CCNA2 as an Immunological Biomarker Encompassing Tumor Microenvironment and Therapeutic Response in Multiple Cancer Types

MOICS, a novel classier deciphering immune heterogeneity and aid precise management of clear cell renal cell carcinoma at multiomics level

Recent studies have indicated that the tumor immune microenvironment plays a pivotal role in the initiation and progression of clear cell renal cell carcinoma (ccRCC). However, the characteristics and heterogeneity of tumor immunity in ccRCC, particularly at the multiomics level, remain poorly understood. We analyzed immune multiomics datasets to perform a consensus cluster analysis and validate the clustering results across multiple internal and external ccRCC datasets; and identified two distinctive immune phenotypes of ccRCC, which we named multiomics immune-based cancer subtype 1 (MOICS1) and subtype 2 (MOICS2). The former, MOICS1, is characterized by an immune-hot phenotype with poor clinical outcomes, marked by significant proliferation of CD4+ and CD8+ T cells, fibroblasts, and high levels of immune inhibitory signatures; the latter, MOICS2, exhibits an immune-cold phenotype with favorable clinical characteristics, characterized by robust immune activity and high infiltration of endothelial cells and immune stimulatory signatures. Besides, a significant negative correlation between immune infiltration and angiogenesis were identified. We further explored the mechanisms underlying these differences, revealing that negatively regulated endopeptidase activity, activated cornification, and neutrophil degranulation may promote an immune-deficient phenotype, whereas enhanced monocyte recruitment could ameliorate this deficiency. Additionally, significant differences were observed in the genomic landscapes between the subtypes: MOICS1 exhibited mutations in TTN, BAP1, SETD2, MTOR, MUC16, CSMD3, and AKAP9, while MOICS2 was characterized by notable alterations in the TGF-β pathway. Overall, our work demonstrates that multi-immune omics remodeling analysis enhances the understanding of the immune heterogeneity in ccRCC and supports precise patient management.

Analysis of the progression of cervical cancer in a low-and-middle-income country: From pre-malignancy to invasive disease

To better understand cervical cancer progression, we analyzed RNA from 262 biopsies from women referred for colposcopy. We determined the HPV type and analyzed the expression of 51 genes. HPV31 was significantly more prevalent in precancer than stage 1 cancer and invasive cancer (p < 0.0001), and HPV16 increased in invasive disease (p < 0.0001). CCNE1, MELTF, and ULBP2 were significantly increased in HPV16-positive compared to HPV31 precancers, while NECTIN2 and HLA-E expression decreased. Markers of the innate immune system, DNA repair genes, and cell cycle genes are significantly increased during cancer progression (p = 0.0001). In contrast, the TP53 and RB1 tumor suppressor gene expression is significantly decreased in cancer cells. The T cell markers CD28 and FLT3LG expression decreased in cancer while FOXP3, IDO1, and ULBP2 expression increased. There is a significantly higher survival rate in individuals with increased expression of CD28 (p = 0.0005), FOXP3 (p = 0.0002), IDO1 (p = 0.038), FLT3LG (p = 0.026), APOBEC3B (p = 0.0011), and RUNX3 (p = 0.019), and a significantly lower survival rate in individuals with increased expression of ULBP2 (p = 0.035). These results will help us elucidate the molecular factors influencing the progression of cervical precancer to cancer. Understanding the risk of progression of specific HPV types and sublineages may aid in the triage of positive patients, and better knowledge of the immune response may aid in developing and applying immunotherapies.

Fibroblasts regulate the transcriptional signature of human papillomavirus-positive keratinocytes

Persistent human papillomavirus (HPV) infection is necessary but insufficient for viral oncogenesis. Additional contributing co-factors, such as immune evasion and viral integration have been implicated in HPV-induced cancer progression. It is widely accepted that HPV + keratinocytes require co-culture with fibroblasts to maintain viral DNA as episomes. How fibroblasts regulate viral episome maintenance is a critical knowledge gap. Here we present comprehensive RNA sequencing and proteomic analysis demonstrating that coculture with fibroblasts is supportive of the viral life cycle, and is confirmatory of previous observations. Novel observations suggest that errors in "cross-talk" between fibroblasts and infected keratinocytes may regulate HPV integration and drive oncogenic progression. Our co-culture models offer new insights into HPV-related transformation mechanisms.

Antihyperlipidemic drug rosuvastatin suppressed tumor progression and potentiated chemosensitivity by downregulating CCNA2 in lung adenocarcinoma

Rosuvastatin (RSV) is widely used to treat hyperlipidemia and hypercholesterolemia and is recommended for the primary and secondary prevention of cardiovascular diseases (CVD). In this study, we aimed to explore its action and mechanism in lung adenocarcinoma (LUAD) therapy. Lewis and CMT64 cell-based murine subcutaneous LUAD models were employed to explore the effects of RSV monotherapy combined with cisplatin and gemcitabine. Human lung fibroblasts and human LUAD cell lines were used to assess the effects of RSV on normal and LUAD cells. Bioinformatics and RNA interference were used to observe the contribution of cyclin A2 (CCNA2) knockdown to RSV inhibition and to improve chemosensitivity in LUAD. RSV significantly suppressed grafted tumor growth in a murine subcutaneous LUAD model and exhibited synergistic anti-tumor activity with cisplatin and gemcitabine. In vitro and in vivo experiments demonstrated that RSV impaired the proliferation and migration of cancer cells while showing little inhibition of normal lung cells. RNA interference and CCK8 detection preliminarily indicated that RSV inhibited tumor growth and enhanced the chemosensitivity to cisplatin and gemcitabine by downregulating CCNA2. RSV suppressed LUAD progression and enhanced chemosensitivity to cisplatin and gemcitabine by downregulating CCNA2, which should be prior consideration for the treatment of LUAD, especially for patients co-diagnosed with hyperlipidemia and hypercholesterolemia.

Klrb1 Loss Promotes Chronic Hepatic Inflammation and Metabolic Dysregulation

Background/Objectives: CD161, encoded by the KLRB1 gene, is an inhibitory receptor expresses on various immune cell and has gained attention in immune checkpoint research. In recent studies, KLRB1 has been found to be one of the potential markers of liver diseases such as cirrhosis. Therefore, it will be important to understand what process KLRB1 involved in the liver for the prevention of liver diseases. Methods: We compared KO mice with wild-type controls by routine blood analysis and RNA-seq, and additionally performed H&E staining and qPCR to validate the differentially expressed genes (DEGs). Results:KO mice had fewer lymphocytes compared to the wild-type mice. A transcriptomic analysis showed that Klrb1 loss causes the upregulation of immune-related genes and pathways like NOD-like receptor and p53 signaling, while causing the downregulation of lipid metabolism-related genes. A protein interaction analysis indicated a potential cancer risk under chronic inflammation. Histological examination with H&E staining reveals an inflammatory response around the central venous vessels in the liver tissue of the KO mice. Conclusions: We conclude that Klrb1 knockout disrupts the immune and metabolic functions in the liver, which may possibly lead to chronic inflammation and malignancy risks. These findings highlight the role of Klrb1 in hepatic health.

Single-cell and bulk RNA-sequencing reveal SPP1 and CXCL12 as cell-to-cell communication markers to predict prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) generally presents as an immunosuppressive microenvironment. The characteristics of cell-to-cell communication in the LUAD microenvironment has been unclear. In this study, the LUAD bulk RNA-seq data and single-cell RNA-seq data were retrieved from public dataset. Differential expression genes (DEGs) between LUAD tumor and adjacent non-tumor tissues were calculated by limma algorithm, and then detected by PPI, KEGG, and GO analysis. Cell-cell interactions were explored using the single-cell RNA-seq data. Finally, the first 15 CytoHubba genes were used to establish related pathways and these pathways were used to characterize the immune-related ligands and their receptors in LUAD. Our analyses showed that monocytes or macrophages interact with tissue stem cells and NK cells via SPP1 signaling pathway and tissue stem cells interact with T and B cells via CXCL signaling pathway in different states. Hub genes of SPP1 participated in SPP1 signaling pathway, which was negatively correlated with CD4+ T cell and CD8+ T cell. The expression of SPP1 in LUAD tumor tissues was negatively correlated with the prognosis. While CXCL12 participated in CXCL signaling pathway, which was positively correlated with CD4+ T cell and CD8+ T cell. The role of CXCL12 in LUAD tumor tissues exhibits an opposite effect to that of SPP1. This study reveals that tumor-associated monocytes or macrophages may affect tumor progression. Moreover, the SPP1 and CXCL12 may be the critic genes of cell-to-cell communication in LUAD, and targeting these pathways may provide a new molecular mechanism for the treatment of LUAD.

Fibroblasts Regulate the Transformation Potential of Human Papillomavirus-positive Keratinocytes

Persistent human papillomavirus (HPV) infection is necessary but insufficient for viral oncogenesis. Additional contributing co-factors, such as immune evasion and viral integration have been implicated in HPV-induced cancer progression. It is widely accepted that HPV+ keratinocytes require co-culture with fibroblasts to maintain viral episome expression, yet the exact mechanisms for this have yet to be elucidated. Here we present comprehensive RNA sequencing and proteomic analysis demonstrating that fibroblasts not only support the viral life cycle, but reduce HPV+ keratinocyte transformation. Our co-culture models offer novel insights into HPV-related transformation mechanisms.

Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications: A Network-Based Approach

Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official World Health Organization (WHO) classification of the central nervous system (CNS). These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on The Cancer Genome Atlas (TCGA) glioma RNA-sequencing data set updated according to the 2016 and 2021 WHO guidelines, we proposed a 2-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularized Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.

Transcriptomic Differences in Medullary Thyroid Carcinoma According to Grade

Medullary thyroid carcinoma (MTC) is a rare cancer derived from neuroendocrine C-cells of the thyroid. In contrast to other neuroendocrine tumors, a histological grading system was lacking until recently. A novel two-tier grading system based on the presence of high proliferation or necrosis is associated with prognosis. Transcriptomic analysis was conducted on 21 MTCs, including 9 high-grade tumors, with known mutational status, using the NanoString Tumor Signaling 360 Panel. This analysis, covering 760 genes, revealed upregulation of the genes EGLN3, EXO1, UBE2T, UBE2C, FOXM1, CENPA, DLL3, CCNA2, SOX2, KIF23, and CDCA5 in high-grade MTCs. Major pathways differentially expressed between high-grade and low-grade MTCs were DNA damage repair, p53 signaling, cell cycle, apoptosis, and Myc signaling. Validation through qRT-PCR in 30 MTCs demonstrated upregulation of ASCL1, DLL3, and SOX2 in high-grade MTCs, a gene signature akin to small-cell lung carcinoma, molecular subgroup A. Subsequently, DLL3 expression was validated by immunohistochemistry. MTCs with DLL3 overexpression (defined as ≥ 50% of positive tumor cells) were associated with significantly lower disease-free survival (p = 0.041) and overall survival (p = 0.01). Moreover, MTCs with desmoplasia had a significantly increased expression of DLL3. Our data supports the idea that DLL3 should be further explored as a predictor of aggressive disease and poor outcomes in MTC.

Liquid-liquid phase separation-related features of PYGB/ACTR3/CCNA2/ITGB1/ATP8A1/RAP1GAP2 predict the prognosis of pancreatic cancer

Background

The growth and metastasis of pancreatic cancer (PC) has been found to be closely associated with liquid-liquid phase separation (LLPS). This study sought to identify LLPS-related biomarkers in PC to construct a robust prognostic model.

Methods

Transcriptomic data and clinical information related to PC were retrieved from publicly accessible databases. The PC-related data set was subjected to differential expression, Mendelian randomization (MR), univariate Cox, and least absolute selection and shrinkage operator analyses to identify biomarkers. Using the biomarkers, we subsequently constructed a risk model, identified the independent prognostic factors of PC, established a nomogram, and conducted an immune analysis.

Results

The study identified four genes linked with an increased risk of PC; that is, PYGB, ACTR3, CCNA2, and ITGB1. Conversely, ATP8A1, and RAP1GAP2 were found to provide protection against PC. These findings contributed significantly to the development of a highly precise risk model in which risk, age, and pathology N stage were categorized as independent factors in predicting the prognosis of PC patients. Using these factors, a nomogram was established to predict survival outcomes accurately. An immune analysis revealed varying levels of eosinophils, gamma delta T cells, and other immune cells between the distinct risk groups. The high-risk patients exhibited increased potential for immune escape, while the low-risk patients showed a higher response to immunotherapy.

Conclusions

Six genes were identified as having potential causal relationships with PC. These genes were integrated into a prognostic risk model, thereby serving as unique prognostic signatures. Our findings provide novel insights into predicting the prognosis of PC patients.

Deciphering the miRNA-mRNA Interaction Landscape between Breast Cancer and Triple-Negative Breast Cancer: An Integrated Bioinformatics Approach

Breast cancer (BC) is globally recognized as the second most prevalent form of cancer. It predominantly affects women and can be categorized into distinct types based on the overexpression of specific cancer receptors.The key receptors implicated in this context are the human epidermal growth factor receptor-2 (HER2), estrogen receptor (ER), and progesterone receptor (PR), alongside a particularly intricate subclass known as triple-negative breast cancer (TNBC). This subclassification is critical for the stratification of breast cancer and informs therapeutic decision-making processes. Due to a lack of therapeutic targets, such as growth factor receptors, TNBC is the most aggressive type. Hence, identifying targetable regulators such as miRNAs could pave the way for potential therapeutic interventions. To identify common differentially expressed mRNAs (DE-mRNAs) in BC, including TNBC, we leveraged two data sets from the GEO collection and The Cancer Genome Atlas (TCGA). Significant DE-mRNAs were identified through PPI, MCODE, CytoNCA, and CytoHubba analyses. Following this, miRNAs were predicted using mirDIP. We utilized GSE42568, GSE185645, and TCGA and identified 159 common DE-mRNAs. Using Cytoscape plug-ins, we identified the 10 most significant DE-mRNAs in BC. Using mirDIP, target miRNAs for 10 DE-mRNAs were identified. We conducted an advanced analysis on the TNBC GEO data set (GSE45498) to corroborate the significance of shared DE-mRNAs and DE-miRNAs in TNBC. We identified four downregulated DE-miRNAs, including hsa-miR-802, hsa-miR-1258, hsa-miR-548a-3p, and hsa-miR-2053, significantly associated with TNBC. Our study revealed significant miRNA-mRNA interactions, specifically hsa-miR-802/MELK, hsa-miR-1258/NCAPG, miR-548a-3p/CCNA2, and hsa-miR-2053/NUSAP1, in both BC and TNBC. The observed downregulation of hsa-miR-548a-3p is associated with diminished survival rates in BC patients, emphasizing their potential utility as prognostic indicators. Furthermore, the differential expression of mRNAs, including CCNB2, UBE2C, MELK, and KIF2C, correlates with reduced survival outcomes, signifying their critical role as potential targets for therapeutic intervention in both BC and TNBC. These findings highlight specific regulatory mechanisms that are potentially crucial for understanding and treating these cancer types.

A new perspective: deciphering the aberrance and clinical implication of disulfidptosis signatures in clear cell renal cell carcinoma

Recent research has discovered disulfidptosis as a form of programmed cell death characterized by disulfide stress. However, its significance in clear cell renal cell carcinoma (ccRCC) remains unclear. To investigate this, data from The Cancer Genome Atlas were collected and used to identify ccRCC subgroups. Unsupervised clustering was employed to determine ccRCC heterogeneity. The mutation landscape and immune microenvironment of the subgroups were analyzed. The Disulfidptosis-Related Score was calculated using the LASSO-penalized Cox regression algorithm. The E-MATB-1980 cohort was used to validate the signature. The role of SLC7A11 in ccRCC metastasis was explored using western blotting and Transwell assays. Disulfidptosis-related genes are commonly downregulated in cancers and are linked to hypermethylation and copy number variation. The study revealed that ccRCC is divided into two sub-clusters: the disulfidptosis-desert sub-cluster, which is associated with a poor prognosis, a higher mutation frequency, and an immunosuppressive microenvironment. A 14-gene prognostic model was developed using differentially expressed genes and was validated in the E-MATB-1980 cohort. The low-risk group demonstrated longer overall and disease-free survival and responded better to targeted immunotherapy. Results from in vitro experiments identified SLC7A11 as a key participant in ccRCC metastasis.

Identification of hub genes and pathways in hepatitis B virus-associated hepatocellular carcinoma: A comprehensive in silico study

Background and Aim

The hepatitis B virus (HBV) is one of the most common causes of liver cancer in the world. This study aims to provide a better understanding of the mechanisms involved in the development and progression of HBV-associated hepatocellular carcinoma (HCC) by identifying hub genes and the pathways related to their functions.

Methods

GSE83148 and GSE94660 were selected from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) with an adjusted p-value < 0.05 and a |logFC| ≥1 were identified. Common DEGs of two data sets were identified using the GEO2R tool. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) databases were used to identify pathways. Protein-protein interactions (PPIs) analysis was performed by using the Cytoscap and Gephi. A Gene Expression Profiling Interactive Analysis (GEPIA) analysis was carried out to confirm the target genes.

Results

One hundred and ninety-eight common DEGs and 49 hub genes have been identified through the use of GEO and PPI, respectively. The GO and KEGG pathways analysis showed DEGs were enriched in the G1/S transition of cell cycle mitotic, cell cycle, spindle, and extracellular matrix structural constituent. The expression of four genes (TOP2A, CDK1, CCNA2, and CCNB2) with high scores in module 1 were more in tumor samples and have been identified by GEPIA analysis.

Conclusion

In this study, the hub genes and their related pathways involved in the development of HBV-associated HCC were identified. These genes, as potential diagnostic biomarkers, may provide a potent opportunity to detect HBV-associated HCC at the earliest stages, resulting in a more effective treatment.

CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle

Glioblastoma (GBM) is characterized by significant heterogeneity, leading to poor survival outcomes for patients, despite the implementation of comprehensive treatment strategies. The roles of cyclin A2 (CCNA2) and NIMA related kinase 2 (NEK2) have been extensively studied in numerous cancers, but their specific functions in GBM remain to be elucidated. The present study aimed to investigate the potential molecular mechanisms of CCNA2 and NEK2 in GBM. CCNA2 and NEK2 expression and prognosis in glioma were evaluated by bioinformatics methods. In addition, the distribution of CCNA2 and NEK2 expression in GBM subsets was determined using pseudo-time analysis and tricycle position of single-cell sequencing. Gene Expression Omnibus and Kyoto Encyclopedia of Genes and Genome databases were employed and enrichment analyses were conducted to investigate potential signaling pathways in GBM subsets and a nomogram was established to predict 1-, 2- and 3-year overall survival probability in GBM. CCNA2 and NEK2 expression levels were further validated by western blot analysis and immunohistochemical staining in GBM samples. High expression of CCNA2 and NEK2 in glioma indicates poor clinical outcomes. Single-cell sequencing of GBM revealed that these genes were upregulated in a subset of positive neural progenitor cells (P-NPCs), which showed significant proliferation and progression properties and may activate G2M checkpoint pathways. A comprehensive nomogram predicts 1-, 2- and 3-year overall survival probability in GBM by considering P-NPCs, age, chemotherapy and radiotherapy scores. CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle, thus indicating the potential of novel therapy directed to CCNA2 and NEK2 in GBM.

CDK1 and CCNA2 play important roles in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a malignant tumor that occurs in oral cavity and is dominated by squamous cells. The relationship between CDK1, CCNA2, and OSCC is still unclear. The OSCC datasets GSE74530 and GSE85195 configuration files were downloaded from the Gene Expression Omnibus (GEO) database and were derived from platforms GPL570 and GPL6480. Differentially expressed genes (DEGs) were screened. The weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, Comparative Toxicogenomics Database analysis were performed. Gene expression heatmap was drawn. TargetScan was used to screen miRNAs that regulate central DEGs. A total of 1756 DEGs were identified. According to Gene Ontology (GO) analysis, they were predominantly enriched in processes related to organic acid catabolic metabolism, centromeric, and chromosomal region condensation, and oxidoreductase activity. In Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the DEGs were mainly concentrated in metabolic pathways, P53 signaling pathway, and PPAR signaling pathway. Weighted gene co-expression network analysis was performed with a soft-thresholding power set at 9, leading to the identification of 6 core genes (BUB1B, CCNB1, KIF20A, CCNA2, CDCA8, CDK1). The gene expression heatmap revealed that core genes (CDK1, CCNA2) were highly expressed in OSCC samples. Comparative Toxicogenomics Database analysis demonstrated associations between the 6 genes (BUB1B, CCNB1, KIF20A, CCNA2, CDCA8, CDK1) and oral tumors, precancerous lesions, inflammation, immune system disorders, and tongue tumors. The associated miRNAs for CDK1 gene were hsa-miR-203a-3p.2, while for CCNA2 gene, they were hsa-miR-6766-3p, hsa-miR-4782-3p, and hsa-miR-219a-5p. CDK1 and CCNA2 are highly expressed in OSCC. The higher the expression of CDK1 and CCNA2, the worse the prognosis.

Identifying molecular subtypes and tumor microenvironment infiltration signatures in kidney renal clear cell carcinoma based on stemness-associated disulfidptosis genes by integrating machine learning, single-cell analyses and experimental validation

Clear cell renal cell carcinoma (ccRCC) is an aggressive malignant tumor. Disulfidptosis is a new programmed cell death mechanism, which is characterized by the abnormal accumulation of intracellular disulfides that are highly toxic to cells. However, the contribution of disulfidptosis to ccRCC progression has not been fully clarified. In this study, two different molecular subtypes related to disulfidptosis were identified in ccRCC patients by the non-negative matrix factorization (NMF) algorithm. The cluster 1 was characterized by a worse prognosis and higher mRNAsi levels. Then, difference analysis and weighted gene co-expression network analysis (WGCNA) were conducted to search modular genes that are highly associated with tumor stemness and tumor microenvironment. Subsequently, a SADG signature containing nine genes was constructed stepwise by WGCNA and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The high-risk score group had a worse outcome, and immune regulation and metabolic signatures might be responsible for cancer progression in the high-risk group. After that, a predictive nomogram was constructed, and the predicting power of the risk model was verified using inter and three independent external validation datasets. Nine SADGs were shown to significantly correlate with immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) and immune checkpoint. In addition, based on the single-cell RNA sequencing dataset (GSE139555), the distribution and expression of nine hub genes in various types of immune cells were analyzed. Finally, the expression level of the nine genes was verified in clinical samples by qRT-PCR.

Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses

T-cell acute lymphoblastic leukemia (T-ALL)/T-cell lymphoblastic lymphoma (T-LBL) is an uncommon but highly aggressive hematological malignancy. It has high recurrence and mortality rates and is challenging to treat. This study conducted bioinformatics analyses, compared genetic expression profiles of healthy controls with patients having T-ALL/T-LBL, and verified the results through serological indicators. Data were acquired from the GSE48558 dataset from Gene Expression Omnibus (GEO). T-ALL patients and normal T cells-related differentially expressed genes (DEGs) were investigated using the online analysis tool GEO2R in GEO, identifying 78 upregulated and 130 downregulated genes. Gene Ontology (GO) and protein-protein interaction (PPI) network analyses of the top 10 DEGs showed enrichment in pathways linked to abnormal mitotic cell cycles, chromosomal instability, dysfunction of inflammatory mediators, and functional defects in T-cells, natural killer (NK) cells, and immune checkpoints. The DEGs were then validated by examining blood indices in samples obtained from patients, comparing the T-ALL/T-LBL group with the control group. Significant differences were observed in the levels of various blood components between T-ALL and T-LBL patients. These components include neutrophils, lymphocyte percentage, hemoglobin (HGB), total protein, globulin, erythropoietin (EPO) levels, thrombin time (TT), D-dimer (DD), and C-reactive protein (CRP). Additionally, there were significant differences in peripheral blood leukocyte count, absolute lymphocyte count, creatinine, cholesterol, low-density lipoprotein, folate, and thrombin times. The genes and pathways associated with T-LBL/T-ALL were identified, and peripheral blood HGB, EPO, TT, DD, and CRP were key molecular markers. This will assist the diagnosis of T-ALL/T-LBL, with applications for differential diagnosis, treatment, and prognosis.

Identification of key genes and pathways in adrenocortical carcinoma: evidence from bioinformatic analysis

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with poor prognosis. The disease originates from the cortex of adrenal gland and lacks effective treatment. Efforts have been made to elucidate the pathogenesis of ACC, but the molecular mechanisms remain elusive. To identify key genes and pathways in ACC, the expression profiles of GSE12368, GSE90713 and GSE143383 were downloaded from the Gene Expression Omnibus (GEO) database. After screening differentially expressed genes (DEGs) in each microarray dataset on the basis of cut-off, we identified 206 DEGs, consisting of 72 up-regulated and 134 down-regulated genes in three datasets. Function enrichment analyses of DEGs were performed by DAVID online database and the results revealed that the DEGs were mainly enriched in cell cycle, cell cycle process, mitotic cell cycle, response to oxygen-containing compound, progesterone-mediated oocyte maturation, p53 signaling pathway. The STRING database was used to construct the protein-protein interaction (PPI) network, and modules analysis was performed using Cytoscape. Finally, we filtered out eight hub genes, including CDK1, CCNA2, CCNB1, TOP2A, MAD2L1, BIRC5, BUB1 and AURKA. Biological process analysis showed that these hub genes were significantly enriched in nuclear division, mitosis, M phase of mitotic cell cycle and cell cycle process. Violin plot, Kaplan-Meier curve and stage plot of these hub genes confirmed the reliability of the results. In conclusion, the results in this study provided reliable key genes and pathways for ACC, which will be useful for ACC mechanisms, diagnosis and candidate targeted treatment.

Gene dysregulation in acute HIV-1 infection – early transcriptomic analysis reveals the crucial biological functions affected

Introduction

Transcriptomic analyses from early human immunodeficiency virus (HIV) infection have the potential to reveal how HIV causes widespread and lasting damage to biological functions, especially in the immune system. Previous studies have been limited by difficulties in obtaining early specimens.

Methods

A hospital symptom-based screening approach was applied in a rural Mozambican setting to enrol patients with suspected acute HIV infection (Fiebig stage I-IV). Blood samples were collected from all those recruited, so that acute cases and contemporaneously recruited, uninfected controls were included. PBMC were isolated and sequenced using RNA-seq. Sample cellular composition was estimated from gene expression data. Differential gene expression analysis was completed, and correlations were determined between viral load and differential gene expression. Biological implications were examined using Cytoscape, gene set enrichment analysis, and enrichment mapping.

Results

Twenty-nine HIV infected subjects one month from presentation and 46 uninfected controls were included in this study. Subjects with acute HIV infection demonstrated profound gene dysregulation, with 6131 (almost 13% of the genome mapped in this study) significantly differentially expressed. Viral load was correlated with 1.6% of dysregulated genes, in particular, highly upregulated genes involved in key cell cycle functions, were correlated with viremia. The most profoundly upregulated biological functions related to cell cycle regulation, in particular, CDCA7 may drive aberrant cell division, promoted by overexpressed E2F family proteins. Also upregulated were DNA repair and replication, microtubule and spindle organization, and immune activation and response. The interferome of acute HIV was characterized by broad activation of interferon-stimulated genes with antiviral functions, most notably IFI27 and OTOF. BCL2 downregulation alongside upregulation of several apoptotic trigger genes and downstream effectors may contribute to cycle arrest and apoptosis. Transmembrane protein 155 (TMEM155) was consistently highly overexpressed during acute infection, with roles hitherto unknown.

Discussion

Our study contributes to a better understanding of the mechanisms of early HIV-induced immune damage. These findings have the potential to lead to new earlier interventions that improve outcomes.

Copper Death Inducer, FDX1, as a Prognostic Biomarker Reshaping Tumor Immunity in Clear Cell Renal Cell Carcinoma

BACKGROUND

Progress in the diagnosis and treatment of clear cell renal cell carcinoma (ccRCC) has significantly prolonged patient survival. However, ccRCC displays an extreme heterogenous characteristic and metastatic tendency, which limit the benefit of targeted or immune therapy. Thus, identifying novel biomarkers and therapeutic targets for ccRCC is of great importance.

METHOD

Pan cancer datasets, including the expression profile, DNA methylation, copy number variation, and single nucleic variation, were introduced to decode the aberrance of copper death regulators (CDRs). Then, FDX1 was systematically analyzed in ccRCC to evaluate its impact on clinical characteristics, prognosis, biological function, immune infiltration, and therapy response. Finally, in vivo experiments were utilized to decipher FDX1 in ccRCC malignancy and its role in tumor immunity.

RESULT

Copper death regulators were identified at the pancancer level, especially in ccRCC. FDX1 played a protective role in ccRCC, and its expression level was significantly decreased in tumor tissues, which might be regulated via CNV events. At the molecular mechanism level, FDX1 positively regulated fatty acid metabolism and oxidative phosphorylation. In addition, FDX1 overexpression restrained ccRCC cell line malignancy and enhanced tumor immunity by increasing the secretion levels of IL2 and TNFγ.

CONCLUSIONS

Our research illustrated the role of FDX1 in ccRCC patients' clinical outcomes and its impact on tumor immunity, which could be treated as a promising target for ccRCC patients.

Upregulation of CENPM is associated with poor clinical outcome and suppression of immune profile in clear cell renal cell carcinoma

BACKGROUND

The response of advanced clear cell renal cell carcinoma (ccRCC) to immunotherapy is still not durable, suggesting that the immune landscape of ccRCC still needs to be refined, especially as some molecules that have synergistic effects with immune checkpoint genes need to be explored.

METHODS

The expression levels of CENPM and its relationship with clinicopathological features were explored using the ccRCC dataset from TCGA and GEO databases. Quantitative polymerase chain reaction (qPCR) analysis was performed to validate the expression of CENPM in renal cancer cell lines. Kaplan-Meier analysis, COX regression analysis and Nomogram construction were used to systematically evaluate the prognostic potential of CENPM in ccRCC. Besides, single gene correlation analysis, protein-protein interaction (PPI) network, genetic ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to predict the biological behaviour of CENPM and the possible signalling pathways involved. Finally, a comprehensive analysis of the crosstalk between CENPM and immune features in the tumor microenvironment was performed based on the ssGSEA algorithm, the tumor immune dysfunction and exclusion (TIDE) algorithm, the TIMER2.0 database and the TISIDB database.

RESULTS

CENPM was significantly upregulated in ccRCC tissues and renal cancer cell lines and was closely associated with poor clinicopathological features and prognosis. Pathway enrichment analysis revealed that CENPM may be involved in the regulation of the cell cycle in ccRCC and may have some crosstalk with the immune microenvironment in tumors. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. Furthermore, the TISIDB database provides evidence that not only CENPM is positively associated with immune checkpoint genes such as CTLA4, PDCD1, LAG3, TIGIT, but also chemokines and receptors (such as CCL5, CXCL13, CXCR3, CXCR5) may be responsible for the malignant phenotype of CENPM in ccRCC. Meanwhile, predictions based on the TIDE algorithm support that patients with high CENPM expression have a worse response to immunotherapy.

CONCLUSIONS

The upregulation of CENPM in ccRCC predicts a poor clinical outcome, and this malignant phenotype may be associated with its exacerbation of the immunosuppressive state in the tumor microenvironment.

A new thinking: deciphering the aberrance and clinical implication of copper-death signatures in clear cell renal cell carcinoma

RATIONALE

Recent research has indicated that cuprotosis, or copper induced cell death, is a novel type of cell death that could be utilized as a new weapon for cancer management. However, the characteristics and implications of such signatures in cancers, especially in clear cell renal cell cancer (ccRCC), remain elusive.

METHODS

Expression, methylation, mutation, clinical information, copy number variation, functional implication, and drug sensitivity data at the pan-cancer level were collected from The Cancer Genome Atlas. An unsupervised clustering algorithm was applied to decipher ccRCC heterogeneity. Immune microenvironment construction, immune therapy response, metabolic pattern, and cancer progression signature between subgroups were also investigated.

RESULTS

Cuprotosis related genes were specifically downregulated in various cancer tissues compared with normal tissues and were correlated with hypermethylation and copy number variation. Cuprotosis scores were also dysregulated in tumor tissues, and we found that such a signature could positively regulate oxidative phosphorylation and Myc and negatively regulate epithelial mesenchymal translation and myogenesis pathways. CPCS1 (cuprotosis scores high) and CPCS2 (cuprotosis scores low) in ccRCC displayed distinctive clinical profiles and biological characteristics; the CPCS2 subtype had a higher clinical stage and a worse prognosis and might positively regulate cornification and epidermal cell differentiation to fuel cancer progression. CPCS2 also displayed a higher tumor mutation burden and low tumor stemness index, while it led to a low ICI therapy response and dysfunctional tumor immunity state. The genome-copy numbers of CPCS2, including arm- gain and arm- loss, were higher than those of CPCS1. The prognostic model constructed based on subgroup biomarkers exerted satisfactory performance in both the training and validation cohorts. In addition, overexpression of the copper death activator DLAT suppressed the malignant ability, including cell migration and proliferation, of renal cell lines in vitro and in vivo. Finally, activation of cuprotosis in tumors could enhance antitumor immunity through dsDNA-cGAS-STING signaling in ccRCC.

CONCLUSION

The activation of cuprotosis might function as a promising approach among multiple cancers. The cuprotosis related signatures could reshape tumor immunity in the ccRCC microenvironment via cGAS-STING signal, thus activating tumor antigen-presenting process. Upregulation of DLAT expression in ccRCC cell lines could reactivate the copper death pattern and be treated as a suitable target for ccRCC.

The potential mechanism of Neu5Gc inducing colorectal cancer based on network pharmacology and experimental validation

Colorectal cancer has high morbidity and mortality worldwide, especially in western countries; the incidence of colorectal cancer has been high, which is closely related to the high intake of red meat; and the N-glycolylneuraminic acid (Neu5Gc) is responsible for red meat-induced colorectal cancer. A large number of previous studies have suggested that exogenous Neu5Gc-activated inflammation induced the occurrence of colorectal cancer. However, it has not been known whether the Neu5Gc has a direct inducing effect on colorectal cancer. In this study, we found that Neu5Gc promoted the proliferation of colorectal cancer cells and normal intestinal epithelial cells, and further screened out 98 Neu5Gc targets related to the occurrence and development of colorectal cancer by network pharmacology. Subsequently, GO and KEGG enrichment analyses of these targets revealed that mainly enriched in the PI3K-Akt signaling pathway. Then, we selected SRC, HRAS, CDK2, CCNA2, and AKT2 as core targets based on the phenomena of the previous experiments and the available literature reports, and then we used AutoDock for molecular docking with Neu5Gc; the results found that these five genes could bind to Neu5Gc stably. In vitro experiments showed that the mRNA levels of SRC, HRAS, AKT2, CDK2, and CCNA2 were upregulated and the protein levels of HRAS, AKT2, and CCNA2 were enhanced in FHC and SW620 cells after Neu5Gc (100 ng/mL) treatment. In conclusion, this study revealed that Neu5Gc probably acted as a carcinogen that stimulates the expression of proto-oncogene HRAS and the PI3K-Akt pathway and accelerated cell cycle progression. These findings revealed a novel mechanism that Neu5Gc promoted the occurrence and development of colorectal cancer.

Exploring the role of Xingren on COVID ‐19 based on network pharmacology and molecular docking

Since the outbreak of novel Coronavirus Pneumonia 2019 (COVID‐19), the role of Almonds (Xingren) in the protection and treatment of COVID‐19 is not clear. Network pharmacology and molecular docking were used to explore the potential mechanism and potential key targets of Xingren on COVID‐19. A total of nine common targets between them were obtained, and these targets were involved in multiple related processes of GO and KEGG pathway enrichment analysis. Molecular docking showed that licochalcone B has the best binding energy (−9.33 kJ·mol⁻¹) to PTGS2. They are maybe the important ingredient and key potential target. Its possible mechanism is to intervene anxiety disorder in the process of disease development, such as regulation of blood pressure, reactive oxygen species metabolic process, leishmaniasis peroxisome, and IL‐17 signaling pathway.

Identification of a novel histone phosphorylation prognostic signature in hepatocellular carcinoma based on bulk and single-cell RNA sequencing

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of death in the world, characterized by high morbidity, poor prognosis and high mortality. Histone modifications regulate intracellular gene expression at the post-transcriptional level, and disturbances in the regulatory pattern of histone modifications at individual locus or across the genome can lead to tumorigenesis of HCC. In this study, we constructed a prognosis-related histone phosphorylation regulated (HPR) genes signature and elucidated whether HPR genes can predict overall survival in HCC patients.

METHODS

Differentially expressed genes were screened using TCGA, ICGC and GEO databases, and a new risk signature was constructed by univariate Cox regression and Lasso regression analysis. Predictive nomograms were established by multivariate Cox regression of risk scores and clinical parameters, calibration curve and decision curve analysis were used to evaluate the models. The ssGSEA methods were used to determine the effect of risk scores on the tumor immune microenvironment. Data for HCC single-cell RNA sequencing (scRNA-seq) have been downloaded from Gene Expression Omnibus (GEO) to understand the role of HPR genes in tumorigenesis.

RESULTS

Our analyses of nine HPR genes provided prognostic insights. Overall survival in the low-risk and high-risk groups was statistically higher, respectively (P<0.001). Cox regression analysis revealed that the risk score is a significant predictor of HCC outcomes (HR=2. 2.62, 95%CI: 1.248-5.514, P=0.011). In addition, a nomogram combining risk scores with TNM stages was constructed and tested from calibration curves and decision curves (AUC=0.780). MHC-class-I genes, iDCs, Macrophages, Tfh, Treg, Th2 were overexpressed in the high-risk group.

CONCLUSION

HPR genes risk score is closely related to the prognosis of HCC, tumor immune process and tumor cell progression.