Enhanced expression of KIF4A in colorectal cancer is associated with lymph node metastasis

Unveiling the impact of glutathione (GSH) and p53 gene deletion on tumor cell metabolism by amino acid and proteomics analysis

Background

Tumor cell inhibition is a pivotal focus in anti-cancer research, and extensive investigations have been conducted regarding the role of p53. Numerous studies have highlighted its close association with reactive oxygen species (ROS). However, the precise impact of the antioxidant glutathione (GSH) in this context remains inadequately elucidated. Here, we will elucidate the anti-cancer mechanisms mediated by p53 following treatment with GSH.

Methods

In this study, we employed a p53 gene knockout approach in SW480 colorectal cells and conducted comprehensive analyses of 20 amino acids and proteomics using liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS).

Results

These analyses unveiled profound alterations in amino acids and proteins triggered by GSH treatment, shedding light on novel phenomena and delineating the intricate interplay between GSH and cellular proteins. The deletion of the p53 gene exerts a profound influence on tumor cell proliferation. Moreover, tumor cell proliferation is significantly affected by elevated GSH levels. Importantly, in the absence of the p53 gene, cells exhibit heightened sensitivity to GSH, leading to inhibited cell growth. The combined therapeutic approach involving GSH and p53 gene deletion expedites the demise of tumor cells. It is noteworthy that this treatment leads to a marked decline in amino acid metabolism, particularly affecting the down-regulation of methionine (Met) and phenylalanine (Phe) amino acids. Among the 41 proteins displaying significant changes, 8 exhibit consistent alterations, with 5 experiencing decreased levels and 3 demonstrating increased quantities. These proteins primarily participate in crucial cellular metabolic processes and immune functions.

Conclusions

In conclusion, the concurrent administration of GSH treatment and p53 gene deletion triggers substantial modifications in the amino acid and protein metabolism of tumor cells, primarily characterized by down-regulation. This, in turn, compromises cell metabolic activity and immune function, ultimately culminating in the demise of tumor cells. These newfound insights hold promising implications and could pave the way for the development of straightforward and efficacious anti-cancer treatments.

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning-based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.

Integrated analysis of intestinal microbiota and host gene expression in colorectal cancer patients

Introduction. Colorectal cancer (CRC) is one of the most common cancers and poses heavy burden on global health. The relationship between mucosal microbiome composition and colorectal gene expression are rarely studied. In this study, we integrated transcriptome data with microbiome data to investigate the relationship between them in colorectal cancer patients.Gap statement. Previous studies have identified the contribution of gut microbiota and DEGs to the pathogenesis of CRC, but the relationship between mucosal microbiome composition and colorectal gene expression are rarely studied.Aim. In this study, we integrated transcriptome data with microbiome data to investigate the relationship between mucosal microbiome composition and colorectal gene expression.Methodology. First, three independent CRC gene expression profiles (GSE184093, GSE156355 and GSE146587) from Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs). Second, another dataset (GSE163366) was used to analyse gut mucosal microbiome differential abundance. GO (Gene Ontology) function and KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway enrichment analyses of the DEGs were performed. Protein-protein interactions (PPIs) of the DEGs were constructed. The Spearman correlation analysis was computed between host DEGs and gut microbiome abundance data.Results. A total of 1036 upregulated DEGs and 1194 downregulated DEGs between noncancerous tissues and cancerous tissues were identified based on the analysis. One significant module with a score 37.65 was selected out via MCODE including 41 upregulated DEGs, which are were mostly enriched in two pathways, including microtubule binding and tubulin binding. In particular, significant negative correlations are prevalent between Fusobacterium and the 41 DEGs with the correlation ranging between -0.54 and -0.35, and there commonly exist significant positive correlations between Blautia and the 41 DEGs with the correlation ranging between 0.42 and 0.54, indicating that Fusobacterium and Blautia are two of the most important microbes interacting with the gene regulation.Conclusion. Our results demonstrate significant correlation between some gut microbes and DEGs, providing a comprehensive bioinformatics analysis of them for future investigation into the molecular mechanisms and biomarkers.

A Novel Five-Gene Signature Related to Clinical Outcome and Immune Microenvironment in Breast Cancer

Breast cancer (BC) is the most frequent cancer in women and the main cause of cancer-related deaths in the globe, according to the World Health Organization. The need for biomarkers that can help predict survival or guide treatment decisions in BC patients is critical in order to provide each patient with an individualized treatment plan due to the wide range of prognoses and therapeutic responses. A reliable prognostic model is essential for determining the best course of treatment for patients. Patients’ clinical and pathological data, as well as their mRNA expression levels at level 3, were gleaned from the TCGA databases. Differentially expressed genes (DEGs) between BC and non-tumor specimens were identified. Tumor immunity analyses have been utilized in order to decipher molecular pathways and their relationship to the immune system. The expressions of KIF4A in BC cells were determined by RT-PCR. To evaluate the involvement of KIF4A in BC cell proliferation, CCK-8 tests were used. In this study, utilizing FC > 4 and p < 0.05, we identified 140 upregulated genes and 513 down-regulated genes. A five-gene signature comprising SFRP1, SAA1, RBP4, KIF4A and COL11A1 was developed for the prediction of overall survivals of BC. Overall survival was distinctly worse for patients in the high-risk group than those in the low-risk group. Cancerous and aggressiveness-related pathways and decreased B cell, T cell CD4⁺, T cell CD8⁺, Neutrophil and Myeloid dendritic cells levels were seen in the high-risk group. In addition, we found that KIF4A was highly expressed in BC and its silence resulted in the suppression of the proliferation of BC cells. Taken together, as a possible prognostic factor for BC, the five-gene profile created and verified in this investigation could guide the immunotherapy selection.

Promising novel biomarkers and candidate small-molecule drugs for lung adenocarcinoma: Evidence from bioinformatics analysis of high-throughput data

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer associated with an unstable prognosis. Thus, there is an urgent demand for the identification of novel diagnostic and prognostic biomarkers as well as targeted drugs for LUAD. The present study aimed to identify potential new biomarkers associated with the pathogenesis and prognosis of LUAD. Three microarray datasets (GSE10072, GSE31210, and GSE40791) from the Gene Expression Omnibus database were integrated to identify the differentially expressed genes (DEGs) in normal and LUAD samples using the limma package. Bioinformatics tools were used to perform functional and signaling pathway enrichment analyses for the DEGs. The expression and prognostic values of the hub genes were further evaluated by Gene Expression Profiling Interactive Analysis and real-time quantitative polymerase chain reaction. Furthermore, we mined the “Connectivity Map” (CMap) to explore candidate small molecules that can reverse the tumoral of LUAD based on the DEGs. A total of 505 DEGs were identified, which included 337 downregulated and 168 upregulated genes. The PPI network was established with 1,860 interactions and 373 nodes. The most significant pathway and functional enrichment associated with the genes were cell adhesion and extracellular matrix-receptor interaction, respectively. Seven DEGs with high connectivity degrees (ZWINT, RRM2, NDC80, KIF4A, CEP55, CENPU, and CENPF) that were significantly associated with worse survival were chosen as hub genes. Lastly, top 20 most important small molecules which reverses the LUAD gene expressions were identified. The findings contribute to revealing the molecular mechanisms of the initiation and progression of LUAD and provide new insights for integrating multiple biomarkers in clinical practice.

Enhanced expression of KIF4A in osteosarcoma predicts a poor prognosis and facilitates tumor growth by activation of the MAPK pathway

The present study aimed to explore the prognostic value and role of kinesin family member 4A (KIF4A) expression in human osteosarcoma. KIF4A expression was evaluated in human osteosarcoma tissues from The Cancer Genome Atlas and Gene Expression Omnibus datasets. Reverse transcription-quantitative PCR was then applied to assess KIF4A level in both osteosarcoma cell lines and tissues. The association between KIF4A expression and clinical results in patients with osteosarcoma was detected by survival analysis. MTT assays and colony formation assays were used to evaluate the effects of KIF4A on osteosarcoma cell proliferation. The results indicated that the level of KIF4A was increased and associated with a poor prognosis in osteosarcoma tissues. Knockdown of KIF4A was shown to inhibit osteosarcoma cellular proliferation by affecting the MAPK pathway. The level of KIF4A was high in the human osteosarcoma tissues and this could be considered as a tumor induction gene, which may be used as an indicator of prognosis.

KIF4A promotes the development of bladder cancer by transcriptionally activating the expression of CDCA3

Bladder cancer (BC) is among the most common urinary system tumors with a high morbidity and mortality worldwide. Despite advancements being made in the diagnosis and treatment of bladder cancer, targeted therapy remains the most promising treatment, and novel therapeutic targets are urgently required in to improve the outcomes of patients with BC. Kinesin family member 4A (KIF4A) is a plus-end directed motor protein involved in the regulation of multiple cellular processes, such as mitosis and axon growth. Notably, KIF4A plays important roles in tumor growth and progression, and its expression is associated with the prognosis of several types of cancer. However, the potential role and molecular mechanisms of KIF4A in bladder cancer development remain unclear. The present study demonstrated that KIF4A was highly expressed in human BC tissues, and its expression was associated with patient clinicopathological characteristics, such as tumor stage (P=0.012) and with the prognosis of patients with BC. It was further found that KIF4A promoted the cell proliferation of bladder cancer both in vitro and in vivo. On the whole, the data presented herein provide evidence that KIF4A promotes the development of BC through the transcriptional activation of the expression of CDCA3. The present study indicates the involvement of KIF4A in the progression of BC and suggests that KIF4A may be a promising therapeutic target for the treatment of BC.

High expression of TREM2 promotes EMT via the PI3K/AKT pathway in gastric cancer: bioinformatics analysis and experimental verification

Background: To date, the pathogenesis of gastric cancer (GC) remains unclear. We combined public database resources and bioinformatics analysis methods, explored some novel genes and verified the experiments to further understand the pathogenesis of GC and to provide a promising target for anti-tumor therapy. Methods: We downloaded the chip data related to GC from the Gene Expression Omnibus (GEO) database, extracted differentially expressed genes (DEGs), and then determined the key genes in the development of GC via PPI networks and model analysis. Functional annotation via GO and KEGG enrichment of DEGs was used to understand the latent roles of DEGs. The expression of the triggering receptor expressed on myeloid cells 2 (TREM2) gene in GC cell lines was verified via RT-PCR and western blotting. Moreover, the CCK-8, wound healing assay, and transwell migration and invasion assays were used to understand the changes in the proliferation, migration, and invasion abilities of GC cells after silencing TREM2. Western blotting verified the interaction between TREM2 and PI3K predict of the string website, as well as the effect of TREM2 on EMT. Finally, a lung metastasis model was used to explore the relationship between TREM2 and metastasis. Results: Our study identified 16 key genes, namely BGN, COL1A1, COL4A1, COL5A2, NOX4, SPARC, HEYL, SPP1, TIMP1, CTHRC1, TREM2, SFRP4, FBXO32, GPX3, KIF4A, and MMP9 genes associated with GC. The EMT-related pathway was the most significantly altered pathway. TREM2 expression was higher in GC cell lines and was remarkably associated with tumor invasion depth, TNM stage, histological grade, histological type, anatomic subdivision, and Helicobacter pylori state. Knockdown of TREM2 expression inhibited the proliferation, migration, and invasion of GC cells as well as the progression of EMT by PI3K/AKT signaling in vitro. In addition, lung metastasis were decreased in vivo. Conclusions: We identified some important genes associated with the progression of GC via public database analysis, explored and verified the effects of proto-oncogene TREM2 on EMT via the PI3K/AKT pathway. TREM2 may be a novel target in the GC therapy.

KIF4A Promotes Clear Cell Renal Cell Carcinoma (ccRCC) Proliferation in vitro and in vivo

PURPOSE

To evaluate the expression in human clear cell renal cell carcinoma (ccRCC) tissues and explore the effects of kinesin family member 4A (KIF4A) on ccRCC progression.

METHODS

GEPIA was used to evaluate the mRNA levels of KIF4A in human ccRCC tissues from TCGA database, and Immunohistochemistry (IHC) assays were performed to assess its expression in human ccRCC tissues collected in our hospital. The clinical-pathological analysis was performed to explore the correlation with KIF4A expression. The effects of KIF4A on ccRCC cell proliferation were detected through colony formation and MTT assays. Finally, the effects of KIF4A on tumor growth were measured using a mice model.

RESULTS

Bioinformation results showed the expression of KIF4A mRNA was upregulated in ccRCC tissues and high expression of KIF4A was related with poor prognosis in ccRCC patients. We also found a high expression of KIF4A in human ccRCC tissues collected in our hospital. We also found its expression level was correlated with clinical characteristics, including T stage (P=0.035*) and lymphatic metastasis (P=0.028*). We further confirmed that knockdown of KIF4A suppressed cell proliferation in HTB-47 and CRL-1932 cells. Furthermore, KIF4A contributes to tumor growth of ccRCC cells in mice.

CONCLUSION

We found the abnormal high expression of KIF4A in human ccRCC tissues and demonstrated that KIF4A could serve as a tumor induction gene.

Integrated bioinformatics analysis of the association between apolipoprotein E expression and patient prognosis in papillary thyroid carcinoma

The prognosis of most patients with papillary thyroid carcinoma (PTC) is excellent despite some cases of tumor progression or relapse. The present study was designed to reveal possible prognostic risk indicators for PTC. Differentially expressed genes (DEGs) extracted from 4 Gene Expression Omnibus (GEO) cohorts were subjected to functional enrichment analyses by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. A dataset from The Cancer Genome Atlas (TCGA) was obtained to filter and validate significant genes using cytoHubba, followed by analysis of their association with clinicopathological characteristics and prognosis. In total, 240 DEGs were identified after data preprocessing. These DEGs were enriched in ‘intracellular redox equilibrium’, ‘release of exosome’, ‘cell adhesion’, ‘regulation of extracellular matrix’, ‘collagen binding’ and ‘energy metabolism’ based on GO analysis which including cellular component, molecular function and biological process. KEGG pathway analysis revealed that the DEGs were enriched in thyroid hormone synthesis, pathways in cancer, focal adhesion, metabolic pathways, apoptosis, PPAR signaling pathway and PI3K/AKT signaling pathway. Using cytoHubba, the following hub genes were identified: Apolipoprotein E (APOE); hemoglobin subunit α1 (HBA1); angiotensin II receptor 1 (AGTR1); collagen I α1 (COL1A1); galectin 3 (LGALS3) and TIMP metallopeptidase inhibitor 1 (TIMP1). The expression of these genes was found to be consistent in TCGA datasets. Kaplan-Meier analysis revealed that APOE was significantly associated with overall survival (P=0.00067) and disease free survival (P=0.00220). Additionally, low expression of APOE was significantly associated with older age (P<0.001) and higher TNM stage (P<0.001) compared with the high expression group. Therefore, APOE may function as a predictive risk indicator for progression as well as prognosis of PTC.

Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

Identification of KIF11 As a Novel Target in Meningioma

Kinesins play an important role in many physiological functions including intracellular vesicle transport and mitosis. The emerging role of kinesins in different cancers led us to investigate the expression and functional role of kinesins in meningioma. Therefore, we re-analyzed our previous microarray dataset of benign, atypical, and anaplastic meningiomas (n = 62) and got evidence for differential expression of five kinesins (KIFC1, KIF4A, KIF11, KIF14 and KIF20A). Further validation in an extended study sample (n = 208) revealed a significant upregulation of these genes in WHO°I to °III meningiomas (WHO°I n = 61, WHO°II n = 88, and WHO°III n = 59), which was most pronounced in clinically more aggressive tumors of the same WHO grade. Immunohistochemical staining confirmed a WHO grade-associated upregulated protein expression in meningioma tissues. Furthermore, high mRNA expression levels of KIFC1, KIF11, KIF14 and KIF20A were associated with shorter progression-free survival. On a functional level, knockdown of kinesins in Ben-Men-1 cells and in the newly established anaplastic meningioma cell line NCH93 resulted in a significantly inhibited tumor cell proliferation upon siRNA-mediated downregulation of KIF11 in both cell lines by up to 95% and 71%, respectively. Taken together, in this study we were able to identify the prognostic and functional role of several kinesin family members of which KIF11 exhibits the most promising properties as a novel prognostic marker and therapeutic target, which may offer new treatment options for aggressive meningiomas.

Role of kinesin superfamily in gastrointestinal cancer

Kinesins constitute a protein superfamily that belongs to motor proteins. Kinesins move along microtubules to exert their functions. They play a crucial role in intracellular transportation, mitosis, cell formation, and cell function. Kinesin are not only responsible for the transport of various membrane organelles, protein complexes, mRNA and so on to ensure the basic activity of cells, but also can regulate intracellular molecular signal pathways. Numerous studies have shown that kinesins are closely associated with the development of a variety of human diseases, especially the formation and development of gastrointestinal tumors. This article reviews the role of kinesins in gastrointestinal cancer.

Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis

Breast cancer is one of the most common malignancies. The molecular mechanisms of its pathogenesis are still to be investigated. The aim of this study was to identify the potential genes associated with the progression of breast cancer. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers. The gene expression profiles of GSE1561 were selected from the Gene Expression Omnibus (GEO) database. RNA-seq data and clinical information of breast cancer from TCGA were used for validation. A total of 18 modules were identified via the average linkage hierarchical clustering. In the significant module (R² = 0.48), 42 network hub genes were identified. Based on the Cancer Genome Atlas (TCGA) data, 5 hub genes (CCNB2, FBXO5, KIF4A, MCM10, and TPX2) were correlated with poor prognosis. Receiver operating characteristic (ROC) curve validated that the mRNA levels of these 5 genes exhibited excellent diagnostic efficiency for normal and tumor tissues. In addition, the protein levels of these 5 genes were also significantly higher in tumor tissues compared with normal tissues. Among them, CCNB2, KIF4A, and TPX2 were further upregulated in advanced tumor stage. In conclusion, 5 candidate biomarkers were identified for further basic and clinical research on breast cancer with co-expression network analysis.