BACKGROUND: Meningiomas are common primary brain tumours, with macrophages playing a crucial role in their development and progression. This study aims to identify module genes correlated with meningioma-associated macrophages and analyse their correlation with clinical features and immune infiltration. METHODS: We analysed single-cell RNA sequencing (scRNA-seq) data from two paired meningioma and normal meninges to identify meningioma-associated macrophages. High-dimensional weighted gene co-expression network analysis (hdWGCNA) was employed to identify module genes linked to these macrophages, followed by functional enrichment and pseudotime trajectory analyses. A machine learning-based model using the module genes was developed to predict tumour grades. Finally, meningiomas were classified into two molecular subtypes based on the module genes, followed by a comparison of clinical characteristics and immune cell infiltration. RESULTS: Meningiomas exhibited a significantly higher proportion of macrophages than normal meninges, including novel macrophage clusters referred to as meningioma-associated macrophages. The hdWGCNA analysis of macrophages within meningiomas unveiled 12 distinct modules, with the blue, black, and turquoise modules closely correlated with the meningioma-associated macrophages. Hub genes within these modules were enriched in immune regulation, cellular communication, and metabolism pathways. Machine learning analysis identified 13 module genes (RSBN1, TIPRL, ATIC, SPP1, MALSU1, CDK1, MGP, DDIT3, SUPT16H, NFKBIA, SRSF5, ATXN2L, and UBB) strongly correlated with meningioma grade and constructed a predictive model with high accuracy and robustness. Based on the module genes, meningiomas were classified into two subtypes with distinct clinical and tumour microenvironment characteristics. CONCLUSIONS: Our findings provide insights into the molecular characteristics underlying macrophage infiltration in meningiomas. The molecular signatures of macrophages demonstrate correlations with clinical features and immune cell infiltration in meningiomas.

The progression of prostate cancer (PCa) leads to poor prognosis. However, the molecular mechanism of PCa is still not completely clear. This study aimed to elucidate the important role of centromere protein A (CENPA) in PCa. Large numbers of bulk RNA sequencing (RNA-seq) data and in-house immunohistochemistry data were used in analysing the expression level of CENPA in PCa and metastatic PCa (MPCa). Single-cell RNA-seq data was used to explore the expression status of CENPA in different prostate subpopulations. Enrichment analysis was employed to detect the function of CENPA in PCa. Clinicopathological parameters analysis was utilised in analysing the clinical value of CENPA. The results showed that CENPA was upregulated in PCa (standardised mean difference [SMD] = 0.83, p = 0.001) and MPCa (SMD = 0.61, p = 0.029). CENPA was overexpressed in prostate cancer stem cells (CSCs) with androgen receptor (AR) negative compared to epithelial cells with AR positive. CENPA may influence the development of PCa through affecting cell cycle. Patients with nodal metastasis had higher expression level of CENPA. And patients with high CENPA expression had poor disease-free survival. Taken together, Overexpression of CENPA may influence the development of PCa by regulating cell cycle and promoting metastasis.

Dolichyl-diphosphooligosaccharide protein glycosyltransferase (DDOST) plays a pivotal role in the glycosylation of asparagine residues on nascent polypeptides. However, the biological role of DDOST in glioma remains unclear. The mRNA expression of DDOST in glioma was identified using TCGA, CGGA, GEO and Rembrandt datasets. Immunohistochemistry assay was conducted to examine the protein level of DDOST. Cox regression analysis, nomograms and calibration plots were used to evaluate the prognostic value of DDOST. The association between DDOST and immune cell infiltration was evaluated using CIBERSORT algorithm. Additionally, DNA methylation and ceRNA regulatory network of DDOST expression were investigated using the LinkedOmics and ENCORI databases. The authors found that DDOST was substantially expressed at the mRNA and protein levels. Functional enrichment analysis revealed close associations between DDOST and immune-related pathways, as well as immune cell infiltration. In addition, DDOST exhibited synergistic effects with tumour mutational burden (TMB) and other immune checkpoints. For expression regulation mechanisms, DDOST had low DNA methylation levels in high-grade gliomas and may be involved in multiple ceRNA networks in glioma. Thus, DDOST may serve as an unfavourable biomarker for gliomas. DNA methylation and ceRNA regulatory networks of DDOST expression were identified for the first time in this multi-omics study.

Chromosomal instability (CIN) is closely associated to the early detection of several clinical tumours. In this study, the authors first established a novel prognostic model of melanoma using the hub genes of CIN, based on the datasets of The cancer genome atlas-skin cutaneous melanoma (TCGA-SKCM) and GSE65904 cohorts. Based on the risk scores of our model, the disease-specific survival (DSS) prognosis was worse in the high-risk group. Combining risk score, stage, age, ulceration, and clark factors, a Nomogram was generated to predict 1, 3, 5-year survival rates, which indicated a good clinical validity. Our finding also showed a correlation between high/low risk and tumour infiltration levels of 'activated CD8 T cells' and 'effector memory CD8 T cells'. Moreover, the authors first performed a CIN-based tumour clustering analysis using TCGA-SKCM cases, and identified two melanoma clusters, which exhibit the distinct DSS prognosis and the tumour-infiltrating levels of CD8 T cells. Taken together, a promising CIN-related prognostic signature and clustering for melanoma cases were first established in our study.

Bladder cancer (BC) is a common cancer worldwide with a high prevalence. This study was conducted to elucidate the expression and clinical significance of Sorbin and SH3 domain-containing protein 1 (SORBS1) in BC as well as to explore its molecular mechanism in BC tumourigenesis. RNA-sequencing data, microarray, and Immunohistochemistry (IHC) were applied to elucidated the SORBS1 expression at multiple levels. After that, the relationship between tumour-immune infiltration and SORBS1 was also explored. Finally, SORBS1-related genes in BC were identified to perform functional enrichment analyses. The expression integration revealed that the comprehensive expression of SORBS1 at the mRNA level was -1.02 and that at the protein level was -3.73, based on 12 platforms, including 1221 BC and 187 non-BC samples. SORBS1 was negatively correlated with tumour purity (correlation = -0.342, p < 0.001) and positively correlated with macrophage (correlation = 0.358, p < 0.001). The results of enrichment analyses revealed that the most significant biological pathways of SORBS1-related genes were epithelial-mesenchymal transition. SORBS1 was significantly down-regulated in BC and may play a role as tumour suppressor. This study provides new directions and biomarkers for future BC diagnosis.

Immune system has been reported to play a key role in the development of ischaemic stroke (IS). Nevertheless, its exact immune-related mechanism has not yet been fully revealed. Gene expression data of IS and healthy control samples was downloaded from Gene Expression Omnibus database and differentially expressed genes (DEGs) was obtained. Immune-related genes (IRGs) data was downloaded from the ImmPort database. The molecular subtypes of IS were identified based on IRGs and weighted co-expression network analysis (WGCNA). 827 DEGs and 1142 IRGs were obtained in IS. Based on 1142 IRGs, 128 IS samples were clustered into two molecular subtypes: clusterA and clusterB. Based on the WGCNA, the authors found that the blue module had the highest correlation with IS. In the blue module, 90 genes were screened as candidate genes. The top 55 genes were selected as the central nodes according to gene degree in protein-protein interactions network of all genes in blue module. Through taking overlap, nine real hub genes were obtained that might distinguish between clusterA subtype and clusterB subtype of IS. The real hub genes (IL7R, ITK, SOD1, CD3D, LEF1, FBL, MAF, DNMT1, and SLAMF1) may be associated with molecular subtypes and immune regulation of IS.

BACKGROUND: Diabetic nephropathy (DN) is a complication of diabetes. This study aimed to identify potential diagnostic markers of DN and explore the significance of immune cell infiltration in this pathology. METHODS: The GSE30528, GSE96804, and GSE1009 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by merging the GSE30528 and GSE96804 datasets. Enrichment analyses of the DEGs were performed. A LASSO regression model, support vector machine recursive feature elimination analysis and random forest analysis methods were performed to identify candidate biomarkers. The CIBERSORT algorithm was utilised to compare immune infiltration between DN and normal controls. RESULTS: In total, 115 DEGs were obtained. The enrichment analysis showed that the DEGs were prominent in immune and inflammatory responses. The DEGs were closely related to kidney disease, urinary system disease, kidney cancer etc. CXCR2, DUSP1, and LPL were recognised as diagnostic markers of DN. The immune cell infiltration analysis indicated that DN patients contained a higher ratio of memory B cells, gamma delta T cells, M1 macrophages, M2 macrophages etc. cells than normal people. CONCLUSION: Immune cell infiltration is important for the occurrence of DN. CXCR2, DUSP1, and LPL may become novel diagnostic markers of DN.

Deep venous thrombosis is one of the most common peripheral vascular diseases that lead to major morbidity and mortality. The authors aimed to identify potential differentially expressed miRNAs and target mRNAs, which were helpful in understanding the potential molecule mechanism of deep venous thrombosis. The plasma samples of patients with deep venous thrombosis were obtained for the RNA sequencing. Differentially expressed miRNAs were identified, followed by miRNA-mRNA target analysis. Enrichment analysis was used to analyze the potential biological function of target mRNAs. GSE19151 and GSE173461 datasets were used for expression validation of mRNAs and miRNAs. 131 target mRNAs of 21 differentially expressed miRNAs were identified. Among which, 8 differentially expressed miRNAs including hsa-miR-150-5p, hsa-miR-326, hsa-miR-144-3p, hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-125a-5p, hsa-let-7e-5p and hsa-miR-381-3p and their target mRNAs (PRKCA, SP1, TP53, SLC27A4, PDE1B, EPHB3, IRS1, HIF1A, MTUS1 and ZNF652) were found associated with deep venous thrombosis for the first time. Interestingly, PDE1B and IRS1 had a potential diagnostic value for patients. Additionally, 3 important signaling pathways including p53, PI3K-Akt and MAPK were identified in the enrichment analysis of target mRNAs (TP53, PRKCA and IRS1). Identified circulating miRNAs and target mRNAs and related signaling pathways may be involved in the process of deep venous thrombosis.

Glioblastoma is a grade IV pernicious neoplasm occurring in the supratentorial region of brain. As its causes are largely unknown, it is essential to understand its dynamics at the molecular level. This necessitates the identification of better diagnostic and prognostic molecular candidates. Blood-based liquid biopsies are emerging as a novel tool for cancer biomarker discovery, guiding the treatment and improving its early detection based on their tumour origin. There exist previous studies focusing on the identification of tumour-based biomarkers for glioblastoma. However, these biomarkers inadequately represent the underlying pathological state and incompletely illustrate the tumour because of non-recursive nature of this approach to monitor the disease. Also, contrary to the tumour biopsies, liquid biopsies are non-invasive and can be performed at any interval during the disease span to surveil the disease. Therefore, in this study, a unique dataset of blood-based liquid biopsies obtained primarily from tumour-educated blood platelets (TEP) is utilised. This RNA-seq data from ArrayExpress is acquired comprising human cohort with 39 glioblastoma subjects and 43 healthy subjects. Canonical and machine learning approaches are applied for identification of the genomic biomarkers for glioblastoma and their crosstalks. In our study, 97 genes appeared enriched in 7 oncogenic pathways (RAF-MAPK, P53, PRC2-EZH2, YAP conserved, MEK-MAPK, ErbB2 and STK33 signalling pathways) using GSEA, out of which 17 have been identified participating actively in crosstalks. Using PCA, 42 genes are found enriched in 7 pathways (cytoplasmic ribosomal proteins, translation factors, electron transport chain, ribosome, Huntington's disease, primary immunodeficiency pathways, and interferon type I signalling pathway) harbouring tumour when altered, out of which 25 actively participate in crosstalks. All the 14 pathways foster well-known cancer hallmarks and the identified DEGs can serve as genomic biomarkers, not only for the diagnosis and prognosis of Glioblastoma but also in providing a molecular foothold for oncogenic decision making in order to fathom the disease dynamics. Moreover, SNP analysis for the identified DEGs is performed to investigate their roles in disease dynamics in an elaborated manner. These results suggest that TEPs are capable of providing disease insights just like tumour cells with an advantage of being extracted anytime during the course of disease in order to monitor it.

Nowadays, primary liver cancer is still a major threat to human health. Anoikis is a particular form of programed cell death that has an inhibitory effect on neoplasm metastasis. Although several prognostic models based on anoikis-related genes for Hepatocellular carcinoma (HCC) have been established, signatures associated with anoikis-related lncRNAs have not been identified. To fill this blank space, the authors built up a prognostic signature and appraised its value in guiding immunotherapy. Eleven prognostic anoikis-related lncRNAs were identified through Least Absolute Shrinkage and Selection Operator Cox analysis. The accuracy of the risk signature in predicting prognosis was verified by K-M survival analysis and Receiver operating characteristic analysis. We further discovered that the high-risk group was often enriched in signal pathways related to cell growth and death and immune response; in addition, in the low-risk group, cells often undergo metabolic changes through gene set enrichment analysis. Finally, we realised that HCC patients in the high-risk group were upregulated in immune-checkpoint molecules and tend to have a higher tumour mutation burden level which indicated a higher sensitivity to immunotherapy. All in all, the anoikis-related lncRNAs risk signature showed excellent ability in predicting prognosis and may guide the application of immunotherapy in future clinical practice.

Bladder cancer (BLCA) is a common and difficult-to-manage disease worldwide. Most common type of BLCA is urothelial carcinoma (UC). Fibrillin 2 (FBN2) was first discovered while studying Marfan syndrome, and its encoded products are associated with elastin fibres. To date, the role of FBN2 in BLCA remains unclear. The authors first downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The patients were divided into high FBN2 expression and low FBN2 expression groups, and the survival curve, clinical characteristics, tumour microenvironment (TME), and immune cell differences were analysed between the two groups. Then, the differentially expressed genes (DEGs) were filtered, and functional enrichment for DEGs was performed. Finally, chemotherapy drug susceptibility analysis based on the high and low FBN2 groups was conducted. The authors found upregulated expression of FBN2 in BLCA and proved that FBN2 could be an independent prognostic factor for BLCA. TME analysis showed that the expression of FBN2 affects several aspects of the TME. The upregulated expression of FBN2 was associated with a high stromal score, which may lead to immunosuppression and be detrimental to immunotherapy. In addition, the authors found that NK cells resting, macrophage M0 infiltration, and other phenomena of immune cell infiltration appeared in the high expression group of FBN2. The high expression of FBN2 was related to the high sensitivity of some chemotherapy drugs. The authors systematically investigated the effects and mechanisms of FBN2 on BLCA and provided a new understanding of the role of FBN2 as a risk factor and TME influencer in BLCA.

Hepatocellular carcinoma (HCC) remains a worldwide health problem. Mounting evidence indicates that exhausted T cells play a critical role in the progress and treatment of HCC. Therefore, a detailed characterisation of exhausted T cells and their clinical significance warrants further investigation in HCC. Based on the GSE146115, we presented a comprehensive single-cell Atlas in HCC. Pseudo-time analysis revealed that tumour heterogeneity progressively increased, and the exhausted T cells gradually appeared during tumour progression. Functional enrichment analysis revealed that the evolutionary process of exhausted T cells mainly contained the pathway of cadherin binding, proteasome, cell cycle, and T cell receptor regulation of apoptosis. In the International Cancer Genome Consortium database, we divided patients into three clusters with the T cell evolution-associated genes. We found that the exhausted T cells are significantly related to poor outcomes through immunity and survival analysis. In The Cancer Genome Atlas database, the authors enrolled weighted gene co-expression network analysis, univariate Cox analysis, and Lasso Cox analysis, then screened the 19 core genes in T cells evolution and built a robust prognostic model. This study offers a fresh view on evaluating the patients' outcomes from an exhausted T cells perspective and might help clinicians develop therapeutic systems.

Leucocyte immunoglobulin-like receptors (LILRs) are closely related to tumourigenesis, but their clinical value in early-stage pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy remains unknown. Kaplan-Meier and Cox proportional hazards regression models is used to investigate the association between LILR expression and prognosis in tumour biopsies and peripheral blood mononuclear cells. Risk score was calculated for each patient based on the prognostic model. DAVID, STRING, GeneMANIA, and GSEA were used to conduct pathway and functional analyses. The CIBERSORT algorithm is used to analyse tumour-infiltrating immune cells. Survival analysis showed that high levels of LILRA4 (p = 0.006) and LILRB4 (p = 0.04) were significantly associated with better overall survival. High levels of LILRA2 (p = 0.008) and LILRB4 (p = 0.038) were significantly associated with better relapse-free survival. JAK-STAT signalling pathway, regulation of T cell activation, regulation of the immune effector process, and tumour necrosis factor superfamily cytokine production were involved in molecular mechanisms that affected poor prognoses in the high-risk group in GSEA. CIBERSORT demonstrated that the high-risk group had significantly higher infiltrating fraction of memory-activated CD4 T cells and activated NK cells and lower fraction of resting dendritic cells and neutrophils. LILRB4 plays crucial roles in affecting the clinical outcomes of early-stage PDAC.

The traditional blood glucose estimation method requires to take the invasive measurements several times a day. Therefore, it has a high infection risk and the users are suffered from the pain. Moreover, the long term consumable cost is high. Recently, the wearable and non-invasive blood glucose estimation approach has been proposed. However, due to the unreliability of the acquisition device, the presence of the noise and the variations of the acquisition environments, the obtained features and the reference blood glucose values are highly unreliable. Moreover, different subjects have different responses of the infrared light to the blood glucose. To address this issue, a polynomial fitting approach to smooth the obtained features or the reference blood glucose values has been proposed. In particular, the design of the coefficients in the polynomial is formulated as the various optimisation problems. First, the blood glucose values are estimated based on the individual optimisation approaches. Second, the absolute difference values between the estimated blood glucose values and the actual blood glucose values based on each optimisation approach are computed. Third, these absolute difference values for each optimisation approach are sorted in the ascending order. Fourth, for each sorted blood glucose value, the optimisation method corresponding to the minimum absolute difference value is selected. Fifth, the accumulate probability of each selected optimisation method is computed. If the accumulate probability of any selected optimisation method at a point is greater than a threshold value, then the accumulate probabilities of these three selected optimisation methods at that point are reset to zero. A range of the sorted blood glucose values are defined as that with the corresponding boundaries points being the previous reset point and this reset point. Hence, after performing the above procedures for all the sorted reference blood glucose values in the validation set, the regions of the sorted reference blood glucose values and the corresponding optimisation methods in these regions are determined. It is worth noting that the conventional lowpass denoising method was performed in the signal domain (either in the time domain or in the frequency domain), while the authors' proposed method is performed in the feature space or the reference blood glucose space. Hence, the authors' proposed method can further improve the reliability of the obtained feature values or the reference blood glucose values so as to improve the accuracy of the blood glucose estimation. Moreover, the individual modelling regression method has been employed here to suppress the effects of different users having different responses of the infrared light to the blood glucose values. The computer numerical simulation results show that the authors' proposed method yields the mean absolute relative deviation (MARD) at 0.0930 and the percentage of the test data falling in the zone A of the Clarke error grid at 94.1176%.

The link between family with sequence similarity 99 member A (FAM99A), a type of long non-coding RNA, and tumourigenesis remains ambiguous. Therefore, in this study, the authors conducted an expression profile analysis of FAM99A based on 33 types of cancer within The Cancer Genome Atlas project. The expression profile data revealed low expression levels of FAM99A in hepatocellular carcinoma, cholangiocarcinoma, and testicular germ cell tumour tissues than in the normal control tissues. Survival analysis indicated a correlation between low FAM99A expression and worse survival outcome in patients with hepatic cancer. Further investigation revealed the possible implication of DNA methylation, but not copy number variation, in FAM99A-associated hepatic tumourigenesis. The authors also identified a set of differentially expressed genes between patients with hepatic cancer and negative controls, which were found to be related to biochemical metabolism or the cell cycle. Additionally, FAM99A expression may be associated with the infiltration status of several immune cells, such as dendritic cells, natural killer cells, and neutrophils. Overall, FAM99A may function as a prognostic marker that is potentially associated with DNA methylation, immune cell infiltration, and biochemical metabolism in hepatic cancer.

Several bioinformatics studies have been performed on high-throughput expression data to determine the cellular pathways and hub genes affected by Gastric cancer (GC). However, these studies differ in using a healthy tissue or normal tissue adjacent to the tumour (NAT) as calibrator tissues. This study was designed to find how using healthy or NAT tissues as calibrator tissues could affect pathway enrichment data and hub genes in GC. Two gene expression datasets with NAT tissues (GSE79973 and GSE118916) and one dataset with healthy tissues (GSE54129) were downloaded and processed by the limma package to screen the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were performed by the Enrichr online tool. Protein-protein interaction network construction, module analysis, and hub genes selection were performed by Cytoscape software, Molecular Complex Detection plugin, and cytoHubba plugin, respectively. The gene expression profiling interactive analysis web server was used to analyse RNA sequencing expression data from The Cancer Genome Atlas Program. The Kaplan-Meier plotter was used to perform survival analysis. Our results showed that some KEGG and GO pathways were shared between studies with NAT and the study with healthy tissues. However, some terms, especially inflammation-related terms, were missed when NAT tissues were used as calibrator tissues. Also, only FN1 and COL1A1 are common hub genes between DEGs of the studies with NAT and healthy tissues. Since hub genes are usually extracted and suggested as candidate targets for GC diagnosis, prognosis, or treatment, selecting healthy or NAT tissues may affect the hub genes selection.

Cuproptosis is a novel cell death pathway, and the regulatory mechanism in pancreatic cancer (PC) is unclear. The authors aimed to figure out whether cuproptosis-related lncRNAs (CRLs) could predict prognosis in PC and the underlying mechanism. First, the prognostic model based on seven CRLs screened by the least absolute shrinkage and selection operator Cox analysis was constructed. Following this, the risk score was calculated for pancreatic cancer patients and divided patients into high and low-risk groups. In our prognostic model, PC patients with higher risk scores had poorer outcomes. Based on several prognostic features, a predictive nomogram was established. Furthermore, the functional enrichment analysis of differentially expressed genes between risk groups was performed, indicating that endocrine and metabolic pathways were potential regulatory pathways between risk groups. TP53, KRAS, CDKN2A, and SMAD4 were dominant mutated genes in the high-risk group and tumour mutational burden was positively correlated with the risk score. Finally, the tumour immune landscape indicated patients in the high-risk group were more immunosuppressive than that in the low-risk group, with lower infiltration of CD8+ T cells and higher M2 macrophages. Above all, CRLs can be applied to predict PC prognosis, which is closely correlated with the tumour metabolism and immune microenvironment.

The pathogenesis of colon cancer, a common gastrointestinal tumour, involves complicated factors, especially a series of cell cycle-related genes. E2F transcription factors during the cell cycle play an essential role in the occurrence of colon cancer. It is meaningful to establish an efficient prognostic model of colon cancer targeting cellular E2F-associated genes. This has not been reported previously. The authors first aimed to explore the links of E2F genes with the clinical outcomes of colon cancer patients by integrating data from the TCGA-COAD (n = 521), GSE17536 (n = 177) and GSE39582 (n = 585) cohorts. The Cox regression and Lasso modelling approach to identify a novel colon cancer prognostic model involving several hub genes (CDKN2A, GSPT1, PNN, POLD3, PPP1R8, PTTG1 and RFC1) were utilised. Moreover, an E2F-related nomogram that efficiently predicted the survival rates of colon cancer patients was created. Additionally, the authors first identified two E2F tumour clusters, which showed distinct prognostic features. Interestingly, the potential links of E2F-based classification and 'protein secretion' issues of multiorgans and tumour infiltration of 'T-cell regulatory (Tregs)' and 'CD56dim natural killer cell' were detected. The authors' findings are of potential clinical significance for the prognosis assessment and mechanistic exploration of colon cancer.