Analysis of the expression patterns and clinical relevance of m6A regulators in 33 cancer types.

Background: The role of N6-methyladenine (m6A) RNA methylation in a variety of biological processes is gradually being revealed. Methods: Here, we systematically describe the correlation between the expression pattern of m6A RNA methylation regulatory factors and clinical phenotype, immunity, drug sensitivity, stem cells and prognosis in more than 10,000 samples of 33 types of cancer. Results: The results show that there are significant differences in the expression of 20 m6A RNA methylation regulatory factors in different cancers, and there was a significant correlation with the analysis indicators. Conclusion: In this study, the m6A RNA methylation regulatory factor was found not only to potentially assist in stratifying the prognosis but also to predict or improve the sensitivity of clinical drug therapy.

Biomarker expression analysis in different age groups revealed age was a risk factor for breast cancer.

The relationship between age and breast cancer is ambiguous. Here, we analyzed the differential expression pattern of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in different age groups to provide an effective association between age and breast cancer risk at the molecular level. We integrated the microarray information from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. The patients were divided into young ( < 50 years) and old ( ≥ 50 years) age groups and evaluated by differential gene expression, weighted gene correlation network analysis (WGCNA), functional enrichment analyses, and coexpression analysis. To determine their potential clinical significance, univariate Cox regression analysis and survival assessment were conducted. We identified two lncRNAs (AL139280.1 and AP000851.1) and three mRNAs (MT1M, HBB, and TFPI2) as the risk markers, and Gene set enrichment analysis (GSEA) focusing on a single gene revealed that "pyrimidine metabolism," "cell cycle," and "P53 signaling pathway" were coenriched. These data demonstrated that age may be a risk factor for breast carcinogenesis and prognosis and provide an in-depth molecular characterization based on the expression patterns of lncRNAs and mRNAs.

circRNA-associated ceRNA network construction reveals the circRNAs involved in the progression and prognosis of breast cancer.

Recently, increasing evidences show that circular RNAs (circRNAs) are important regulators of various diseases, especially cancer. However, the regulatory role and the potential mechanism of action of circRNAs in breast cancer remain largely unknown. In this study, weighted gene co-expression network analysis was conducted with the differentially expressed miRNAs and mRNAs in breast cancer from The Cancer Genome Atlas database to identify the key modules associated with the carcinogenesis of breast cancer. In the significant turquoise and brown modules, 22 miRNAs and 1877 mRNAs were identified, respectively. Then, We compared and predicted the target genes and performed survival analysis to identify the miRNAs and mRNAs related to the prognosis of breast cancer. A circRNA-related competitive endogenous RNA network was identified by database co-screening, and deleted in liver cancer 1 (DLC1) was identified as a key gene. Finally, to assess how genes in key modules and key genes contribute to the development of breast cancer, relevant pathway information was obtained through DAVID and Gene Set Enrichment Analysis. These data demonstrated that three circRNAs (hsa-circ-0083373, hsa-circ-0083374, and hsa-circ-0083375) that regulate DLC1 expression via hsa-mir-511 and are involved in the pathogenesis and development of breast cancer.

Development of a risk scoring system for evaluating the prognosis of patients with Her2-positive breast cancer.

BACKGROUND: As one of the many breast cancer subtypes, human epidermal growth factor receptor 2 (Her2)-positive breast cancer has higher invasiveness and poor prognosis, although the advent of anti-Her2 drugs has brought good news to patients. However, the emergence of drug resistance still limits its clinical efficacy, so there is an urgent need to explore new targets and develop a risk scoring system to improve treatments and evaluate patient prognosis. METHODS: Differentially expressed mRNAs associated with Her2-positive breast cancer were screened from a TCGA cohort. The prognostic risk scoring system was constructed according to univariate and Lasso Cox regression model analyses and combined with clinical factors (such as age and TNM) for univariate and multivariate analyses to verify the specificity and sensitivity of the risk scoring system. Finally, based on correlation and CNV mutation analyses, we explored the research value of the mRNAs involved in the system as key genes of the model. RESULTS: In this study, six mRNAs were screened and identified to construct a prognostic risk scoring system, including four up-regulated mRNA (RDH16, SPC25, SPC24, and SCUBE3) and two down-regulated mRNA (DGAT2 and CCDC69). The risk scoring system can divide Her2-positive breast cancer samples into high-risk and low-risk groups to evaluate patient prognosis. In addition, whether through the time-dependent receiver operating characteristics curve or compared with clinical factors, the risk scoring system showed high predictive sensitivity and specificity. Moreover, some CNV mutations in mRNA increase patient risk by influencing expression levels. CONCLUSION: The risk scoring system constructed in this study is helpful to improve the screening of high-risk patients with Her2-positive breast cancer and is beneficial for implementing early diagnosis and personalized treatment. It is suggested that these mRNAs may play an important role in the progression of Her2-positive breast cancer.

Prognostic value of aberrantly expressed methylation gene profiles in lung squamous cell carcinoma: A study based on The Cancer Genome Atlas.

Currently, research on genome-scale epigenetic modifications for studying the pathogenesis of lung cancer is lacking. Aberrant DNA methylation, as the most common and important modification in epigenetics, is an important means of regulating genomic function and can be used as a biomarker for the diagnosis and prognosis of lung squamous cell carcinoma (LUSC). In this paper, methylation information and gene expression data from patients with LUSC were extracted from the TCGA database. Univariate and multivariate COX analyses were used to screen abnormally methylated genes related to the prognosis of LUSC. The relationship between key DNA methylation sites and the transcriptional expression of LUSC-related genes was explored. A prognostic risk model constructed by four abnormally methylated genes (VAX1, CH25H, AdCyAP1, and Irx1) was used to predict the prognosis of LUSC patients. Also, the methylation levels of the key gene IRX1 are significantly correlated with the prognosis and correlated with the methylation of the site cg09232937 and cg10530883. This study is based on high-throughput data mining and provides an effective bioinformatics basis for further understanding the pathogenesis and prognosis of LUSC, which has important theoretical significance for follow-up studies on LUSC.

Four lncRNAs associated with breast cancer prognosis identified by coexpression network analysis.

Previous studies on long noncoding RNA (lncRNA) have made breakthroughs in the treatment of several tumors, and these findings have brought attention to the lncRNA signature of breast cancer. Increased understanding of genomic architecture and achievement of innovative therapeutic strategies has prompted creation of a novel oncological model for the treatment of solid cancers. In this study, we systematically analyzed the transcriptome of breast cancer tissues to gain more in-depth knowledge of tumor biology. Gene coexpression relationships were studied in 206 samples from The Cancer Genome Atlas database, and nine coexpression modules were identified. After screening and analysis, we identified four important prognosis-related lncRNAs (HOTAIR, SNHG16, HCP5, and TINCR), and constructed a prognostic model, one (HCP5) of which has not previously been identified in the context of breast cancer. Importantly, an understanding of prognosis facilitates precise disease risk assessment and advances the selection of strategies for risk-adaptive management. These findings broaden the landscape of carcinogenic lncRNAs in breast cancer, providing insights into the biological significance and clinical application of lncRNAs in breast cancer.

DNA methylation-based diagnostic and prognostic biomarkers of nonsmoking lung adenocarcinoma patients.

Currently, there are few studies on patients with nonsmoking lung adenocarcinoma, and the pathogenesis is still unclear. The role of DNA methylation in the pathogenesis of cancer is gradually being recognized. The purpose of this study was to determine the abnormal methylation genes and pathways involved in nonsmoking lung adenocarcinoma patients. Gene expression microarray data (GSE10072, GSE43458) and gene methylation microarray data (GSE62948) were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes were obtained through GEO2R. Next, we analyzed the function and enrichment of the selected genes using Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction (PPI) networks were constructed using the Search Tool for the Retrieval of Interacting Genes database and visualized in Cytoscape. Finally, we performed module analysis of the PPI network using Molecular Complex Detection. And we obtained 10 hub genes by Cytoscape Centiscape. We analyzed the independent prognostic value of each hub gene in nonsmoking nonsmall cell lung cancer patients through Kaplan-Meier plotter. Seven hub genes (CXCL12, CDH1, CASP3, CREB1, COL1A1, ERBB2, and ENO2) were closely related to the overall survival time. This study provides an effective bioinformatics basis for further understanding the pathogenesis and prognosis of nonsmoking lung adenocarcinoma patients. Hub genes with prognostic value could be selected as effective biomarkers for timely diagnosis and prognostic of nonsmoking lung adenocarcinoma patients.

MicroRNAs associated with lung squamous cell carcinoma: New prognostic biomarkers and therapeutic targets.

BACKGROUND: With the development in research, the importance of microRNAs (miRNAs) in the occurrence and metastasis, and prognosis of lung squamous cell carcinoma (LUSC) have received extensive attention. This study aims to identify new biomarkers and pivotal genes associated with LUSC prognosis through bioinformatics. MATERIALS AND METHODS: We downloaded miRNA and messenger RNA samples related to LUSC from The Cancer Genome Atlas (TCGA) database. Following initial data screening and preprocessing, we utilized the R platform and a range of analysis tools (miRDB, TargetScanHuman7.2, DAVID, and Cytoscape_v3.5.1) to analyze the TCGA data and identify highly specific and sensitive biomarkers. RESULTS: We finally identified 12 miRNAs and six central genes closely related to the overall survival of patients with LUSC. We found that high expression of 7 miRNAs (miR-301b, miR-383, miR-512, miR-515, miR-525, miR-577, and miR-5682) and low expression of five miRNAs (miR-448, miR-486, miR-4732,miR-4732, miR-516, and miR-1911) can significantly improve the overall survival rate of patients with LUSC. The six central genes DLGAP5, CENPA, CHEK1, LRRK2, CALB1, and TOP2A are directly or indirectly involved in the formation and metastasis of LUSC and could, therefore, be considered as target genes for drug therapy. CONCLUSION: With the development in research, the importance of miRNAs in the occurrence and metastasis and prognosis of LUSC have received extensive attention. This study aims to identify new biomarkers and pivotal genes associated with LUSC prognosis through bioinformatics.

Identification of prognostic biomarkers for breast cancer based on miRNA and mRNA co-expression network.

PURPOSE: Breast cancer (BC) remains a serious health threat for women due to its high incidence and the trend of rejuvenation. Accumulating evidence has highlighted that microRNAs (miRNAs) and messenger RNAs (mRNAs) could play important roles in various biological processes involved in the pathogenesis of BC. The present study aimed to identify potential prognostic biomarkers associated with BC. METHODS: Here, original gene expression profiles of patients with BC was downloaded from The Cancer Genome Atlas (TCGA) database. TargetScan, miRDB, and miRTarBase databases were used to predict the target genes of prognostic-related differentially expressed miRNAs (DEMs). Subsequently, functional enrichment analysis and topological analysis were performed on the overlaps of target genes and differentially expressed mRNAs (DEGs), and Kaplan-Meier analysis was used to predict prognosis-related target genes to identify prognostic biomarkers. RESULTS: A total of 218 DEMs and 2222 DEGs were extracted in which eight miRNAs were associated with prognosis, and 278 target DEGs were screened out incorporated into functional enrichment analysis and protein-protein interaction network visualization studies. Additionally, five hub genes (CXCL12, IGF1, LEF1, MMP1, and RACGAP1) were observed as potential biomarkers for BC prognosis through survival analysis. CONCLUSION: We performed a distinctive correlation analysis of miRNA-mRNA in BC patients, and identified eight miRNAs and five hub genes may be effective biomarkers for the prognosis of BC patients.

Deciphering the mechanism of Indirubin and its derivatives in the inhibition of Imatinib resistance using a "drug target prediction-gene microarray analysis-protein network construction" strategy.

BACKGROUND: The introduction of imatinib revolutionized the treatment of chronic myeloid leukaemia (CML), substantially extending patient survival. However, imatinib resistance is currently a clinical problem for CML. It is very importantto find a strategy to inhibit imatinib resistance. METHODS: (1) We Identified indirubin and its derivatives and predicted its putative targets; (2) We downloaded data of the gene chip GSE2810 from the Gene Expression Omnibus (GEO) database and performed GEO2R analysis to obtain differentially expressed genes (DEGs); and (3) we constructed a P-P network of putative targets and DEGs to explore the mechanisms of action and to verify the results of molecular docking. RESULT: We Identified a total of 42 small-molecule compounds, of which 15 affected 11 putative targets, indicating the potential to inhibit imatinib resistance; the results of molecular docking verified these results. Six biomarkers of imatinib resistance were characterised by analysing DEGs. CONCLUSION: The 15 small molecule compounds inhibited imatinib resistance through the cytokine-cytokine receptor signalling pathway, the JAK-stat pathway, and the NF-KB signalling pathway. Indirubin and its derivatives may be new drugsthat can combat imatinib resistance.

MicroRNA expression in cervical cancer: Novel diagnostic and prognostic biomarkers.

Growing evidence has shown that a large number of miRNAs are abnormally expressed in cervical cancer (CC) tissues and play irreplaceable roles in tumorigenesis, progression, and metastasis. This study aimed to identify new biomarkers and pivotal genes associated with CC prognosis through comprehensive bioinformatics analysis. At first, the data of gene expression microarray (GSE30656) was downloaded from GEO database and differential miRNAs were obtained. Additionally, 4 miRNAs associated with the survival time of patients with CC were screened through TCGA differential data analysis, Kaplan-Meier, and Landmark analysis. Among them, the low expression of miR-188 and high expression of miR-223 correlated with the short survival of CC patients, while the down-regulation of miR-99a and miR-125b was closely related to the 5-year survival rate of patients. Then, based on the correspondence between the differentially expressed genes (DEGs) in CC from the TCGA data and the 4 miRNAs target genes, 58 target genes were screened to perform the analysis of function enrichment and the visualization of protein-protein interaction (PPI) networks. The seven pivotal genes of the PPI network as the target genes of four miRNAs related to prognosis, they were directly or indirectly involved in the development of CC. In this study, based on high-throughput data mining, differentially expressed miRNAs and related target genes were analyzed to provide an effective bioinformatics basis for further understanding of the pathogenesis and prognosis of CC. And the results may be a promising biomarker for the early screening of high-risk populations and early diagnosis of cervical cancer.

Developing DNA methylation-based prognostic biomarkers of acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous clonal neoplasm characterized by complex genomic alterations. The incidence of AML increases with age, and most cases experience serious illness and poor prognosis. To explore the relationship between abnormal DNA methylation and the occurrence and development of AML based on the Gene Expression Database (GEO), this study extracted data related to methylation in AML and identified a methylated CpG site that was significantly different in terms of expression and distribution between the primary cells of AML patients, and hematopoietic stem/progenitor cells from normal bone marrow. To further investigate the differences caused by the dysfunction of methylation sites, bioinformatics analysis was used to screen methylation-related biomarkers, and the potential prognostic genes were selected by univariate and multivariate Cox proportional hazards regressions. Finally, five independent prognostic indicators were identified. In addition, these results provide new insight into the molecular mechanisms of methylation.

Exploration of methylation-driven genes for monitoring and prognosis of patients with lung adenocarcinoma.

BACKGROUND: As one of the most common malignant tumors in humans, lung cancer has experienced a gradual increase in morbidity and mortality. This study examined prognosis-related methylation-driven genes specific to lung adenocarcinoma (LUAD) to provide a basis for prognosis prediction and personalized targeted therapy for LUAD patients. METHODS: The methylation and survival time data from LUAD patients in the TCGA database were downloaded. The MethylMix algorithm was used to identify the differential methylation status of LUAD and adjacent tissues based on the ß-mixture model to obtain disease-related methylation-driven genes. A COX regression model was then used to screen for LUAD prognosis-related methylation-driven genes, and a linear risk model based on five methylation-driven gene expression profiles was constructed. A methylation and gene expression combined survival analysis was performed to further explore the prognostic value of 5 genes independently. RESULTS: There were 118 differentially expressed methylation-driven genes in the LUAD tissues and adjacent tissues. Five of the genes, CCDC181, PLAU, S1PR1, ELF3, and KLHDC9, were used to construct a prognostic risk model. Overall, the survival time was significantly lower in the high-risk group compared with that in the low-risk group (P < 0.05). In addition, the methylation and gene expression combined survival analysis found that the combined expression levels of the genes CCDC181, PLAU, and S1PR1 as well as KLHDC9 alone can be used as independent prognostic markers or drug targets. CONCLUSION: Our findings provide an important bioinformatic basis and relevant theoretical basis for guiding subsequent LUAD early diagnosis and prognosis assessments.

Deciphering Key Pharmacological Pathways of Qingdai Acting on Chronic Myeloid Leukemia Using a Network Pharmacology-Based Strategy.

Qingdai, a traditional Chinese medicine (TCM) used for the treatment of chronic myeloid leukemia (CML) with good efficacy, has been used in China for decades. However, due to the complexity of traditional Chinese medicinal compounds, the pharmacological mechanism of Qingdai needs further research. In this study, we investigated the pharmacological mechanisms of Qingdai in the treatment of CML using network pharmacology approaches. First, components in Qingdai that were selected by pharmacokinetic profiles and biological activity predicted putative targets based on a combination of 2D and 3D similarity measures with known ligands. Then, an interaction network of Qingdai putative targets and known therapeutic targets for the treatment of chronic myeloid leukemia was constructed. By calculating the 4 topological features (degree, betweenness, closeness, and coreness) of each node in the network, we identified the candidate Qingdai targets according to their network topological importance. The composite compounds of Qingdai and the corresponding candidate major targets were further validated by a molecular docking simulation. Seven components in Qingdai were selected and 32 candidate Qingdai targets were identified; these were more frequently involved in cytokine-cytokine receptor interaction, cell cycle, p53 signaling pathway, MAPK signaling pathway, and immune system-related pathways, which all play important roles in the progression of CML. Finally, the molecular docking simulation showed that 23 pairs of chemical components and candidate Qingdai targets had effective binding. This network-based pharmacology study suggests that Qingdai acts through the regulation of candidate targets to interfere with CML and thus regulates the occurrence and development of CML.

A Systems Biology-Based Approach to Uncovering Molecular Mechanisms Underlying Effects of Traditional Chinese Medicine Qingdai in Chronic Myelogenous Leukemia, Involving Integration of Network Pharmacology and Molecular Docking Technology.

BACKGROUND The method of multiple targets overall control is increasingly used to predict the main active ingredient and potential target group of Chinese traditional medicines and to determine the mechanisms involved in their curative effects. Qingdai is the main traditional Chinese medicine used in the treatment of chronic myelogenous leukemia (CML), but the complex active ingredients and antitumor targets in treatment of CML have not been clearly defined in previous studies. MATERIAL AND METHODS We constructed a protein-protein interaction network diagram of CML with 638 nodes (proteins) and 1830 edges, based on the biological function of chronic myelocytic leukemia by use of Cytoscape, and we determined 19 key gene nodes in the CML molecule by network topological properties analysis in a data bank. Then, we used the Surflex-dock plugin in SYBYL7.3 docking and acquired the protein crystal structures of key genes involved in CML from the chemical composition of the traditional Chinese medicine Qingdai with key proteins in CML networks. RESULTS According to the score and the spatial structure, the pharmacodynamically active ingredients of Qingdai are Isdirubin, Isoindigo, N-phenyl-2-naphthylamine, and Isatin, among which Isdirubin is the most important. We further screened the most effective activity key protein structures of CML to find the best pharmacodynamically active ingredients of Qingdai, according to the binding interactions of the inhibitors at the catalytic site performed in best docking combinations. CONCLUSIONS The results suggest that Isdirubin plays a role in resistance to CML by altering the expressions of PIK3CA, MYC, JAK2, and TP53 target proteins. Network pharmacology and molecular docking technology can be used to search for possible reactive molecules in traditional chinese medicines (TCM) and to elucidate their molecular mechanisms.

Exploring risk factors for autoimmune diseases complicated by non-hodgkin lymphoma through regulatory T cell immune-related traits: a Mendelian randomization study.

Background: The effect of immune cells on autoimmune diseases (ADs) complicated by non-Hodgkin lymphoma (NHL) has been widely recognized, but a causal relationship between regulatory T cell (Treg) immune traits and ADs complicated by NHL remains debated. Methods: Aggregate data for 84 Treg-related immune traits were downloaded from the Genome-Wide Association Study (GWAS) catalog, and GWAS data for diffuse large B-cell lymphoma (DLBCL; n=315243), follicular lymphoma (FL; n=325831), sjögren's syndrome (SS; n=402090), rheumatoid arthritis (RA; n=276465), dermatopolymyositis (DM; n=311640), psoriasis (n=407876), atopic dermatitis (AD; n=382254), ulcerative colitis (UC; n=411317), crohn's disease(CD; n=411973) and systemic lupus erythematosus (SLE; n=307587) were downloaded from the FinnGen database. The inverse variance weighting (IVW) method was mainly used to infer any causal association between Treg-related immune traits and DLBCL, FL, SS, DM, RA, Psoriasis, AD, UC, CD and SLE, supplemented by MR-Egger, weighted median, simple mode, and weighted mode. Moreover, we performed sensitivity analyses to assess the validity of the causal relationships. Results: There was a potential genetic predisposition association identified between CD39+ CD8br AC, CD39+ CD8br % T cell, and the risk of DLBCL (OR=1.51, p<0.001; OR=1.25, p=0.001) (adjusted FDR<0.1). Genetic prediction revealed potential associations between CD25++ CD8br AC, CD28- CD25++ CD8br % T cell, CD39+ CD8br % CD8br, and the risk of FL (OR=1.13, p=0.022; OR=1.28, p=0.042; OR=0.90, p=0.016) (adjusted FDR>0.1). Furthermore, SLE and CD exhibited a genetically predicted potential association with the CD39+ CD8+ Tregs subset. SS and DM were possibly associated with an increase in the quantity of the CD4+ Tregs subset; RA may have reduced the quantity of the CD39+ CD8+ Tregs subset, although no causal relationship was identified. Sensitivity analyses supported the robustness of our findings. Conclusions: There existed a genetically predicted potential association between the CD39+ CD8+ Tregs subset and the risk of DLBCL, while SLE and CD were genetically predicted to be potentially associated with the CD39+ CD8+ Tregs subset. The CD39+ CD8+ Tregs subset potentially aided in the clinical diagnosis and treatment of SLE or CD complicated by DLBCL.

Potential of cucurbitacin as an anticancer drug.

In Chinese medicine, the Cucurbitaceae family contains many compounds known as cucurbitacins, which have been categorized into 12 classes ranging from A to T and more than 200 derivatives. Cucurbitacins are a class of highly oxidized tetracyclic triterpenoids with potent anticancer properties. The eight components of cucurbitacins with the strongest anticancer activity are cucurbitacins B, D, E, I, IIa, L-glucoside, Q, and R. Cucurbitacins have also been reported to suppress JAK-STAT 3, mTOR, VEGFR, Wnt/ß-catenin, and MAPK signaling pathways, all of which are crucial for the survival and demise of cancer cells. In this paper, we review the progress in research on cucurbitacin-induced apoptosis, autophagy, cytoskeleton disruption, cell cycle arrest, inhibition of cell proliferation, inhibition of invasion and migration, inhibition of angiogenesis, epigenetic alterations, and synergistic anticancer effects in tumor cells. Recent studies have identified cucurbitacins as promising molecules for therapeutic innovation with broad versatility in immune response. Thus, cucurbitacin is a promising class of anticancer agents that can be used alone or in combination with chemotherapy and radiotherapy for the treatment of many types of cancer.Therefore, based on the research reports in the past five years at home and abroad, we further summarize and review the structural characteristics, chemical and biological activities, and studies of cucurbitacins based on the previous studies to provide a reference for further development and utilization of cucurbitacins.

Natural compounds: Wnt pathway inhibitors with therapeutic potential in lung cancer.

The Wnt/ß-catenin pathway is abnormally activated in most lung cancer tissues and considered to be an accelerator of carcinogenesis and lung cancer progression, which is closely related to increased morbidity rates, malignant progression, and treatment resistance. Although targeting the canonical Wnt/ß-catenin pathway shows significant potential for lung cancer therapy, it still faces challenges owing to its complexity, tumor heterogeneity and wide physiological activity. Therefore, it is necessary to elucidate the role of the abnormal activation of the Wnt/ß-catenin pathway in lung cancer progression. Moreover, Wnt inhibitors used in lung cancer clinical trials are expected to break existing therapeutic patterns, although their adverse effects limit the treatment window. This is the first study to summarize the research progress on various compounds, including natural products and derivatives, that target the canonical Wnt pathway in lung cancer to develop safer and more targeted drugs or alternatives. Various natural products have been found to inhibit Wnt/ß-catenin in various ways, such as through upstream and downstream intervention pathways, and have shown encouraging preclinical anti-tumor efficacy. Their diversity and low toxicity make them a popular research topic, laying the foundation for further combination therapies and drug development.

ncRNA-mediated ceRNA regulatory network: Transcriptomic insights into breast cancer progression and treatment strategies.

With the rapid development of next-generation sequencing technology, several studies have shown that ncRNAs can act as competitive endogenous RNAs (ceRNAs) and are involved in various biological processes, such as proliferation, differentiation, apoptosis, and migration of breast cancer (BC) cells, and plays an important role in BC progression as a molecular target for its diagnosis, treatment, prognosis, and differentiation of subtypes and age groups of BC patients. Based on the description of ceRNA-related biological functions, this study screened and sorted the sequencing analysis and experimental verification conclusions of BC-related ceRNAs and found that the ncRNAs mediated ceRNA networks can promote the development of BC by promoting the expression of genes related to BC proliferation, drug resistance, and apoptosis, inducing the production of epithelial-mesenchymal transition (EMT) to promote metastasis and activating cancer-related signaling pathways.

Development of a Clinical Prognostic Model for Metabolism-Related Genes in Squamous Lung Cancer and Correlation Analysis of Immune Microenvironment.

Background: The incidence of squamous lung cancer (LUSC) has substantially increased. Systematic studies of metabolic genomic patterns are fundamental for the treatment and prediction of LUSC. Because cancer metabolism and immune cell metabolism have been studied in depth, metabolism and the state and function of immune cells have become key factors in tumor development. This also indicates that metabolic genes and the tumor immune microenvironment (TME) are crucial in tumor treatment. This study is aimed at dissecting the connection between TME and LUSC digestion-related qualities. Methods: The information used in this study was obtained from The Cancer Genome Atlas dataset. Metabolism-related genes in patients with LUSC were screened, and relevant clinical data were collated. Next, genes associated with prognosis were screened using univariate COX regression and LASSO regression analyses. Finally, a timer database study was conducted to analyze the molecular mechanisms of immune cell infiltration of LUSC prognosis-related metabolic genes at the immune cell level. Results: Nine metabolism-related genes were identified: ADCY7, ALDH3B1, CHIA, CYP2C18, ENTPD6, GGCT, HPRT1, PLA2G1B, and PTGIS. A clinical prediction model for LUSC based on metabolism-related genes was constructed. In addition, 22 subpopulations of tumor-infiltrating immune cells (TIIC) in the TME were analyzed using the CIBERSORT algorithm. Finally, we used the TIMER database to analyze the immune infiltration of LUSC and the relationship between metabolism-related genes and immune cells. Conclusion: Our study identified metabolic genes associated with the prognosis of LUSC, which are important markers for its diagnosis, clinically relevant assessments, and prognosis. The relationship between metabolic genes with prognostic impact and immune infiltration was also analyzed, and a metabolic gene-based clinical prediction model was identified, providing a new perspective for LUSC treatment.