Cancer Expression Profiles Research Articles

Single-cell sequencing analysis revealed that WDR72 was a novel cancer stem cells related gene in gastric cancer

BackgroundCancer stem cells (CSCs) are pivotal in tumor resistance to chemotherapy and gastric cancer's rapid proliferation and metastasis. We aimed to explore the CSCs-related genes in gastric cancer epithelial cells. MethodsThe mRNA expression profile and single-cell sequencing data of gastric cancer were downloaded from the public database. ResultsWe identified WDR72 as a CSCs-related gene in gastric cancer epithelial cells. WDR72 was highly expressed in gastric cancer tissues, and high expression of WDR72 was associated with inferior prognosis of patients. WDR72 expression had a significant negative correlation with the infiltration of CD8 + T cells and activated memory CD4 + T cells. PD-L1 expression was significantly reduced in gastric cancer patients with high WDR72 expression. WDR72 was correlated with IC50 of multiple small-molecule drugs. ConclusionWe identified a novel CSCs-related gene in gastric cancer epithelial cells, WDR72, which was highly expressed in patients with high stemness scores.

A pan-cancer analysis of the core pre-mRNA 3′ end processing factors, and their association with prognosis, tumor microenvironment, and potential targets

Alternative polyadenylation (APA) is a crucial mechanism for regulating gene expression during pre-mRNA 3′ processing. Pre-mRNA 3′ end processing factors is the main factor involved in this process. However, pre-mRNA 3′ end processing factors in different cancer expression profiles and the relationship between pre-mRNA 3′ end processing factors and tumor microenvironment and the prognosis of the same patient is still unclear. In this study, we conducted a comprehensive exploration of the core pre-mRNA 3′ end processing factors across various cancer types by utilizing common cancer database, and revealing a robust correlation between the expression of these core factors and tumor characteristics. Leveraging advanced bioinformatics databases, we evaluated the expression levels and prognostic relevance of pre-mRNA 3′ end processing factors across pan-cancer tissues. Our extensive pan-cancer analysis revealed unique expression patterns of pre-mRNA 3′ end processing factors in both tumor and adjacent non-tumorous tissues. Notably, we found a significant correlation between the expression levels of pre-mRNA 3′ end processing factors and patient prognosis. Furthermore, we identified strong associations between pre-mRNA 3′ end processing factors expression and various factors, such as stromal, immune, RNA stemness, and DNA stemness scores across pan-cancer tissues. Our data also highlighted a link between the expression of pre-mRNA 3′ end processing factors and sensitivity to specific drugs, including pyrazoloacndine, amonaflide, and chelerythrinede, among others. We found four key pre-mRNA 3′ end processing factors that play a crucial role in mRNA preprocessing. Our study illuminates the potential promotion and inhibition role of pre-mRNA 3′ end processing regulators in the progression of cancer, CPSF2, CPSF3, CSTF2, SYMPK offering valuable insights for future research investigations on these regulators as diagnostic markers and therapeutic targets across pan-cancer.

A novel high-risk model identified by epithelial-mesenchymal transition predicts prognosis and radioresistance in rectal cancer.

Many studies have shown that tumor cells that survive radiotherapy are more likely to metastasize, but the underlying mechanism remains unclear. Here we aimed to identify epithelial-mesenchymal transition (EMT)-related key genes, which associated with prognosis and radiosensitivity in rectal cancer. First, we obtained differentially expressed genes by analyzing the RNA expression profiles of rectal cancer retrieved from The Cancer Genome Atlas database, EMT-related genes, and radiotherapy-related databases, respectively. Then, Lasso and Cox regression analyses were used to establish an EMT-related prognosis model (EMTPM) based on the identified independent protective factor Fibulin5 (FBLN5) and independent risk gene EHMT2. The high-EMTPM group exhibited significantly poorer prognosis. Then, we evaluated the signature in an external clinical validation cohort. Through in vivo experiments, we further demonstrated that EMTPM effectively distinguishes radioresistant from radiosensitive patients with rectal cancer. Moreover, individuals in the high-EMTPM group showed increased expression of immune checkpoints compared to their counterparts. Finally, pan-cancer analysis of the EMTPM model also indicated its potential for predicting the prognosis of lung squamous cell carcinoma and breast cancer patients undergoing radiotherapy. In summary, we established a novel predictive model for rectal cancer prognosis and radioresistance based on FBLN5 and EHMT2 expressions, and suggested that immune microenvironment may be involved in the process of radioresistance. This predictive model could be used to select management strategies for rectal cancer.

Loss of tumor-derived SMAD4 enhances primary tumor growth but not metastasis following BMP4 signalling

BackgroundBone morphogenetic protein 4 (BMP4) is a potent inhibitor of breast cancer metastasis. However, a tumor-promoting effect of BMP4 is reported in other tumor types, especially when SMAD4 is inactive.MethodsTo assess the requirement for SMAD4 in BMP4-mediated suppression of metastasis, we knocked down SMAD4 in two different breast tumors and enforced SMAD4 expression in a third line with endogenous SMAD4 deletion. In addition, we assessed the requirement for SMAD4 in tumor cell-specific BMP signalling by expression of a constitutively active BMP receptor. Delineation of genes regulated by BMP4 in the presence or absence of SMAD4 was assessed by RNA sequencing and a BMP4-induced gene, MYO1F was assessed for its role in metastasis. Genes regulated by BMP4 and/or SMAD4 were assessed in a publicly available database of gene expression profiles of breast cancer patients.ResultsIn the absence of SMAD4, BMP4 promotes primary tumor growth that is accompanied by increased expression of genes associated with DNA replication, cell cycle, and MYC signalling pathways. Despite increased primary tumor growth, BMP4 suppresses metastasis in the absence of tumor cell expression of SMAD4. Consistent with the anti-metastatic activity of BMP4, enforced signalling through the constitutively active receptor in SMAD4 positive tumors that lacked BMP4 expression still suppressed metastasis, but in the absence of SMAD4, the suppression of metastasis was largely prevented. Thus BMP4 is required for suppression of metastasis regardless of tumor SMAD4 status. The BMP4 upregulated gene, MYO1F, was shown to be a potent suppressor of breast cancer metastasis. Gene signature upregulated by BMP4 in the absence of SMAD4 was associated with poor prognosis in breast cancer patients, whereas gene signature upregulated by BMP4 in the presence of SMAD4 was associated with improved prognosis.ConclusionsBMP4 expression is required for suppression of metastasis regardless of the SMAD4 status of the tumor cells. Since BMP4 is a secreted protein, we conclude that it can act both in an autocrine manner in SMAD4-expressing tumor cells and in a paracrine manner on stromal cells to suppress metastasis. Deletion of SMAD4 from tumor cells does not prevent BMP4 from suppressing metastasis via a paracrine mechanism.

A Metastatic Cancer Expression Generator (MetGen): A Generative Contrastive Learning Framework for Metastatic Cancer Generation.

Despite significant advances in tumor biology and clinical therapeutics, metastasis remains the primary cause of cancer-related deaths. While RNA-seq technology has been used extensively to study metastatic cancer characteristics, challenges persist in acquiring adequate transcriptomic data. To overcome this challenge, we propose MetGen, a generative contrastive learning tool based on a deep learning model. MetGen generates synthetic metastatic cancer expression profiles using primary cancer and normal tissue expression data. Our results demonstrate that MetGen generates comparable samples to actual metastatic cancer samples, and the cancer and tissue classification yields performance rates of 99.8 ± 0.2% and 95.0 ± 2.3%, respectively. A benchmark analysis suggests that the proposed model outperforms traditional generative models such as the variational autoencoder. In metastatic subtype classification, our generated samples show 97.6% predicting power compared to true metastatic samples. Additionally, we demonstrate MetGen's interpretability using metastatic prostate cancer and metastatic breast cancer. MetGen has learned highly relevant signatures in cancer, tissue, and tumor microenvironments, such as immune responses and the metastasis process, which can potentially foster a more comprehensive understanding of metastatic cancer biology. The development of MetGen represents a significant step toward the study of metastatic cancer biology by providing a generative model that identifies candidate therapeutic targets for the treatment of metastatic cancer.

A risk model based on lncRNA-miRNA-mRNA gene signature for predicting prognosis of patients with bladder cancer.

We aimed to analyze lncRNAs, miRNAs, and mRNA expression profiles of bladder cancer (BC) patients, thereby establishing a gene signature-based risk model for predicting prognosis of patients with BC. We downloaded the expression data of lncRNAs, miRNAs and mRNA from The Cancer Genome Atlas (TCGA) as training cohort including 19 healthy control samples and 401 BC samples. The differentially expressed RNAs (DERs) were screened using limma package, and the competing endogenous RNAs (ceRNA) regulatory network was constructed and visualized by the cytoscape. Candidate DERs were screened to construct the risk score model and nomogram for predicting the overall survival (OS) time and prognosis of BC patients. The prognostic value was verified using a validation cohort in GSE13507. Based on 13 selected. lncRNAs, miRNAs and mRNA screened using L1-penalized algorithm, BC patients were classified into two groups: high-risk group (including 201 patients ) and low risk group (including 200 patients). The high-risk group's OS time ( hazard ratio [HR], 2.160; 95% CI, 1.586 to 2.942; P= 5.678e-07) was poorer than that of low-risk groups' (HR, 1.675; 95% CI, 1.037 to 2.713; P= 3.393 e-02) in the training cohort. The area under curve (AUC) for training and validation datasets were 0.852. Younger patients (age ⩽ 60 years) had an improved OS than the patients with advanced age (age > 60 years) (HR 1.033, 95% CI 1.017 to 1.049; p= 2.544E-05). We built a predictive model based on the TCGA cohort by using nomograms, including clinicopathological factors such as age, recurrence rate, and prognostic score. The risk model based on 13 DERs patterns could well predict the prognosis for patients with BC.

Integrating single-cell and bulk expression data to identify and analyze cancer prognosis-related genes

Compared with traditional evaluation methods of cancer prognosis based on tissue samples, single-cell sequencing technology can provide information on cell type heterogeneity for predicting biomarkers related to cancer prognosis. Therefore, the bulk and single-cell expression profiles of breast cancer and normal cells were comprehensively analyzed to identify malignant and non-malignant markers and construct a reliable prognosis model. We first screened highly reliable differentially expressed genes from bulk expression profiles of multiple breast cancer tissues and normal tissues, and inferred genes related to cell malignancy from single-cell data. Then we identified eight critical genes related to breast cancer to conduct Cox regression analysis, calculate polygenic risk score (PRS), and verify the predictive ability of PRS in two data groups. The results show that PRS can divide breast cancer patients into high-risk group and low-risk group. PRS is related to the overall survival time and relapse-free interval and is a prognosis factor independent of conventional clinicopathological characteristics. Breast cancer is usually regarded as a cancer with a relatively good prognosis. In order to further explore whether this workflow can be applied to cancer with poor prognosis, we selected lung cancer for a comparative study. The results show that this workflow can also build a reasonable prognosis model for lung cancer. This study provides new insight and practical source code for further research on cancer biomarkers and drug targets. It also provides basis for survival prediction, treatment response prediction, and personalized treatment.

Exploring the expression and clinical significance of the miR-140-3p-HOXA9 axis in colorectal cancer

PurposeThis study aims to investigate the expression patterns and clinical significance of miR-140-3p and homeobox A9 (HOXA9) in colorectal cancer (CRC) selected by bioinformatic study, while elucidating their potential interplay.MethodsThe microRNA expression profiles of paired colorectal cancer and matched normal tissues were retrieved from the Gene Expression Omnibus Database. Differentially expressed microRNAs and microRNA candidates were filtered and subjected to further analysis. Clinicopathological data, along with paraffin-embedded samples of colorectal tumor tissues were collected to facilitate comprehensive analysis. Expression levels of miR-140-3p and HOXA9 were quantified using qRT-PCR and immunohistochemistry. Survival rates were determined using the Kaplan–Meier method, and the COX regression model was utilized to identify independent prognostic factors that impact the overall prognosis.ResultsMiR-140-3p was significantly downregulated in colorectal tumors compared to normal tissue, and HOXA9 was identified as a previously unreported potential downstream target. HOXA9 expression was elevated in tumors compared to normal tissues. Reduced miR-140-3p expression was associated with lymph node metastasis, while high HOXA9 expression correlated with both lymph node metastasis and lympho-vascular invasion. Patients with low miR-140-3p and high HOXA9 expression had a poorer prognosis. HOXA9 was identified as an independent risk factor for CRC patient survival.ConclusionThe miR-140-3p-HOXA9 signaling disruption is closely linked to lymph node metastasis and unfavorable prognosis in CRC. This axis shows promise as a clinical biomarker for predicting the CRC patient survival and a potential therapeutic target.

Genome-wide binding analysis unveils critical implication of B-Myb-mediated transactivation in cancers.

B-Myb, also known as MYB proto-oncogene like 2 (MYBL2), is an important transcription factor implicated in transcription regulation, cell cycle and tumorigenesis. However, the molecular mechanism underlying B-Myb-controlled transactivation in different cell contexts as well as its functional implication in cancers remains elusive. In this study, we have conducted a comprehensive genome-wide analysis of B-Myb binding sites in multiple immortalized or cancer cell lines and identified its critical target genes. The results revealed that B-Myb regulates a common set of core cell cycle genes and cell type-specific genes through collaboration with other important transcription factors (e.g. NFY and MuvB complex) and binding to cell type-invariant promoters and cell type-specific enhancers and super-enhancers. KIF2C, UBE2C and MYC were further validated as B-Myb target genes. Loss-of-function analysis demonstrated that KIF2C knockdown inhibited tumor cell growth both in vitro and in vivo, suppressed cell motility and cell cycle progression, accompanied with defects in microtubule organization and mitosis, strongly suggesting that KIF2C is a critical regulator of cancer cell growth and mitosis, and maintains high cancer cell motility ability and microtubule dynamics. Pan-cancer transcriptomic analysis revealed that the overexpression of both B-Myb and KIF2C presents as independent prognostic markers in various types of cancer. Notably, B-Myb associates with NFYB, binds to target gene promoters, enhancers and super-enhancers, and provokes a cascade of oncogenic gene expression profiles in cancers. Overall, our results highly suggest the critical implication of B-Myb-mediated gene regulation in cancers, and the promising therapeutic and prognostic potentials of B-Myb and KIF2C for cancer diagnosis and treatment.

17-AAG Induces Endoplasmic Reticulum Stress-mediated Apoptosis in Breast Cancer Cells, Possibly Through PERK/eIF2α Up-regulation.

Breast cancer is the most predominant type of cancer affecting women worldwide and the current therapeutic treatment for breast cancer patients is not adequately effective. This study aimed to investigate the mechanism of 17-AAG, a heat shock protein (HSP90) inhibitor, as a treatment for inducing breast cancer cell apoptosis. The pharmacology network was employed to examine the correlation of 17-AAG with the gene expression profiles of breast cancer, obtained by Gene Expression Profiling Interactive Analysis (GEPIA). MTT and flow cytometry were utilized to investigate cell proliferation and cell apoptosis, respectively. Dichloro-dihydro-fluorescein diacetate (DCFH-DA) assay and western blot analysis were employed to examine the correlation between cellular oxidant levels and protein expression. Immunofluorescence staining was utilized to confirm the protein localization and assess DNA damage. The pharmacological network analysis revealed that HSP90 serves as the common target connecting 17-AAG and breast cancer genes. Treatment with 17-AAG significantly increased cell apoptosis. Moreover, the treatment resulted in up-regulation of cellular oxidant levels and PERK/eIF2α expression. In line with these, protein localization after treatment revealed an increase in DNA damage, correlating with higher ER stress levels. Furthermore, GEPIA demonstrated that PERK and eIF2α expression were significantly higher in breast invasive carcinoma compared to other tumor types. HSP90 emerges as a potential target for inducing apoptosis in breast cancer cells by disrupting protein homeostasis in the endoplasmic reticulum, possibly through PERK/eIF2α up-regulation. 17-AAG, an HSP90 inhibitor, may therefore potentially hold an alternative therapeutic strategy for breast cancer treatment.

A bioinformatic analysis found low expression and clinical significance of ATF4 in breast cancer

BackgroundActivating Transcription Factor 4 (ATF4) expression exhibits differential patterns across different types of tumors. Besides, the pathogenesis of breast cancer is complex, and the exact relationship between ATF4 and ATF4 remains uncertain. MethodsThe analysis of ATF4 expression was conducted by utilizing The Cancer Genome Atlas (TCGA) pan-cancer data, while the gene expression profile of breast cancer was checked by the comprehensive database-Gene Expression Omnibus database. In order to gain a more comprehensive understanding of the specific cell types that exhibit ATF4 expression within the microenvironment of breast cancer, we conducted a single-cell analysis of ATF4 using two distinct datasets of human breast cancer (GSE114717 and GSE11088, respectively). The spatial distribution of ATF4 within a tissue was demonstrated based on datasets obtained from the Human Protein Atlas (HPA) and SpatialDB. The clinical prognostic significance of ATF4 was assessed by analyzing clinical survival data obtained from TCGA, GSE4830, and GSE25055 datasets. We used the R package clusterProfiler to carry out an enrichment analysis of ATF4. We assessed how ATF4 impacts the growth and movement of breast cancer cell lines. We manipulated ATF4 levels using plasmid transfection techniques. ResultsThe expression of ATF4 was found to be suboptimal and demonstrated a significant correlation with enhanced disease-specific survival (p = 0.012) and overall survival (p = 0.032) in breast cancer as well as other malignancies. We conducted an analysis to investigate the interaction between the infiltration level of immune cells and the expression of ATF4, using samples obtained from TCGA with known immune cell infiltration scores. Furthermore, a notable positive correlation exists between the elevated expression of ATF4 and immune-related genomes, specifically those associated with chemokine as well as immunity. Subsequent examination revealed a notable augmentation in the cytodifferentiation of T cells into regulatory T (Treg) cells within tissues exhibiting elevated levels of ATF4 expression. ATF4 exhibits notable upregulation in the MDA-MB-231 cell, thereby exerting a substantial impact on cell proliferation and migration upon its knockdown. Conversely, the overexpression of ATF4 in the MCF7 Luminal A breast cancer cell line can also modulate cellular function. ConclusionsOur study suggests that ATF4 helps T cells differentiate into Treg cells in breast cancer. ATF4 can represent a clinically useful biomarker to predict the overall survival rate, especially in patients with different subtypes of breast cancer. Provide certain guidance value for the development of targeted drugs or inhibitors targeting ATF4.

Transcriptome Analysis of the Inhibitory Effects of 20(S)-Protopanaxadiol on NCI-H1299 Non-Small Cell Lung Cancer Cells.

Lung cancer seriously threatens human health. To explore the molecular mechanism of 20(S)-Protopanaxadiol (PPD) on human non-small cell lung cancer cells, we investigated the transcriptional profile of PPD-treated NCI-H1299 cells. Cell proliferation, cell cycle, and apoptosis were detected using cell counting kit-8 and flow cytometry, respectively. Differentially expressed genes (DEGs) between PPD-treated and untreated cells were determined using RNA sequencing and bioinformatic analysis. Protein phosphorylation was detected using Western blotting. Data of mRNA expression profiles of lung cancer were from The Cancer Genome Atlas (TCGA) and analyzed using R software version 4.3.1. PPD showed an inhibitory effect on the proliferation of NCI-H1299 cells and induced apoptosis. There were 938 upregulated genes and 466 downregulated genes in PPD-treated cells, and DEGs were primarily enriched in the MAPK signaling pathway. The detection of phosphorylation revealed that the phosphorylation of ERK and p38 MAPK was significantly reduced in PPD-treated cells. Further comparison of PPD-regulated DEGs with clinical data of lung adenocarcinoma demonstrated that most downregulated genes in tumor tissues were upregulated in PPD-treated cells or vice versa. Two PPD-downregulated genes HSPA2 and EFNA2 were associated with patients' overall survival. Therefore, PPD could inhibit NCI-H1299 cells by affecting gene expression and regulating ERK and p38 MAPK pathways.

Effects of aspirin on colon cancer using quantitative proteomic analysis

BackgroundColon cancer is one of the most prevalent digestive cancers worldwide. Results of epidemiological, experimental, and clinical studies suggest that aspirin inhibits the development of colon cancer. This study aimed to systematically elucidate the molecular mechanisms by which aspirin prevents colon carcinogenesis. MethodsWe determined the global protein expression profiles of colorectal cancer and aspirin-treated cells using quantitative proteomic analysis. We analyzed the proteomic results using bioinformatics (including differential proteins, protein annotation, Kyoto Encyclopedia of Genes and Genomes [KEGG] pathways, and protein–protein interaction [PPI] network). The viability of the colon cancer cell line and HT29 ​cells treated with aspirin was determined using the cell counting kit-8 assay. The differentially expressed proteins, such as p53 and cyclin-dependent kinase 1 (CDK1), were quantified using real-time polymerase chain reaction (PCR) and Western blotting. We measured cell cycle distribution and apoptosis in HT29 ​cells exposed to aspirin using fluorescence-activated cell sorting (FACS). ResultsWe found that 552 proteins were significantly dysregulated, of which 208 and 334 were upregulated and downregulated, respectively, in colon cancer cells exposed to 10 ​mmol/L of aspirin (95% confidence interval [CI]: -1.269 to -0.106, P ​< ​0.05). Further gene enrichment analysis revealed that cell cycle-related proteins, such as p53 and CDK1, were significantly differentially expressed. Proteomic analysis showed that after 24 ​h of aspirin exposure, the level of p53 increased by 2.52-fold and CDK1 was downregulated to half that of the controls in HT29 ​cells (95% CI: -0.619 to -0.364, P ​< ​0.05). Real-time PCR and Western blotting results showed that p53 was upregulated (95%CI: -3.088 to -1.912, P ​< ​0.001) and CDK1 was significantly downregulated after aspirin exposure in colon cancer cells (95% CI: 0.576 to 1.045, P ​< ​0.05). We observed that aspirin promoted G1/S cell cycle arrest in HT29 ​cells. We confirmed that aspirin induces apoptosis in human HT29 colon cancer cells in a concentration-dependent manner. ConclusionsThese results indicate that aspirin induces G1 arrest and apoptosis in colorectal cancer cells via the p53–CDK1 pathway. Aspirin may be a promising drug candidate for colon cancer prevention.

Identification of a novel therapeutic target for lung cancer: Mitochondrial ribosome protein L9

Lung cancer has a high fatality rate and incidence rate. At present, the initial and progress mechanism of lung cancer has not been completely elucidated and new therapeutic targets still need to be developed. In this study, the screening process was based on lung cancer expression profile data and survival analysis. Mitochondrial ribosome protein L9 (MRPL9) was upregulated in lung cancer tissues and related to the poor overall survival rate and recurrence-free survival rate of lung cancer patients. Knockdown of MRPL9 inhibited the proliferation, sphere-formation, and migration ability of lung cancer cells. MRPL9 was associated with the c-MYC signaling pathway, and lung cancer patients with high expression of both MRPL9 and MYC had a poor prognosis. Furthermore, c-MYC was associated with the epithelial-mesenchymal transition (EMT) regulatory protein zinc finger E-box binding homeobox 1 (ZEB1) by bioinformatics analysis. The relationship between ZEB1 and c-MYC was further confirmed by interfering with c-MYC expression. MRPL9 is a potential therapeutic target for lung cancer and exerts its biological functions by affecting the transcription factor c-MYC thereby regulating the EMT regulator ZEB1.

Identification of key molecular players and associated pathways in cervical squamous cell carcinoma progression through network analysis.

Cervical cancer is the primary cause of mortality among women in developing countries. Preventing cervical cancer is partially possible through early vaccination against the human papillomavirus, the most common cause of the disease. Nevertheless, it is imperative to understand the genetics of the disease progression to develop new therapeutic strategies. The present study aims to identify potential genes and associated pathways associated with cervical squamous cell carcinoma progression. We used an integrative approach by combining differential expression analysis, network biology, and functional enrichment analysis with survival analysis. In the present study, differential expression analysis of the microarray-based gene expression profiles of cervical cancer resulted in identifying a total of 544 significantly differentially expressed genes (DEGs). Further, centrality and network vulnerability analysis of the protein-protein interaction network (PPIN) and not well documented in cervical cancer, resulted in seven proteins (FN1, MCM5, TRIP13, KIF11, TTK, CDC45, and BUB1B), in which four proteins were vulnerable. These genes are mostly enriched in biological processes of cell division, mitotic nuclear division, cell cycle checkpoint, and cell proliferation in gene ontology analysis. The KEGG pathway enrichment analysis of the proteins lists them as mainly associated with the cell cycle. In the survival analysis, it was found that the genes MCM5, FN1, KIF11, and CDC45 were statistically significant prognostic factors for cervical cancer. The outcome of the current study identifies and explores the key role of the candidate genes involved in the progression of cervical cancer.

Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer.

Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear. Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features. Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore. Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination.

Dynamic Network Construction for Identifying Early Warning Signals Based On a Data-Driven Approach: Early Diagnosis Biomarker Discovery for Gastric Cancer.

During the development of complex diseases, there is a critical transition from one status to another at a tipping point, which can be an early indicator of disease deterioration. To effectively enhance the performance of early risk identification, a novel dynamic network construction algorithm for identifying early warning signals based on a data-driven approach (EWS-DDA) was proposed. In EWS-DDA, the shrunken centroid was introduced to measure dynamic expression changes in assumed pathway reactions during the progression of complex disease for network construction and to define early warning signals by means of a data-driven approach. We applied EWS-DDA to perform a comprehensive analysis of gene expression profiles of gastric cancer (GC) from The Cancer Genome Atlas database and the Gene Expression Omnibus database. Six crucial genes were selected as potential biomarkers for the early diagnosis of GC. The experimental results of statistical analysis and biological analysis suggested that the six genes play important roles in GC occurrence and development. Then, EWS-DDA was compared with other state-of-the-art network methods to validate its performance. The theoretical analysis and comparison results suggested that EWS-DDA has great potential for a more complete presentation of disease deterioration and effective extraction of early warning information.

Dynamic network biomarker to determine the critical point of breast cancer stage progression.

The discovery of early warning signs and biomarkers in patients with early breast cancer is crucial for the prevention and treatment of breast cancer. Dynamic Network Biomarker (DNB) is an approach based on nonlinear dynamics theory, which we exploited to identify a set of DNB members and their key genes as early warning signals during breast cancer staging progression. First, based on the gene expression profile of breast cancer in the TCGA database, the DNB algorithm was used to calculate the composite index (CI) of each gene cluster in the process of breast cancer anatomical staging. Then we calculated gene modules associated with the clinical phenotype stage based on weighted gene co-expression network analysis (WGCNA), combined with DNB membership to identify key genes in the network. We identified a set of gene clusters with the highest CI in Stage II as DNBs, whose roles in related pathways indicate the emergence of a tipping point and impact on breast cancer development. In addition, analysis of the key gene GPRIN1 showed that high expression of GPRIN1 predicts poor prognosis, and related immune analysis showed that GPRIN1 is involved in the development of breast cancer through immune aspects. The discovery of DNBs and the key gene GPRIN1 can provide potential biomarkers and therapeutic targets for breast cancer.

Five-hub genes identify potential mechanisms for the progression of asthma to lung cancer.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Bioinformatics-based genetic analysis of correlation between esophageal cancer and iron death

Tounderstand whether there is a correlation between esophageal cancer and iron death, we performed a series of integrative analyses of gene expression profiles of esophageal cancer and driver genes of iron death through bioinformatics, and finally we identified 9 genes associated with iron death with significant overall prognostic variability of the genes. Through gene enrichment we understood that these 9 genes were associated with various pathways such as autophagy and metabolism. Among them, CDO1 and YAP1 genes have the greatest impact on esophageal cancer and may become potential prognostic markers for esophageal cancer.