• NSCLC
• feature selection
• machine learning
• microRNA
• prognosis
• qPCR

• RNPA2B1
• energy metabolism
• lung cancer
• oxidative phosphorylation
• particulate matter

• Animals
• Humans
• Mice
• A549 Cells
• Apoptosis
• Cell Line, Tumor
• Cell Movement
• Cell Proliferation
• Disease Progression
• Gene Expression Regulation, Neoplastic
• Heterogeneous-Nuclear Ribonucleoprotein Group A-B/metabolism
• Heterogeneous-Nuclear Ribonucleoprotein Group A-B/genetics
• Lung Neoplasms/pathology
• Lung Neoplasms/genetics
• Lung Neoplasms/metabolism
• Mice, Inbred BALB C
• Mice, Nude
• Oxidative Phosphorylation
• Particulate Matter/adverse effects
• RNA Stability
• RNA, Messenger/genetics
• RNA, Messenger/metabolism
• Xenograft Model Antitumor Assays

• EGFR‐mutated lung adenocarcinoma
• Gene Expression Omnibus
• degree topology, therapeutic molecule
• hub gene
• hypopharyngeal cancer

• Humans
• ErbB Receptors/genetics
• ErbB Receptors/metabolism
• Gene Regulatory Networks/genetics
• Adenocarcinoma of Lung/genetics
• Mutation
• Hypopharyngeal Neoplasms/genetics
• Computational Biology/methods
• Lung Neoplasms/genetics
• Gene Expression Regulation, Neoplastic/genetics
• Gene Expression Profiling

• RGP.2/219/43 Deanship of Scientific Research Large Groups at King Khalid University, Kingdom of Saudi Arabia

• AURKA
• Lung adenocarcinoma
• Machine learning
• Pathomics
• Prognosis

• FCGBP
• Immune checkpoint
• Immune microenvironment
• NSCLC
• Prognostic signature

• KYCX21-3108 Postgraduate Research & Practice Innovation Program of Jiangsu Province
• LGY2016037 "Six-one" Project for High-level Health Talents
• 82370253 Nantong Key Laboratory of Translational Medicine in Cardiothoracic Diseases, Nantong Clinical Medical Research Center of Cardiothoracic Disease, and Institution of Translational Medicine in Cardiothoracic Diseases in Affiliated Hospital of Nantong University. National Natural Science Foundation of China
• YJXYY202204 Jiangsu Provincial Research Hospital
• XNBHCX31773 Innovation Team Project of Affiliated Hospital of Nantong University
• Postgraduate Research & Practice Innovation Program of Jiangsu Province
• “Six-one” Project for High-level Health Talents

• DNA damage repair (DDR)
• DNA damage repair inhibitor (DDR inhibitor)
• non-small cell lung cancer (NSCLC)

• chromatin regulator
• immune cell infiltration
• lung adenocarcinoma
• risk model
• survival

• Humans
• Chromatin
• Prognosis
• Adenocarcinoma of Lung/genetics
• Computational Biology
• Lung Neoplasms/genetics
• Lung Neoplasms/therapy

• Differentially Expressed Genes
• Lung Cancer
• Protein-Protein Interaction Network

• apoptosis
• cancer progression
• cdc20
• cdc20-target inhibitors
• tumorigenesis

• Humans
• Neoplasms/drug therapy
• Cell Transformation, Neoplastic
• Carcinogenesis
• Cell Cycle Proteins
• Cell Cycle
• Tumor Microenvironment
• Cdc20 Proteins/genetics

• key genes
• lung adenocarcinoma
• protein-protein interaction network
• weighted gene co-expression network analysis

• Humans
• Aurora Kinase A
• Lung Neoplasms/genetics
• Adenocarcinoma of Lung
• Biomarkers
• Databases, Factual
• Gene Expression Profiling
• Gene Regulatory Networks
• Biomarkers, Tumor/genetics

• Differentially expressed genes
• Lung adenocarcinoma
• Nomogram
• Prognostic signature
• Risk score

There is a close relationship between radiotherapy and autophagy in tumors, but the prognostic role of radiotherapy-related autophagy genes (RRAGs) in lung adenocarcinoma (LUAD) remains unclear.

Data used in the current study were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted gene co-expression network analysis (WGCNA) was executed to recognize module genes associated with radiotherapy. The differentially expressed genes (DEGs) between different radiotherapy response groups were filtered via edgeR package. The differentially expressed radiotherapy-related autophagy genes (DERRAGs) were obtained by overlapping the module genes, DEGs, and autophagy genes (ATGs). Then, prognostic autophagy genes were selected by Cox analyses, and a risk model and nomogram were subsequently built. Gene Set Enrichment Analysis (GSEA) and single-sample Gene Set Enrichment Analysis (ssGSEA) were performed to investigate potential mechanisms through which prognostic autophagy signatures regulate LUAD. Radiotherapy-resistant cell lines (A549IR and PC9IR) were established after exposure to hypo-fractionated irradiation. Ultimately, mRNA expression was validated by quantitative real-time PCR (qRT-PCR), and relative protein levels were measured in different cell lines by western blot.

A total of 11 DERRAGs were identified in LUAD. After Cox analyses, SHC1, NAPSA, and AURKA were filtered as prognostic signatures in LUAD. Then, the risk score model was constructed using the prognostic signatures, which had a good performance in predicting the prognosis, as evidenced by receiver operating characteristics curves. Furthermore, Cox regression analyses demonstrated that risk score was deemed as an independent prognostic factor in LUAD. Moreover, GSEA and ssGSEA results revealed that prognostic RRAGs may regulate LUAD by modulating the immune microenvironment and affecting cell proliferation. The colony formation assay showed that the radiosensitivity of radiation-resistant cell lines was lower than that of primary cells. The western blot assay found that the levels of autophagy were elevated in the radiotherapy-resistant cell lines. Moreover, the expression of DERRAGs (SHC1, AURKA) was higher in the radiotherapy-resistant cells than in primary cells.

Our study explored the role of RRAGs in the prognosis of LUAD and identified three biomarkers. The findings enhanced the understanding of the relationship between radiotherapy, autophagy, and prognosis in LUAD and provided potential therapeutic targets for LUAD patients.

• Humans
• Aurora Kinase A/genetics
• Aurora Kinase A/metabolism
• Lung Neoplasms/genetics
• Lung Neoplasms/radiotherapy
• Lung Neoplasms/metabolism
• Gene Expression Regulation, Neoplastic
• Adenocarcinoma of Lung/genetics
• Adenocarcinoma of Lung/radiotherapy
• Prognosis
• Autophagy/genetics
• Tumor Microenvironment

Breast cancer (BRCA) ranks as a leading cause of cancer death in women worldwide. Glucose metabolism is a noticeable characteristic of the occurrence of malignant tumors. In this study, we aimed to construct a novel glycometabolism-related gene (GRG) signature to predict overall survival (OS), immune infiltration and therapeutic response in BRCA patients.

The mRNA sequencing and corresponding clinical data of BRCA patients were obtained from public cohorts. Lasso regression was applied to establish a GRG signature. The immune infiltration was evaluated with the ESTIMATE and CIBERSORT algorithms. The drug sensitivity was estimated using the value of IC50, and further forecasted the therapeutic response of each patient. The candidate target was selected in Cytoscape. A nomogram was constructed via the R package of “rms”.

We constructed a six-GRG signature based on CACNA1H, CHPF, IRS2, NT5E, SDC1 and ATP6AP1, and the high-risk patients were correlated with poorer OS (P = 2.515 × 10⁻⁷). M2 macrophage infiltration was considerably superior in high-risk patients, and CD8⁺ T cell infiltration was significantly higher in low-risk patients. Additionally, the high-risk group was more sensitive to Lapatinib. Fortunately, SDC1 was recognized as candidate target and patients had a better OS in the low-SDC1 group. A nomogram integrating the GRG signature was developed, and calibration curves were consistent between the actual and predicted OS.

We identified a novel GRG signature complementing the present understanding of the targeted therapy and immune biomarker in breast cancer. The GRGs may provide fresh insights for individualized management of BRCA patients.

• grant obtained from the Qilu health leader training project (Na Zhou) and the National Natural Science Foundation of China

• PRAD
• biomarker
• heterogeneity
• overall survival (OS)
• tissue microarray

• gene expression omnibus
• lung cancer
• nomogram
• prognosis
• risk score

Lung cancer is the leading cause of cancer-related mortality worldwide. Although cigarette smoking is an established risk factor for lung cancer, few reliable smoking-related biomarkers for non-small-cell lung cancer (NSCLC) are available. An improved understanding of these biomarkers would further the development of new biomarker-targeted therapies and lead to improvements in overall patient survival.

We performed bioinformatic analysis to screened potential target genes, then quantitative PCR, western, siRNA, CCK-8, flow cytometry, tumorigenicity assays in nude mice were performed to validated the function.

In this study, we identified 83 smoking-related genes (SRGs) based on an integration analysis of two Gene Expression Omnibus (GEO) datasets, and 27 hub SRGs with potential carcinogenic effects by analyzing a dataset of smokers with NSCLC in The Cancer Genome Atlas (TCGA) database. A survival analysis revealed three genes with potential prognostic value, namely SRXN1, KRT6A and JAKMIP3. A univariate Cox analysis revealed significant associations of elevated SRXN1 and KRT6A expression with prognosis. A receiver operating characteristic (ROC) curve analysis indicated the high diagnostic value of SRXN1 and KRT6A for smoking and cancer. Quantitative PCR and western blotting validated the increased expression of SRXN1 and KRT6A mRNA and protein, respectively, in lung cancer cell lines and NSCLC tissues. In patients with NSCLC, SRXN1 and KRT6A expression was associated with the tumor–node–metastasis (TNM) stage, presence of metastasis, history of smoking and daily smoking consumption. Furthermore, inhibition of SRXN1 or KRT6A suppressed viability and enhanced apoptosis in the A549 human lung carcinoma cell line. Tumorigenicity assays in nude mice confirmed that the siRNA-mediated downregulation of SRXN1 and KRT6A expression inhibited tumor growth in vivo.

In summary, SRXN1 and KRT6A act as oncogenes in NSCLC and might be potential biomarkers of smoking exposure and the early diagnosis and prognosis of NSCLC in smokers, which is vital for lung cancer therapy.

• KRT6A
• SRXN1
• lung cancer
• non-small-cell lung cancer
• smoking-related gene

• Biomarkers
• Drug targets
• Important genes
• Lung cancer
• Systems biology

Lung adenocarcinoma (LUAD) is a heterogeneous disease. However the inner sub‐groups of LUAD have not been fully studied. Markers predicted the sub‐groups and prognosis of LUAD are badly needed.

To identify biomarkers associated with the sub‐groups and prognosis of LUAD.

Using nonnegative matrix factorization (NMF) clustering, LUAD patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets and LUAD cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) dataset were divided into different sub‐consensuses based on the gene expression profiling. The overall survival of LUAD patients in each sub‐consensus was determined by Kaplan‐Meier survival analysis. The common genes which were differentially expressed in each sub‐consensus of LUAD patients and LUAD cell lines were identified using TBtools. The predictive accuracy of TPX2 and SELENBP1 for theinner sub‐consensuses of LUAD was determined by Receiver operator characteristic (ROC) analysis. The Kaplan‐Meier survival analysis was also used to test the prognostic significance of TPX2 and SELENBP1 in LUAD patients.

Using nonnegative matrix factorization clustering, LUAD patients in The Cancer Genome Atlas (TCGA), GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets were divided into three sub‐consensuses. Sub‐consensus3 LUAD patients were with low overall survival and were with high TP53 mutations. Similarly, LUAD cell lines were also divided into three sub‐consensuses by NMF method, and sub‐consensus2 cell lines were resistant to EGFR inhibitors. Identification of the common genes which were differentially expressed in different sub‐consensuses of LUAD patients and LUAD cell lines revealed that TPX2 was highly expressed in sub‐consensus3 LUAD patients and sub‐consensus2 LUAD cell lines. On the contrary, SELENBP1 was highly expressed in sub‐consensus1 LUAD patients and sub‐consensus1 LUAD cell lines. The expression levels of TPX2 and SELENBP1 could distinguish sub‐consensus3 LUAD patients or sub‐consensus2 LUAD cell lines from other sub‐consensuses of LUAD patients or cell lines. Moreover, compared with normal lung tissues, TPX2 was highly expressed, while, SELENBP1 was lowly expressed in LUAD tissues. Furthermore, the higher expression levels of TPX2 were associated with the lower relapse‐free survival and the lower overall survival of LUAD patients. While, the higher expression levels of SELENBP1 were associated with the higher relapse‐free survival and higher overall survival. At last, we showed that TP53 mutant LUAD patients were with higher TPX2 and lower SELENBP1 expressions.

Both iCluster and NMF method are proved to be robust LUAD classification systems. However, the LUAD patients in different iclusters had no significant clinical overall survival, while, sub‐consensus3 LUAD patients from NMF classification were with lower overall survival than other sub‐consensuses.

By integrated analysis of 1765 LUAD patients and 64 LUAD cell lines, we showed that NMF was a robust inner sub‐consensuses classification method of LUAD. TPX2 and SELENBP1 were differentially expressed in different LUAD sub‐ consensuses, and predicted the inner sub‐consensuses of LUAD with high accuracy. TPX2 was an unfavorable prognostic biomarker of LUAD which was up‐regulated in LUAD tissues and associated with the low overall survival of LUAD. SELENBP1 was a favorable prognostic biomarker of LUAD which was down‐regulated in LUAD tissues and associated with the prolonged overall survival of LUAD.

• SELENBP1
• TPX2
• nonnegative matrix factorization
• sub-consensus of lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common and malignant cancer types. Abnormal cell proliferation, exemplified by cell cycle and cell division dysregulation, is one of the most prominent hallmarks of cancer and is responsible for recurrence, metastasis, and resistance to cancer therapy. However, LUAD-specific gene regulation and clinical significance remain obscure. Here, by using both tissues and cells from LUAD and normal lung samples, 434 increased and 828 decreased genes of biological significance were detected, including 127 cell cycle-associated genes (95 increased and 32 decreased), 66 cell division-associated genes (56 increased and 10 decreased), and 81 cell proliferation-associated genes (34 increased and 47 decreased). Among them, 12 increased genes (TPX2, CENPF, BUB1, PLK1, KIF2C, AURKB, CDKN3, BUB1B, HMGA2, CDK1, ASPM, and CKS1B) and 2 decreased genes (TACC1 and MYH10) were associated with all the three above processes. Importantly, 2 (CDKN3 and CKS1B) out of the 11 increased genes (except HMGA2) are previously uncharacterized ones in LUAD and can potentially be prognostic markers. Moreover, PLK1 could be a promising therapeutic target for LUAD. Besides, protein–protein interaction network analysis showed that CDK1 and CDC20 were the hub genes, which might play crucial roles in cell proliferation of LUAD. Furthermore, transcriptional regulatory network analysis suggested that the transcription factor E2F1 could be a key regulator in controlling cell proliferation of LUAD via expression modulation of most cell cycle-, cell division-, and cell proliferation-related DEGs. Finally, trichostatin A, hycanthone, vorinostat, and mebeverine were identified as four potential therapeutic agents for LUAD. This work revealed key regulators contributing to cell proliferation in human LUAD and identified four potential therapeutic agents for treatment strategy.

• cell proliferation
• lung adenocarcinoma
• prognosis
• protein–protein interaction
• transcriptional regulation

Non-small cell lung cancer (NSCLC) is one of the fatal tumors and is associated with a poor prognosis. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to quantify the proportions of 22 types of immune cells. Weighted gene co-expression network analysis (WGCNA) was established from the GSE37745 data, and key modules correlating most with CD8⁺ T cell infiltration were determined. Genes that manifested a high module connectivity in the key module were identified as hub genes. Three bioinformatics online databases were used to evaluate hub gene expression levels in tumor and normal tissues. Finally, survival analysis was conducted for these hub genes. In this study, we chose four hub genes (AURKB, CDC20, TPX2 and KIF2C) based on the comprehensive bioinformatics analyses. All hub genes were overexpressed in tumor tissue, and high expression of AURKB, CDC20, TPX2, and KIF2C correlated with the poor prognosis of these patients. In vitro experiments confirmed that CDC20 knockdown inhibited cell proliferation and growth. The above results indicated that AURKB, CDC20, TPX2, and KIF2C are potential CD8⁺ T cell infiltration-related biomarkers and therapeutic targets.

• CIBERSORT
• Non-small cell lung cancer
• Weighted gene co-expression network analysis

• AURKA
• CXCL5
• Cytotoxic autophagy
• NSCLC
• Radiosensitivity

• A549 Cells
• Animals
• Aurora Kinase A/genetics
• Aurora Kinase A/metabolism
• Autophagy
• Carcinoma, Non-Small-Cell Lung/drug therapy
• Carcinoma, Non-Small-Cell Lung/enzymology
• Carcinoma, Non-Small-Cell Lung/genetics
• Carcinoma, Non-Small-Cell Lung/pathology
• Cell Movement
• Chemokine CXCL5/genetics
• Chemokine CXCL5/metabolism
• Female
• Gene Expression Regulation, Neoplastic
• Gene Silencing
• Humans
• Lung Neoplasms/enzymology
• Lung Neoplasms/genetics
• Lung Neoplasms/pathology
• Lung Neoplasms/radiotherapy
• Male
• Mice, Inbred BALB C
• Mice, Nude
• Middle Aged
• NF-kappa B/metabolism
• Neoplasm Invasiveness
• Radiation Tolerance/genetics
• Signal Transduction
• Tumor Burden
• Xenograft Model Antitumor Assays
• Mice

• BRCA1
• CDC20
• H1N1
• MAD2L1
• WGCNA

The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC).

In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence.

Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87.

Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.

The online version contains supplementary material available at 10.1007/s00259-021-05371-7.

• Artificial intelligence
• Gene expression
• Image analysis
• Lung cancer
• Mutation
• PET/CT
• Radiogenomics

• Carcinoma, Non-Small-Cell Lung
• Humans
• Lung Neoplasms/diagnostic imaging
• Lung Neoplasms/genetics
• Neoplasm Recurrence, Local
• Positron Emission Tomography Computed Tomography
• Receptors, Eph Family
• Retrospective Studies
• Transcriptome

• Associazione Italiana per la Ricerca sul Cancro

Cell division cycle protein (CDC20) has been observed to be expressed higher in various kinds of human cancers and was associated with poor prognosis. However, studies on role of CDC20 in breast cancer are seldom reported till now, most of which are not systematic and conclusive. The present study was performed to analyze the expression pattern, potential function, and distinct prognostic effect of CDC20 in breast cancer using several online databases including Oncomine, bc-GenExMiner, PrognoScan, and UCSC Xena. To verify the results from databases, we compared the mRNA CDC20 expression in breast cancer tissues and adjacent normal tissues of patients by real-time PCR. We found that CDC20 was expressed higher in different types of breast cancer, comparing with normal tissues. Moreover, the patients with a more advanced stage of breast cancer tended to express higher level CDC20. CDC20 was expressed higher in breast cancer tissues than normal tissues from patients in our hospital, consistent with the results from databases. Estrogen receptor (ER) and progesterone receptor (PR) status were negatively correlated with CDC20 level. Conversely, Scarff–Bloom–Richardson (SBR) grade, Nottingham prognostic index (NPI), epidermal growth factor receptor-2 (HER-2) status, basal-like status, and triple-negative status were positively related to CDC20 expression in breast cancer patients with respect to normal individuals. Higher CDC20 expression correlated with worse survival. Finally, a positive correlation between CDC20 and Targeting protein for Xenopus kinesin-like protein 2 (TPX2) expression was revealed. CDC20 could be considered as a potential predictive indicator for prognosis of breast cancer with co-expressed TPX2 gene.

• Bioinformatics analysis
• Breast cancer
• CDC20
• Prognosis

Chronic obstructive pulmonary disease (COPD) is characterized by persistent airway inflammation and fixed airflow obstruction. Patients with COPD have increased risk of lung cancer (LC), and the coexistence of both diseases is associated with poorer survival. However, the mechanisms predisposing patients with COPD to LC development and poor prognosis remain unclear. Gene expression profiles were downloaded from the Gene Expression Omnibus. Twenty‐two data sets were included (n = 876). We identified 133 DEGs and 145 DEGs in patients with COPD and LC compared with healthy controls, respectively. There were 1544 DEGs in patients with LC and coexisting COPD compared with COPD, and these DEGs are mainly involved in the cell cycle, DNA replication, p53 signalling and insulin signalling. The biological processes primarily associated with these DEGs are oxidation reduction and apoptosis. SPP1 was the only overlapping DEG that was up‐regulated in patients with COPD and/or LC, and this was validated by qPCR in an independent cohort. The area under the curve value for SPP1 was 0.893 (0.822–0.963) for the prediction of LC in patients with COPD. High expression of SPP1 in patients with LC was associated with shorter survival time. Up‐regulation of SPP1 may be associated with increased risk of LC in patients with COPD and therefore may have potential as a therapeutic target for LC in patients with COPD.

• SPP1
• bioinformatics
• chronic obstructive pulmonary disease
• lung cancer
• meta-analysis

Lung adenocarcinoma (LUAD) has become the main histologic type, which account for nearly 40% of lung cancer. The present study aimed to investigate the gene expression signature in smoking related LUAD. A total of 45 smoking related DEGs in LUAD were identified and functional enrichment analysis was also performed. Then Cox's regression model and Kaplan-Meier analysis were used to screen potential prognostic genes. Finally, AURKA and FAM83A were left for further immune-related mechanism exploration. Kaplan-Meier analysis indicated survival rates are related to different immune cell (B cell and Dendritic cell) infiltration levels. Mechanistically, we further explore the correlation between AURKA and FAM83A gene expression levels and tumor-infiltrating lymphocytes (TILs) level as well as their response to immunomodulators. The results suggested that AURKA and FAM83A are highly expressed in smoking related LUAD, and negatively correlated to B cell and Dendritic cell infiltration levels. At the same time, B cell and Dendritic cell infiltration levels also related to the prognosis of LUAD. We further revealed AURKA and FAM83A could be novel targets to improve the prognosis of LUAD through regulated the response to immunomodulators.

• lung adenocarcinoma
• prognosis
• smoking
• tumor-infiltrating lymphocytes

• HSC70
• clear cell renal cell carcinoma
• prognosis

• Aged
• Biomarkers, Tumor/analysis
• Biomarkers, Tumor/metabolism
• Carcinoma, Renal Cell/diagnosis
• Carcinoma, Renal Cell/mortality
• Carcinoma, Renal Cell/secondary
• Carcinoma, Renal Cell/surgery
• Disease Progression
• Female
• Follow-Up Studies
• Gene Expression Regulation, Neoplastic
• HSC70 Heat-Shock Proteins/analysis
• HSC70 Heat-Shock Proteins/metabolism
• Humans
• Kaplan-Meier Estimate
• Kidney/pathology
• Kidney/surgery
• Kidney Neoplasms/diagnosis
• Kidney Neoplasms/mortality
• Kidney Neoplasms/pathology
• Kidney Neoplasms/surgery
• Male
• Middle Aged
• Nephrectomy
• Prognosis
• Up-Regulation

Lung cancer has remained one of the major causes of death worldwide. Thus, a more effective treatment approach is essential, such as the inhibition of specific cancer-promoting molecules. Aurora kinases regulate the process of mitosis—a process of cell division that is necessary for normal cell proliferation. Dysfunction of these kinases can contribute to cancer formation. In this review, we present studies indicating the implication of Aurora kinases in tumor formation, drug resistance, and disease prognosis. The effectivity of using Aurora kinase inhibitors in the pre-clinical and clinical investigations has proven their therapeutic potential in the setting of lung cancer. This work may provide further information to broaden the development of anticancer drugs and, thus, improve the conventional lung cancer management.

Despite tremendous efforts to improve the treatment of lung cancer, prognosis still remains poor; hence, the search for efficacious therapeutic option remains a prime concern in lung cancer research. Cell cycle regulation including mitosis has emerged as an important target for cancer management. Novel pharmacological agents blocking the activities of regulatory molecules that control the functional aspects of mitosis such as Aurora kinases are now being investigated. The Aurora kinases, Aurora-A (AURKA), and Aurora B (AURKB) are overexpressed in many tumor entities such as lung cancer that correlate with poor survival, whereby their inhibition, in most cases, enhances the efficacy of chemo-and radiotherapies, indicating their implication in cancer therapy. The current knowledge on Aurora kinase inhibitors has increasingly shown high potential in ensuing targeted therapies in lung malignancies. In this review, we will briefly describe the biology of Aurora kinases, highlight their oncogenic roles in the pre-clinical and clinical studies in lung cancer and, finally, address the challenges and potentials of Aurora kinases to improve the therapy of this malignancy.

• AURKA
• AURKB
• Aurora kinases
• lung cancer
• lung cancer therapy

• bioinformatic analysis
• differentially expressed genes
• lung adenocarcinoma

• Adenocarcinoma/genetics
• Computational Biology
• Databases, Genetic
• Gene Expression Regulation, Neoplastic
• Humans
• Lung Neoplasms/genetics

• bioinformatics analysis
• biomarker
• lung adenocarcinoma
• prognosis
• single nucleotide polymorphism

• Adenocarcinoma of Lung/genetics
• Adenocarcinoma of Lung/mortality
• Biomarkers, Tumor/genetics
• Calcium-Binding Proteins/genetics
• Chromosomal Proteins, Non-Histone/genetics
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Gene Ontology
• Humans
• Kaplan-Meier Estimate
• Lung Neoplasms/genetics
• Lung Neoplasms/mortality
• Microfilament Proteins/genetics
• Mutation
• Neural Cell Adhesion Molecules/genetics
• Polymorphism, Single Nucleotide
• Prognosis
• Protein Interaction Maps/genetics
• Reproducibility of Results
• Semaphorins/genetics
• Tumor Suppressor Protein p53/genetics

• DNA methylation
• carbon dots
• gene expression
• human lung fibroblasts
• surface charge

• Apoptosis/drug effects
• Apoptosis/genetics
• Carbon/pharmacology
• Cell Proliferation/drug effects
• Cell Proliferation/genetics
• Cells, Cultured
• DNA Methylation/drug effects
• DNA Methylation/genetics
• Extracellular Matrix/genetics
• Fibroblasts/drug effects
• Gene Expression/drug effects
• Gene Expression/genetics
• Gene Expression Profiling/methods
• Humans
• Lung/drug effects
• MicroRNAs/genetics
• Neoplasms/genetics
• Oxidative Stress/drug effects
• Oxidative Stress/genetics
• RNA, Messenger/genetics
• Signal Transduction/drug effects
• Signal Transduction/genetics

• LO1508 Ministerstvo Školství, Mládeže a Tělovýchovy
• LM2015073 Ministerstvo Školství, Mládeže a Tělovýchovy
• CZ.02.1.01/0.0/0.0/16_019/0000754 Ministerstvo Školství, Mládeže a Tělovýchovy
• CZ.02.1.01/0.0/0.0/16_019/0000754 Univerzita Palackého v Olomouci

• Anaplastic thyroid cancer
• Bioinformatics analysis
• Differentially expressed genes
• Quantitative real-time PCR

• CDC2 Protein Kinase/genetics
• Cdc20 Proteins
• Cell Cycle/genetics
• Cell Cycle Proteins/genetics
• Cell Line, Tumor
• Checkpoint Kinase 1/genetics
• Computational Biology/methods
• Cyclin B2/genetics
• Genetic Association Studies/methods
• Genetic Markers
• Humans
• Molecular Targeted Therapy
• Protein Serine-Threonine Kinases/genetics
• Reverse Transcriptase Polymerase Chain Reaction
• Ribonucleoside Diphosphate Reductase/genetics
• Signal Transduction/genetics
• Thyroid Neoplasms/diagnosis
• Thyroid Neoplasms/genetics
• Thyroid Neoplasms/metabolism
• Thyroid Neoplasms/pathology

Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.

To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD).

We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID.

Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD.

23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

• GSE31210
• bioinformatics analysis
• core genes
• lung adenocarcinoma
• smoking

Lung cancer (LC) is one of the most lethal and most prevalent malignant tumors, and its incidence and mortality are increasing annually. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. Several biomarkers have been confirmed by data excavation to be related to metastasis, prognosis and survival. However, the moderate predictive effect of a single gene biomarker is not sufficient. Thus, we aimed to identify new gene signatures to better predict the possibility of LUAD.

Using an mRNA-mining approach, we performed mRNA expression profiling in large LUAD cohorts (n = 522) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and connections between genes and glycolysis were found in the Cox proportional regression model.

We confirmed a set of nine genes (HMMR, B4GALT1, SLC16A3, ANGPTL4, EXT1, GPC1, RBCK1, SOD1, and AGRN) that were significantly associated with metastasis and overall survival (OS) in the test series. Based on this nine-gene signature, the patients in the test series could be divided into high-risk and low-risk groups. Additionally, multivariate Cox regression analysis revealed that the prognostic power of the nine-gene signature is independent of clinical factors.

Our study reveals a connection between the nine-gene signature and glycolysis. This research also provides novel insights into the mechanisms underlying glycolysis and offers a novel biomarker of a poor prognosis and metastasis for LUAD patients.

• Glycolysis
• Lung adenocarcinoma
• Meta-analysis survival
• Metastasis
• Prognostic
• mRNAs

• coexpression networks
• differentially expressed genes (DEGs)
• early detection and prognosis biomarkers
• interstitial lung diseases (ILDs)
• lung cancer
• pulmonary arterial hypertension (PAH)
• survival

Epidemiological studies suggested the use of antidepressants to be associated with decreased risk of colorectal cancer (CRC). However, the underlying mechanism through which this decreased risk occurs remains elusive. The norepinephrine transporter (NET) is a target of antidepressants that maintains noradrenergic transmission homeostasis; however, little is known about its function in human CRC cells. The present study, using public datasets and immunohistochemistry approaches, revealed that NET was highly expressed in human CRC tissues with metastasis and in human colon cancer cells. Furthermore, knockdown of NET inhibited the invasive capability of human colon cancer cells. Additionally, epithelial (E)-cadherin expression was increased and Notch1 signaling was inhibited in NET-depleted colon cancer cells. These findings suggest that NET is highly expressed in human colon cancer, which is associated with the invasion of human colon cancer cells by influencing cell-cell adhesion through the Notch1-E-cadherin pathway. Thus, the present study revealed a novel function for NET and its downstream effectors in colon cancer cells, which will be valuable for future studies in a clinical setting.

• human colon cancer cell
• invasion
• norepinephrine transporter

Introduction: Endometrial cancer is one of the most common uterine cancers worldwide. AKT is reported to regulate progesterone receptor B dependent transcription and angiogenesis in endometrial cancer. However, the potential mechanisms of AKT in the tumor progression of endometrial cancer remain unclear.

Methods: We used GSE72708 with gene expression profiles of AKT regulation from the GEO database. We performed GSEA analysis to explore pathway enrichments. We found that most upregulated enriched pathways in siAKT group were associated with acid metabolism and immune network. Endometrial cancer and various signaling pathways were downregulated enriched. Moreover, different molecular mechanism of regulation between progestin (R5020) and AKT was identified, which were related to VEGF signaling pathway. The hub genes were evaluated by immunohistochemical staining of endometrial cancer tissues.

Results: We screened out a total of 623 differentially expressed genes among different groups. According to weighted gene co-expression network analysis (WGCNA) method, four distinct modules were identified. We found brown module showed a very high positive correlation with siAKT group and a very high negative correlation with R5020 group. A total of six hub genes including PBK, BIRC5, AURKA, GTSE1, KNSTRN, and PSMB10 were finally identified associated with AKT1. In addition, the data also shows that the higher expression of AKT1, GTSE1, BIRC5, AURKA, and KNSTRN is significantly associate with poor prognosis of endometrial cancer.

Conclusion: Our study identified six hub genes related to the prognosis of endometrial cancer, which may provide new insights into the underlying biological mechanisms driving the tumorigenesis of endometrial cancer, especially in AKT1 regulation.

• AKT1
• GEO
• WGCNA
• bioinformatics
• endometrial cancer

• non-small cell lung cancer
• overall survival
• prognostic signature
• risk score
• stage

• Cox regression model
• gene set enrichment analysis
• lung squamous cell carcinoma
• prognosis
• risk score

• bioinformatics analysis
• candidate small molecule
• lower-grade glioma
• novel biomarkers
• prognosis