriched in digestive and amino acid metabolism pathways. 16 S rRNA analysis revealed a significant increase in the abundance of Aenigmarchaeota and a decrease in the abundance of Proteobacteria in HCC tissues. The former was positively associated with glycolysis, whereas the latter showed a negative association. Through public data integration, 17 glycolysis-related DEGs were identified and 101 predictive models were constructed using machine learning. The StepCox[both] + random survival forest model using AGL, G6PD, GOT2, and KIF20A exhibited the best diagnostic performance among the three datasets. Single-cell RNA sequencing indicated significant infiltration of CD8 + Tex, CD8 + T, CD8 + Trm, and epithelial cells in HCC tissues. AGL, G6PD, GOT2, and KIF20A were highly expressed in CD8 + Tex cells, CD8 + Trm cells, macrophages, and monocytes, respectively. Spatial transcriptomics and CyTOF analyses showed greater infiltration of CD8 + Tex and CD8 + Trm cells in tumor tissues than in controls. Molecular assays further confirmed that G6PD and KIF20A expression levels were significantly higher, whereas AGL and GOT2 expression levels were lower, in HCC tissues than in control tissues. CONCLUSION: Through integrative multi-omics analysis, we identified glycolysis-related prognostic markers with distinct expression profiles across immune cell subsets in HCC. Our findings identify potential biomarkers and therapeutic targets for the diagnosis and treatment of HCC.
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Identification of a prognostic gene signature in patients with cisplatin resistant squamous cell lung cancer.
BACKGROUND: In the absence of targeted mutations and immune checkpoints, platinum-based chemotherapy remains a gold standard agent in the treatment of patients with lung squamous cell carcinoma (LUSC). However, cisplatin resistance greatly limits its therapeutic efficacy and presents challenges in the treatment of lung cancer patients. Therefore, the potential clinical needs for this research focus on identifying novel molecular signatures to further elucidate the underlying mechanisms of cisplatin resistance in LUSC. A growing body of evidence indicates that alternative splicing (AS) events significantly influence the tumor progression and survival of patients with LUSC. However, there are few systematic analyses of AS reported in LUSC. This study aims to explore the role of messenger RNA (mRNA), microRNA (miRNA), and AS in predicting prognosis in patients with cisplatin-resistant LUSC and provide potential therapeutic targets and drugs. METHODS: Gene expression and miRNA expression, using RNA sequencing (RNA-seq), and SpliceSeq data were downloaded from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to construct predictive models. Kaplan-Meier survival analyses were used to evaluate patients' prognosis. Single-sample gene set enrichment analysis (ssGSEA) conducted via the R package "GSEAbase" was used to evaluate the immune-related characteristics. Immunohistochemistry was used to examine protein expression. The Connectivity Map (CMap) database was used to screen for potential drugs. The 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di-phenytetrazoliumromide (MTT) assay was used to determine and calculate the half-maximal inhibitory concentration (IC50) of the drugs, sulforaphane and parthenolide. RESULTS: In this study, bioinformatics were used to identify mRNAs, miRNAs, and AS events related to response to cisplatin and to establish an integrated prognostic signature for 70 patients with LUSC and cisplatin resistance. The prognostic signature served as an independent prognostic factor with high accuracy [hazard ratio (HR) =2.346, 95% confidence interval (CI): 1.568-3.510; P<0.001], yielding an area under the curve (AUC) of 0.825, 0.829, and 0.877 for 1-, 3-, and 5-year survival, respectively. It also demonstrated high predictive performance in this cohort of patients with LUSC, with an AUC of 0.734, 0.767, and 0.776 for 1-, 3-, and 5-year survival, respectively. This integrated signature was also found to be an independent indicator among conventional clinical features (HR =2.288, 95% CI: 1.547-3.383; P<0.001). In addition, we analyzed the correlation of the signature with immune infiltration and identified several small-molecule drugs that had the potential to improve the survival of patients with LUSC. CONCLUSIONS: This study provides a framework for the mRNA-, miRNA-, and AS-based evaluation of cisplatin response and several potential therapeutic drugs for targeting cisplatin resistance in LUSC. These findings may serve as a theoretical basis for the clinical alleviation of cisplatin resistance and thus help to improve treatment responses to chemotherapy in patients with LUSC.
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Upfront whole blood transcriptional patterns in patients receiving immune checkpoint inhibitors associate with clinical outcome.
Whole blood (WB) transcriptomics offers a minimal-invasive method to assess patients' immune system. This study aimed to identify transcriptional patterns in WB associated with clinical outcomes in patients treated with immune checkpoint inhibitors (ICIs). We performed RNA-sequencing on pre-treatment WB samples from 145 patients with advanced cancer. Additionally, we compiled a separate dataset of 14,085 WB transcriptomes from diverse health backgrounds from public repositories and applied consensus-independent component analysis (c-ICA) to identify transcriptional components (TCs). The biological processes represented by these TCs were elucidated using gene set enrichment analysis. The activity of the TCs was then quantified in the 145 WB profiles and analyzed for associations with tumor response, progression-free survival, and overall survival using univariate and multivariate analyses in a permutation framework. RNA-sequencing variant calling was performed, and the activity of the TCs was assessed in specific cell lineages using a single-cell immune cell atlas of the human hematopoietic system. c-ICA on 14,085 WB transcriptomes identified 1262 distinct TCs representing various cellular processes. Of these, 18 TCs were associated with ≥ 1 outcome parameter, with three specifically linked to tumor response. Top genes in these three TCs included CCHCR1, TCF19, LTA, DDX39B, and PPP1R18. RNA-sequencing variant calling and single-cell transcriptome projections revealed associations between these four TCs and germline variants. These findings support the potential of the identified WB-based transcriptional patterns to complement tumor characteristics in predictive and prognostic models for improved patient stratification.
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Chronic intermittent hypoxia elicits distinct transcriptomic responses among neurons and oligodendrocytes within the brainstem of mice.
Chronic intermittent hypoxia (CIH) is a prevalent condition characterized by recurrent episodes of oxygen deprivation, linked to respiratory and neurological disorders. Prolonged CIH is known to have adverse effects, including endothelial dysfunction, chronic inflammation, oxidative stress, and impaired neuronal function. These factors can contribute to serious comorbidities, including metabolic disorders and cardiovascular diseases. To investigate the molecular impact of CIH, we examined male C57BL/6J mice exposed to CIH for 21 days, comparing with normoxic controls. We used single-nucleus RNA sequencing to comprehensively examine the transcriptomic impact of CIH on key cell classes within the brainstem, specifically excitatory neurons, inhibitory neurons, and oligodendrocytes. These cell classes regulate essential physiological functions, including autonomic tone, cardiovascular control, and respiration. Through analysis of 10,995 nuclei isolated from pontine-medullary tissue, we identified seven major cell classes, further subdivided into 24 clusters. Our findings among these cell classes, revealed significant differential gene expression, underscoring their distinct responses to CIH. Notably, neurons exhibited transcriptional dysregulation of genes associated with synaptic transmission, and structural remodeling. In addition, we found dysregulated genes encoding ion channels and inflammatory response. Concurrently, oligodendrocytes exhibited dysregulated genes associated with oxidative phosphorylation and oxidative stress. Utilizing CellChat network analysis, we uncovered CIH-dependent altered patterns of diffusible intercellular signaling. These insights offer a comprehensive transcriptomic cellular atlas of the pons-medulla and provide a fundamental resource for the analysis of molecular adaptations triggered by CIH.NEW & NOTEWORTHY This study on chronic intermittent hypoxia (CIH) from pons-medulla provides initial insights into the molecular effects on excitatory neurons, inhibitory neurons, and oligodendrocytes, highlighting our unbiased approach, in comparison with earlier studies focusing on single target genes. Our findings reveal that CIH affects cell classes distinctly, and the dysregulated genes in distinct cell classes are associated with synaptic transmission, ion channels, inflammation, oxidative stress, and intercellular signaling, advancing our understanding of CIH-induced molecular responses.
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ZNF282 Promotes Colorectal Cancer Progression Possibly via E2F1 Activation.
Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, highlighting the urgent need for improved therapeutic strategies. In this study, we identified zinc finger protein 282 (ZNF282), located on chromosome 7q, as a novel gene that contributes to CRC progression through comprehensive expression profiling using data from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing combined with spatial transcriptomics in clinical CRC datasets. ZNF282 was overexpressed in tumor cells. High ZNF282 mRNA expression was associated with distant metastasis and served as an independent poor prognostic factor in CRC. In vitro and in vivo experiments using CRISPR/Cas9-mediated ZNF282 knockout CRC cell lines demonstrated significantly reduced tumor growth and cell proliferation, which were reversed by reexpression of ZNF282. Mechanistically, ZNF282 knockout impaired the G1/S cell cycle transition and downregulated E2F1 and its downstream targets, CCNE1 and CCND1. These findings were supported by single-cell RNA sequencing analysis, which showed ZNF282 enrichment in malignant epithelial cells linked to cell cycle pathways. Motif scanning further identified a putative ZNF282 binding site upstream of the E2F1 transcription start site, suggesting that ZNF282 may enhance E2F1 expression by facilitating the activity of an upstream regulatory region. In conclusion, ZNF282 is a novel gene that promotes CRC progression in part by enhancing cell cycle progression possibly via E2F1 activation. Its high expression is associated with poor prognosis, supporting its potential as a prognostic biomarker and therapeutic target in CRC.