Construction of the prediction model for multiple myeloma based on machine learning.

INTRODUCTION: The global burden of multiple myeloma (MM) is increasing every year. Here, we have developed machine learning models to provide a reference for the early detection of MM. METHODS: A total of 465 patients and 150 healthy controls were enrolled in this retrospective study. Based on the variable screening strategy of least absolute shrinkage and selection operator (LASSO), three prediction models, logistic regression (LR), support vector machine (SVM), and random forest (RF), were established combining complete blood count (CBC) and cell population data (CPD) parameters in the training set (210 cases), and were verified in the validation set (90 cases) and test set (165 cases). The performance of each model was analyzed using receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC) were applied to evaluate the models. Delong test was used to compare the AUC of the models. RESULTS: Six parameters including RBC (1012/L), RDW-CV (%), IG (%), NE-WZ, LY-WX, and LY-WZ were screened out by LASSO to construct the model. Among the three models, the AUC of RF model in the training set, validation set, and test set were 0.956, 0.892, and 0.875, which were higher than those of LR model (0.901, 0.849, and 0.858) and SVM model (0.929, 0.868, and 0.846). Delong test showed that there were significant differences among the models in the training set, no significant differences in the validation set, and significant differences only between SVM and RF models in the test set. The calibration curve and DCA showed that the three models had good validity and feasibility, and the RF model performed best. CONCLUSION: The proposed RF model may be a useful auxiliary tool for rapid screening of MM patients.

CLIP170 inhibits the metastasis and EMT of papillary thyroid cancer through the TGF-ß pathway.

Metastasis poses a significant challenge in combating tumors. Even in papillary thyroid cancer (PTC), which typically exhibits a favorable prognosis, high recurrence rates are attributed to metastasis. Cytoplasmic linker protein 170 (CLIP170) functions as a classical microtubule plus-end tracking protein (+TIP) and has shown close association with cell migration. Nevertheless, the specific impact of CLIP170 on PTC cells remains to be elucidated. Our analysis of the GEO and TCGA databases unveiled an association between CLIP170 and the progression of PTC. To explore the impact of CLIP170 on PTC cells, we conducted various assays. We evaluated its effects through CCK-8, wound healing assay, and transwell assay after knocking down CLIP170. Additionally, the influence of CLIP170 on the cellular actin structure was examined via immunofluorescence; we further investigated the molecular expressions of epithelial-mesenchymal transition (EMT) and the transforming growth factor-ß (TGF-ß) signaling pathways through Western blotting and RT-qPCR. These findings were substantiated through an in vivo nude mouse model of lung metastasis. We observed a decreased expression of CLIP170 in PTC in contrast to normal thyroid tissue. Functionally, the knockdown of CLIP170 (CLIP170KD) notably enhanced the metastatic potential and EMT of PTC cells, both in vitro and in vivo. Mechanistically, CLIP170KD triggered the activation of the TGF-ß pathway, subsequently promoting tumor cell migration, invasion, and EMT. Remarkably, the TGF-ß inhibitor LY2157299 effectively countered TGF-ß activity and significantly reversed tumor metastasis and EMT induced by CLIP170 knockdown. In summary, these findings collectively propose CLIP170 as a promising therapeutic target to mitigate metastatic tendencies in PTC.

CircRNAs in cancer therapy tolerance.

The rapidly expanding field of circular RNA (circ-RNA) research has opened new avenues in cancer diagnostics and treatment, highlighting the role of serum circRNAs as potential biomarkers for assessing tumor therapy resistance. This review comprehensively compiles existing knowledge regarding the biogenesis, function, and clinical relevance of circRNAs, emphasizing their stability, abundance, and cell type-specific expression profiles, which make them ideal candidates for noninvasive early biomarkers in cancer treatment. We explored the roles of circRNAs in oncogenesis and tumor progression and their complex interactions with patient responses to various cancer treatments, such as chemotherapy, radiotherapy, targeted therapy, and immunotherapy. Through the analysis of data from recent studies and clinical trials, we underscore the prognostic significance of serum circRNAs in predicting therapeutic outcomes, their involvement in resistance mechanisms, and their capacity to inform personalized treatment approaches. Additionally, this review addresses the obstacles inherent in circRNA research, including the need for standardized protocols for circRNA extraction and quantification and the elucidation of the clinical significance of circRNAs. Furthermore, our investigation extends to future prospects, including embedding circRNA profiling into regular clinical workflows and pioneering circRNA-based therapeutic approaches. We underscore the transformative potential of serum circRNAs in enhancing cancer diagnosis, improving the accuracy of therapy tolerance predictions, and ultimately fostering the advent of precision oncology.

DNA Methylation-Based Testing in Peripheral Blood Mononuclear Cells Enables Accurate and Early Detection of Colorectal Cancer.

An effective blood-based method for the diagnosis of colorectal cancer has not yet been developed. Molecular alterations of immune cells occur early in tumorigenesis, providing the theoretical underpinning for early cancer diagnosis based on immune cell profiling. Therefore, we aimed to develop an effective detection method based on peripheral blood mononuclear cells (PBMC) to improve the diagnosis of colorectal cancer. Analysis of the genome-wide methylation landscape of PBMCs from patients with colorectal cancer and healthy controls by microarray, pyrosequencing, and targeted bisulfite sequencing revealed five DNA methylation markers for colorectal cancer diagnosis, especially early-stage colorectal cancer. A single-tube multiple methylation-specific quantitative PCR assay (multi-msqPCR) for simultaneous detection of five methylation markers was established, which allowed quantitative analysis of samples with as little as 0.1% PBMC DNA and had better discriminative performance than single-molecule detection. Then, a colorectal cancer diagnostic model (CDM) based on methylation markers and the multi-msqPCR method was constructed that achieved high accuracy for early-stage colorectal cancer (AUC = 0.91; sensitivity = 81.18%; specificity = 89.39%), which was improved compared with CEA (AUC = 0.79). The CDM also enabled a high degree of discrimination for advanced adenoma cases (AUC = 0.85; sensitivity = 63.04%). Follow-up data also demonstrated that the CDM could identify colorectal cancer potential up to 2 years before currently used diagnostic methods. In conclusion, the approach constructed in this study based on PBMC-derived DNA methylation markers and a multi-msqPCR method is a promising and easily implementable diagnostic method for early-stage colorectal cancer. SIGNIFICANCE: Development of a diagnostic model for early colorectal cancer based on epigenetic analysis of PBMCs supports the utility of altered DNA methylation in immune cells for cancer diagnosis.

A multiplex blood-based assay targeting DNA methylation in PBMCs enables early detection of breast cancer.

The immune system can monitor tumor development, and DNA methylation is involved in the body's immune response to tumors. In this work, we investigate whether DNA methylation alterations in peripheral blood mononuclear cells (PBMCs) could be used as markers for early detection of breast cancer (BC) from the perspective of tumor immune alterations. We identify four BC-specific methylation markers by combining Infinium 850 K BeadChips, pyrosequencing and targeted bisulfite sequencing. Based on the four methylation markers in PBMCs of BC, we develop an efficient and convenient multiplex methylation-specific quantitative PCR assay for the detection of BC and validate its diagnostic performance in a multicenter cohort. This assay was able to distinguish early-stage BC patients from normal controls, with an AUC of 0.940, sensitivity of 93.2%, and specificity of 90.4%. More importantly, this assay outperformed existing clinical diagnostic methods, especially in the detection of early-stage and minimal tumors.

Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, which seriously threatens people's physical and mental health. Coagulation is closely related to the occurrence and development of HCC. Whether coagulation-related genes (CRGs) can be used as prognostic markers for HCC remains to be investigated. METHODS: Firstly, we identified differentially expressed coagulation-related genes of HCC and control samples in the datasets GSE54236, GSE102079, TCGA-LIHC, and Genecards database. Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset. The predictive capability of the CRRS model was evaluated by Kaplan-Meier survival analysis and ROC analysis. External validation was performed in the ICGC-LIRI-JP dataset. Besides, combining risk score and age, gender, grade, and stage, a nomogram was constructed to quantify the survival probability. We further analyzed the correlation between risk score and functional enrichment, pathway, and tumor immune microenvironment. RESULTS: We identified 5 key CRGs (FLVCR1, CENPE, LCAT, CYP2C9, and NQO1) and constructed the CRRS prognostic model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group. The AUC values for 1 -, 3 -, and 5-year OS in the TCGA dataset were 0.769, 0.691, and 0.674, respectively. The Cox analysis showed that CRRS was an independent prognostic factor for HCC. A nomogram established with risk score, age, gender, grade, and stage, has a better prognostic value for HCC patients. In the high-risk group, CD4+T cells memory resting, NK cells activated, and B cells naive were significantly lower. The expression levels of immune checkpoint genes in the high-risk group were generally higher than that in the low-risk group. CONCLUSIONS: The CRRS model has reliable predictive value for the prognosis of HCC patients.

Lipoxin A4 attenuates MSU-crystal-induced NLRP3 inflammasome activation through suppressing Nrf2 thereby increasing TXNRD2.

Gout is a common inflammatory disease. The activation of NLRP3 inflammasome induced by monosodium urate (MSU) crystals has a critical role in gout, and its prevention is beneficial for patients. Lipoxin A4 (LXA4) is an endogenous lipoxygenase-derived eicosanoid mediator with powerful anti-inflammatory properties. However, whether LXA4 can suppress NLRP3 inflammasome activation induced by MSU crystals remains unclear. This study aimed to investigate the protective effect of LXA4 on MSU-crystal-induced NLRP3 inflammasome activation and its underlying molecular mechanisms. We found that LXA4 inhibited MSU-crystal-induced NLRP3 inflammasome activation, interleukin (IL)-1ß maturation, and pyroptosis. More specifically, LXA4 suppressed the assembly of the NLRP3 inflammasome, including oligomerization and speck formation of ASC, and ASC-NLRP3 interaction. Furthermore, LXA4 suppressed oxidative stress, the upstream events for NLRP3 inflammasome activation, as evidenced by the fact that LXA4 eliminated total reactive oxygen species (ROS) generation and alleviated nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activation and mitochondrial dysfunction. However, LXA4 also depressed the Nrf2 activation, a critical molecule in the antioxidant pathway, and then exerted an inhibitory impact on Klf9 expression and promotional impact on TXNRD2 expression, two molecules located downstream of Nrf2 in sequence. Knockdown of TXNRD2 reversed the LXA4-induced depression of ROS and NLRP3 inflammasome. Moreover, LXA4 alleviated joint inflammation and decreased the production of cleaved caspase-1 and matured IL-1ß in gouty arthritis rats. Taken together, our findings demonstrate that LXA4 can attenuate MSU-crystal-induced NLRP3 inflammasome activation, probably through suppressing Nrf2 activation to increase TXNRD2 expression. The present study highlights the potential of LXA4 as an attractive new gout treatment candidate.

Retraction: The lnc-SNHG1 Gene Is Overexpressed and Correlates with Increased White Blood Cell Count, Poor Induction Treatment Response, and Poor Survival Profile in Adult Acute Myeloid Leukemia

Objective: Long noncoding RNA small nucleolar RNA host gene 1 (lnc-SNHG1) is involved in leukemogenesis via mediating multiple pathways. The current study aimed to further explore its clinical roles in disease risk, clinical features, and prognosis in patients with acute myeloid leukemia (AML). Materials and Methods: A total of 161 adult AML patients, 50 patients as a disease control (DC) group, and 50 healthy individuals as a healthy control (HC) group were enrolled and bone marrow mononuclear cells were collected. Subsequently, reverse transcriptionquantitative polymerase chain reaction (RT-qPCR) was performed to measure lnc-SNHG1 expression. Results: Lnc-SNHG1 expression was higher in AML patients than in the DC and HC groups (both p<0.001), with good value in distinguishing AML patients from DC and HC individuals (area under the curve of 0.726 and 0.884, respectively). Moreover, lnc-SNHG1 expression was positively associated with white blood cell (WBC) count (p=0.008) but was not correlated with other clinical features such as cytogenetics, molecular genetics, and risk stratification (all p>0.05). Lnc-SNHG1 expression was also associated with a lower complete remission (CR) rate (p=0.001). Patients with lnc-SNHG1 expression in the fourth quantile had the worst CR rates compared to patients with lnc-SNHG1 expressions in the first, second, and third quantiles (all p<0.05). Furthermore, lnc-SNHG1 expression was correlated with unsatisfactory event-free survival (p<0.001) and overall survival (p=0.002), which were worst in patients with lnc-SNHG1 expression in the fourth quantile compared to patients with lnc-SNHG1 expressions in the first, second, and third quantiles (all p<0.05). Conclusion: Lnc-SNHG1 overexpression is associated with elevated WBC count, poor induction treatment response, and poor survival profile in cases of AML and it may serve as a potential indicator for AML.

BOP1 Used as a Novel Prognostic Marker and Correlated with Tumor Microenvironment in Pan-Cancer.

Previous studies have indicated the important role of block of proliferation 1 (BOP1) in the progression of several malignant tumors; no comprehensive pan-cancer analysis of BOP1 has been performed. Here, we aim to systematically identify the expression, prognostic value, and potential immunological functions of BOP1 in 33 malignancies. We obtained the gene expression data and clinical information from multiple public databases to assess the expression level and prognostic value of BOP1 in 33 cancers. We also analyzed the relationship between BOP1 expression and DNA methylation, tumor microenvironment (TME), microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints. Moreover, we conducted gene set enrichment analysis (GSEA) to investigate the biological function and signal transduction pathways of BOP1 in different types of tumors. Finally, we validated the expression of BOP1 in lung cancer cell line and detected the influence of BOP1 on lung cancer cell migration and the expression of epithelial-mesenchymal transition- (EMT-) related genes. Collectively, our findings elucidated that BOP1 has the potential to be a promising molecular prognostic biomarker for predicting poor survival in various malignant tumors, as well as a cancer-promoting gene involved in tumorigenesis and tumor immunity.

Correlation analysis of long non-coding RNA TUG1 with disease risk, clinical characteristics, treatment response, and survival profiles of adult Ph- Acute lymphoblastic leukemia.

BACKGROUND: Long non-coding RNA taurine-upregulated gene 1 (lncRNA TUG1) is reported to be involved in the progression and development of several malignancies; however, its role in Philadelphia chromosome-negative acute lymphoblastic leukemia (Ph- ALL) is unknown. The present study aimed to explore the correlation of lncRNA TUG1 with disease risk, disease condition, and prognosis of adult Ph- ALL. METHODS: Total 101 adult Ph- ALL patients and 40 bone marrow (BM) donors were included, followed by detection of BM monocyte cell lncRNA TUG1 expression by reverse transcription-quantitative polymerase chain reaction. According to the quantiles of lncRNA TUG1 expression in Ph- ALL patients, these patients were divided into four tiers: tier 1 (ranked in 0%~25%), tier 2 (ranked in 25%~50%), tier 3 (ranked in 50%~75%), and tier 4 (ranked in 75%~100%). RESULTS: LncRNA TUG1 was upregulated in Ph- ALL patients compared with healthy donors. Further analysis indicated that in Ph- ALL patients, higher lncRNA TUG1 tier was correlated with the presence of central nervous system leukemia, increased white blood cell level, and bone marrow blasts. Furthermore, higher lncRNA TUG1 tier was negatively associated with complete remission (CR) within 4 weeks, total CR, and allogeneic hematopoietic stem cell transplant achievement. In addition, higher lncRNA TUG1 tier was associated with decreased disease-free survival and overall survival, which was further verified to be an independent factor by Cox's regression analysis. CONCLUSION: lncRNA TUG1 presents potential to be a novel biomarker for disease risk assessment and survival surveillance in Ph- ALL management.

The regulatory roles and mechanisms of the transcription factor FOXF2 in human diseases.

Many studies have focused on the relationship between transcription factors and a variety of common pathological conditions, such as diabetes, stroke, and cancer. It has been found that abnormal transcription factor regulation can lead to aberrant expression of downstream genes, which contributes to the occurrence and development of many diseases. The forkhead box (FOX) transcription factor family is encoded by the FOX gene, which mediates gene transcription and follow-up functions during physiological and pathological processes. FOXF2, a member of the FOX transcription family, is expressed in various organs and tissues while maintaining their normal structural and functional development during the embryonic and adult stages. Multiple regulatory pathways that regulate FOXF2 may also be controlled by FOXF2. Abnormal FOXF2 expression induced by uncontrollable regulatory signals mediate the progression of human diseases by interfering with the cell cycle, proliferation, differentiation, invasion, and metastasis. FOXF2 manipulates downstream pathways and targets as both a pro-oncogenic and anti-oncogenic factor across different types of cancer, suggesting it may be a new potential clinical marker or therapeutic target for cancer. However, FOXF2's biological functions and specific roles in cancer development remain unclear. In this study, we provide an overview of FOXF2's structure, function, and regulatory mechanisms in the physiological and pathological conditions of human body. We also discussed the possible reasons why FOXF2 performs the opposite function in the same types of cancer.

Prognostic and Predictive Value of Cadherin 11 for Patients with Gastric Cancer and Its Correlation with Tumor Microenvironment: Results from Microarray Analysis.

Gastric cancer is a disease characterized by inflammation, and epithelial-to-mesenchymal transition (EMT) and tumor-associated macrophages (TAMs) both play a vital role in epithelial-driven malignancy. In the present study, we performed an integrated bioinformatics analysis of transcriptome data from multiple databases of gastric cancer patients and worked on a biomarker for evaluating tumor prognosis. We found that cadherin 11 (CDH11) is highly expressed not only in gastric cancer tissues but also in EMT molecular subtypes and metastatic patients. Also, we obtained evidence that CDH11 has a significant correlation with infiltrating immune cells in the tumor microenvironment (TME). Our findings reflected that CDH11 likely plays an important role in tumor immune escape and could provide a prognostic biomarker and potential therapeutic target for patients with gastric cancer.

Clinical Application Values of Neutrophil to Lymphocyte and Platelet to Lymphocyte Ratios in Multiple Cancers.

BACKGROUND: As the mechanism of systemic inflammatory response in the course of cancer progression is gradually revealed, research has begun to focus on the two indictors of neutrophil to lymphocyte ratio (NLR) and the platelet to lymphocyte ratio (PLR) which may be associated with clinical disease development, treatment, and prognosis in patients who are undergoing surgery, chemotherapy, targeted therapy, and immunotherapy. We aim to define the clinical application values of those two biomarkers in multiple cancers. METHODS: PubMed and Web of Science are used to perform the systematic literature research. Related articles and references were identified for analyzing the association of between NLR and PLR with treatment outcome, as well as progression of cancers. RESULTS: NLR and PLR are convenient, easy to calculate, economical, and practical biomarkers, effectively predicting treatment outcome and risk of death based on inflammatory cells. Elevated NLR and PLR are significantly in line with worse clinical pathological characteristics, deeper invasiveness, more lymph node metastasis and advanced TNM stage. A significant association was observed that high NLR and PLR predict poor overall survival and disease-free survival. CONCLUSIONS: NLR and PLR can be used as available biomarkers in prognostic survival and formulation of treatment strategy of multiple cancers.

Integrated Analysis of the Functions and Prognostic Values of RNA Binding Proteins in Lung Squamous Cell Carcinoma.

Lung cancer is the leading cause of cancer-related deaths worldwide. Dysregulation of RNA binding proteins (RBPs) has been found in a variety of cancers and is related to oncogenesis and progression. However, the functions of RBPs in lung squamous cell carcinoma (LUSC) remain unclear. In this study, we obtained gene expression data and corresponding clinical information for LUSC from The Cancer Genome Atlas (TCGA) database, identified aberrantly expressed RBPs between tumors and normal tissue, and conducted a series of bioinformatics analyses to explore the expression and prognostic value of these RBPs. A total of 300 aberrantly expressed RBPs were obtained, comprising 59 downregulated and 241 upregulated RBPs. Functional enrichment analysis indicated that the differentially expressed RBPs were mainly associated with mRNA metabolic processes, RNA processing, RNA modification, regulation of translation, the TGF-beta signaling pathway, and the Toll-like receptor signaling pathway. Nine RBP genes (A1CF, EIF2B5, LSM1, LSM7, MBNL2, RSRC1, TRMU, TTF2, and ZCCHC5) were identified as prognosis-associated hub genes by univariate, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier survival, and multivariate Cox regression analyses, and were used to construct the prognostic model. Further analysis demonstrated that high risk scores for patients were significantly related to poor overall survival according to the model. The area under the time-dependent receiver operator characteristic curve of the prognostic model was 0.712 at 3 years and 0.696 at 5 years. We also developed a nomogram based on nine RBP genes, with internal validation in the TCGA cohort, which showed a favorable predictive efficacy for prognosis in LUSC. Our results provide novel insights into the pathogenesis of LUSC. The nine-RBP gene signature showed predictive value for LUSC prognosis, with potential applications in clinical decision-making and individualized treatment.

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma.

Lung cancer is the top cause of carcinoma-associated deaths worldwide. RNA binding proteins (RBPs) dysregulation has been reported in various malignant tumors, and that dysregulation is closely associated with tumorigenesis and tumor progression. However, little is known about the roles of RBPs in lung adenocarcinoma (LUAD). In this study, we downloaded the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) database and determined the differently expressed RBPs between normal and cancer tissues. We then performed an integrative analysis to explore the expression and prognostic significance of these RBPs. A total of 164 differently expressed RBPs were identified, including 40 down-regulated and 124 up-regulated RBPs. Pathway and Gene ontology (GO) analysis indicated that the differently expressed RBPs were mainly related to RNA processing, RNA metabolic process, RNA degradation, RNA transport, splicing, localization, regulation of translation, RNA binding, TGF-beta signaling pathway, mRNA surveillance pathway, and aminoacyl-tRNA biosynthesis. Survival analysis revealed that the high expression of BOP1 or GNL3 or WDR12 or DCAF13 or IGF2BP3 or IGF2BP1 were associated with poor overall survival (OS). Conversely, overexpression of KHDRBS2/SMAD predicted high OS in these patients. ROC curve analysis showed that the eight hub genes with a better diagnostic accuracy to distinguish lung adenocarcinoma. The results provided novel insights into the pathogenesis of LUAD and the development of treatment targets and prognostic molecular markers.

Development and validation of a RNA binding protein-associated prognostic model for lung adenocarcinoma.

RNA binding proteins (RBPs) dysregulation have been reported in various malignant tumors and associated with the occurrence and development of cancer. However, the role of RBPs in lung adenocarcinoma (LUAD) is poorly understood. We downloaded the RNA sequencing data of LUAD from the Cancer Genome Atlas (TCGA) database and determined the differently expressed RBPs between normal and cancer tissues. The study then systemically investigated the expression and prognostic value of these RBPs by a series of bioinformatics analysis. A total of 223 differently expressed RBPs were identified, including 101 up-regulated and 122 down-regulated RBPs. Eight RBPs (IGF2BP1, IFIT1B, PABPC1, TLR8, GAPDH, PIWIL4, RNPC3, and ZC3H12C) were identified as prognosis related hub gene and used to construct a prognostic model. Further analysis indicated that the patients in the high-risk subgroup had poor overall survival(OS) compared to those in low-risk subgroup based on the model. The area under the curve of the time-dependent receiver operator characteristic curve of the prognostic model are 0.775 in TCGA cohort and 0.814 in GSE31210 cohort, confirming a good prognostic model. We also established a nomogram based on eight RBPs mRNA and internal validation in the TCGA cohort, which displayed a favorable discriminating ability for lung adenocarcinoma.

CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis.

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. The abnormal expression of genes is significantly related to the occurrence of HCC. The aim of this study was to explore the differentially expressed genes (DEGs) of HCC and to provide bioinformatics basis for the occurrence, prevention and treatment of HCC. The DEGs of HCC and normal tissues in GSE102079, GSE121248, GSE84402 and GSE60502 were obtained using R language. The GO function analysis and KEGG pathway enrichment analysis of DEGs were carried out using the DAVID database. Then, the protein-protein interaction (PPI) network was constructed using the STRING database. Hub genes were screened using Cytoscape software and verified using the GEPIA, UALCAN, and Oncomine database. We used HPA database to exhibit the differences in protein level of hub genes and used LinkedOmics to reveal the relationship between candidate genes and tumor clinical features. Finally, we obtained transcription factor (TF) of hub genes using NetworkAnalyst online tool. A total of 591 overlapping up-regulated genes were identified. These genes were related to cell cycle, DNA replication, pyrimidine metabolism, and p53 signaling pathway. Additionally, the GEPIA database showed that the CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 were associated with the poor survival of HCC patients. UALCAN, Oncomine, and HPA databases and qRT-PCR confirmed that these genes were highly expressed in HCC tissues. LinkedOmics database indicated these genes were correlated with overall survival, pathologic stage, pathology T stage, race, and the age of onset. TF analysis showed that MYBL2, KDM5B, MYC, SOX2, and E2F4 were regulators to these nine hub genes. Overexpression of CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 in tumor tissues predicted poor survival in HCC. They may be potential therapeutic targets for HCC.

Exploring the Pathological Mechanism of Bladder Cancer Based on Tumor Mutational Burden Analysis.

Although immunotherapy has progressed in the treatment of bladder cancer, some patients still have poor prognosis. New therapeutic targets are eager to be discovered to improve the outcomes of bladder cancer. With the development of high-throughput sequencing and tumor profiling, potential tumor biomarkers were identified. Through the interpretation of related data from the Cancer Genome Atlas database (TCGA), some key genes have been discovered to drive the development and prognosis of urinary bladder neoplasm. On account of the success of immunotherapy in many cancer types, we established the relationship between tumor mutation burden and immune microenvironment of bladder cancer and found the changes of several immune cells in this disease. Based on the understanding of the bladder tumor genome and immune environment, this study is supposed to provide new therapies for the treatment of bladder neoplasm.

[Bioinformatics analysis of COL1A1 regulated by miR-129-5p as a potential therapeutic target for gastric cancer].

OBJECTIVE: To explore the pathogenesis of gastric cancer through a bioinformatic approach to provide evidence for the prevention and treatment of gastric cancer. METHODS: The differentially expressed genes (DEGs) in gastric cancer and normal gastric mucosa in GSE79973 dataset were analyzed using GEO2R online tool. GO analysis and KEGG pathway enrichment analysis of the DEGs in DAVID database were performed. The protein interaction network was constructed using STRING database, and the key genes (Hub genes) were screened and their functional modules were analyzed using Cytoscape software. The GEPIA database was used to validate the Hub genes, and the Target Scan database was used to predict the microRNAs that regulate the target genes; OncomiR was used to analyze the expressions of the microRNAs in gastric cancer tissues and their relationship with the survival outcomes of the patients. RESULTS: A total of 181 DEGs were identified in gastric cancer, and 10 hub genes were screened by the protein- protein interaction network. Functional analysis showed that these DEGs were involved mainly in protein digestion and absorption, PI3K-Akt signaling pathway, ECM-receptor interaction and platelet activation signal pathway. GEPIA database validation showed that COL1A1 was highly expressed in gastric cancer tissues and was associated with a poor prognosis of patients with gastric cancer. MiR-129-5p was found to bind to the 3'UTR of COL1A1 mRNA, and compared with that in normal tissues, miR-129-5p expression was obviously down-regulated in gastric cancer tissues, and was correlated with the prognosis of the patients. CONCLUSIONS: COL1A1 under regulation by MiR-129-5p is a potential therapeutic target for gastric cancer.