CENPI is overexpressed in colorectal cancer and regulates cell migration and invasion

Identification of the hub genes linked to zearalenone-induced hepatotoxicity in broiler chickens

Zearalenone (ZEN) is a mycotoxin found in food and feed that impairs the function of multiple organs, especially the liver. However, the specific mechanisms through which ZEN induces liver damage in broiler chickens are not well understood. Therefore, this study aimed to identify the key genes linked to the hepatotoxicity induced by ZEN exposure in broiler chickens. Gene expression data from ZEN-treated and control chicken embryo primary hepatocytes (CEPHs) were used to implement differential expression analysis. Totally, 436 differentially expressed genes (DEGs) were detected, in which 223 and 213 genes were up- and down-regulated in ZEN-treated CEPHs, respectively. Gene ontology analysis suggested that these DEGs were involved in various biological processes, including chromosome segregation, mitotic cytokinesis, mitotic cell cycle, cell division, and mitotic spindle organization. Pathway analysis showed that the DEGs were associated with p53, FoxO, ubiquitin-mediated proteolysis, cell cycle, and mismatch repair signaling pathways. Furthermore, the hub genes, including BRCA1, CDC45, CDCA3, CDKN3, CENPE, CENPF, CENPI, CENPM, CENPU, and CEP55, potentially contributed to ZEN-induced hepatotoxicity. In conclusion, our study provides the valuable insight into the mechanism underlying ZEN-induced hepatotoxicity in broiler chickens.

Immune infiltration related CENPI associates with the malignant features and drug resistance of lung adenocarcinoma

Centromere protein I (CENPI) is an important member of centromeric proteins family, which is crucial to chromosome alignment and segregation. Nevertheless, the interrelation between CENPI expression and tumor progression is in the shadows. In this reserch, we carried out a panoramic bioinformatic analysis about CENPI with TCGA, Timer 2.0, Oncomine, GEPIA, Cbioportal, LinkedOmics and CancerSEA databases. Besides, our bioinformatic results have been further confirmed through in vitro experiments, including Real-Time quantitative PCR (RT-qPCR), immunofluorescence (IF), immunohistochemistry (IHC), western blotting (WB), cell proliferation assays, EdU, cell cycle and apoptosis test. Our results suggested that CENPI was increased in most of the cancers, and may serve as a potential biomarker. What's more, the knock down of CENPI inhibited the expression of CDK2 in lung adenocarcinoma (LUAD), and resulted in the arrest of G0/G1 phase and apoptosis. Besides, CENPI was related to immune cells infiltration and drug sensitivity in pan-cancer, and can act as a potential treatment target to cure cancer patients.

Stemness genes and miR-1247-3p expression associate with clinicopathological parameters and prognosis in lung adenocarcinoma

Lung cancer makes up one-fourth of all cancer-related mortality with the highest mortality rate among all cancers. Despite recent scientific advancements in cancer therapeutics, the 5-year survival rate of lung adenocarcinoma (LUAD) cancer patients remains below 15 percent. It has been suggested that the high mortality rate of LUAD is linked to the acquisition of progenitor-like cells with stem-like characteristics that assist the whole tumor in regulating immune cell infiltration. To examine this hypothesis further, this study mined several databases to explore the presence of stemness-related genes and miRNAs in LUAD cancers. We examine their association with immune and accessory cell infiltration rates and patient survival. We found 3 stem cell-related genes, ORC1L, KIF20A, and DLGAP5, present in LUAD that also correlate with changes in immune infiltration rates and reduced patient survival rates. Additionally, the modulation in myeloid-derived suppressor cell (MDSC) infiltration and miRNA hsa-mir-1247-3p mediated targeting of tumor suppressor SLC24A4 and oncogenes RAB3B and HJURP appears to primarily regulate LUAD patient survival. Given these findings, hsa-mir-1247-3p and/or its associated gene targets may offer a promising avenue to enhance patient survivability.

Exploring tumor-normal cross-talk with TranNet: Role of the environment in tumor progression

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such dependencies. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights into the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations.

Prognostic and immune infiltrative biomarkers of CENPO in pan-cancer and its relationship with lung adenocarcinoma cell proliferation and metastasis

BACKGROUND

The centromere protein O (CENPO) is an important member of the centromere protein family. However, the role of CENPO in pan-cancer and immune infiltration has not been reported. Here, we investigated the role of CENPO in pan-cancer and further validated its role in lung adenocarcinoma (LUAD) by in vitro experiments.

METHOD

The UCSC Xena database and The Cancer Genome Atlas (TCGA)-LUAD data were used to assess the expression levels of CENPO. The potential value of CENPO as a diagnostic and prognostic biomarker for pan-cancer was evaluated using TCGA data and the GEPIA database. The -expression profiles of LUAD patients and the corresponding clinical data were downloaded for correlation analysis. The role of CENPO in immune infiltration was investigated using the UCSC Xena database. Subsequently, qRT-PCR was performed to detect the expression of CENPO. Cell proliferation, migration, and invasion were determined using CCK-8, wound-healing assay, and transwell assay, respectively.

RESULTS

CENPO is highly expressed in most cancers, and the upregulation of CENPO is associated with poor prognosis in many cancers. CENPO expression correlates with age, TNM stage, N stage, T stage, and receipt of radiotherapy in LUAD patients, and LUAD patients with high CENPO expression have poorer overall survival (OS) and disease-free survival (DFS). In addition, CENPO expression is associated with immune cell infiltration and immune checkpoint inhibitors. Moreover, the expression of CENPO was closely related to the expression of tumor mutational load and microsatellite instability. In vitro experiments showed that CENPO expression was increased in LUAD cell lines and that knockdown of CENPO significantly inhibited the proliferation, cell invasion, and migration ability of LUAD cells.

CONCLUSION

CENPO may be a potential pan-cancer biomarker and oncogene, especially in LUAD. In addition, CENPO is associated with immune cell infiltration and may serve as a new molecular therapeutic target and effective prognostic marker for LUAD.

Pan-Cancer Analysis Reveals CENPI as a Potential Biomarker and Therapeutic Target in Adrenocortical Carcinoma

Background

Centromere protein I (CENPI) has been shown to affect the tumorigenesis of breast and colorectal cancers. However, its biological role and prognostic value in other kinds of cancer, especially adrenocortical carcinoma (ACC), remained to be further investigated.

Methods

Various bioinformatics tools were adopted for exploring the significance of differential expression of CENPI in several malignant tumors from databases such as Depmap portal, GTEx, and TCGA. ACC was selected for further analyzed, and information such as clinicopathological features, the prognostic outcome of diverse subgroups, differentially expressed genes (DEGs), co-expression genes, as well as levels of tumor-infiltrating immune cells (TIIC), was extracted from multiple databases. To verify the possibility of CENPI as a therapeutic target in ACC, drug sensitivity assay and si-RNA mediate knockdown of CENPI were carried out.

Results

The pan-cancer analyses showed that the CENPI mRNA expression levels differed significantly among most cancer types. Additionally, a high precision in cancer prediction and close relation with cancer survival indicated that CENPI could be a potential candidate biomarker to diagnose and predict cancer prognosis. In ACC, CENPI was closely related to multiple clinical characteristics, such as pathological stage and primary therapy outcome. High CENPI levels predicted poor overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) of ACC patients, particularly for different clinical subgroups. Moreover, the expression of CENPI showed positive relationship to Th2 cells but negatively related to most of the TIICs. Furthermore, drug sensitivity assay showed that vorinostat inhibit CENPI expression and ACC cell growth. Additionally, si-RNA mediated knockdown of CENPI inhibited ACC cell growth and invasion and showed synergistic anti-proliferation effect with AURKB inhibitor barasertib.

Conclusion

Pan-cancer analysis demonstrated that CENPI is a potential diagnostic and prognostic biomarker in various cancers as well as an anti-ACC therapeutic target.

Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such regulation. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights for the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations. The TranNet method was implemented in python, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/TranNet .

CSTF2 Acts as a Prognostic Marker Correlated with Immune Infiltration in Hepatocellular Carcinoma

Background

Cleavage stimulation factor 2 (CSTF2) encodes a nuclear protein that is implicated in the development of various cancers. However, the role of CSTF2 in hepatocellular carcinoma (HCC) has not been understood. This study aims to explore the function of CSTF2 in HCC.

Methods

The expression, diagnostic capability, prognostic value, and immune cell effect of CSTF2 in HCC were explored using various databases. The expression level of CSTF2 were validated in our cell lines. The effect of CSTF2 on hepatocarcinogenesis was explored by CSTF2 silencing.

Results

CSTF2 expression was significantly elevated in HCC and correlated with multiple clinicopathological characteristics. CSTF2 exhibited good diagnostic capability in discriminating HCC samples from nontumorous samples. High CSTF2 expression was significantly related to poor overall survival. Univariate and multivariate Cox regression analyses suggested that CSTF2 expression was an independent risk factor for HCC. These results were validated in ICGC cohorts. In addition, the nomogram based on CSTF2 showed better predictive performance than the AJCC staging system in TCGA and ICGC cohorts. Functional enrichment analysis revealed that CSTF2-related genes were involved in DNA/RNA processing and the cell cycle. In addition, we found that CSTF2 expression was closely related to the levels of various infiltrating immune cells, especially neutrophils. Moreover, some immune checkpoints had positive relationships with CSTF2 expression. CSTF2 silencing inhibited proliferation, invasion and migration, and promoted apoptosis in HepG2 cells. Western blotting analysis revealed that CSTF2 silencing inactivated the Wnt/β-catenin signaling pathway.

Conclusion

High CSTF2 expression not only correlates with unfavorable outcomes but also affects immune cell infiltration and immune checkpoint expression in HCC. CSTF2 silencing can alleviate the malignant phenotypes of hepatic cancer cell by inactivating the Wnt/β-catenin signaling pathway. These results indicate that CSTF2 can serve as a promising prognostic marker and therapeutic target for HCC patients.

Immune Cell Networks Uncover Candidate Biomarkers of Melanoma Immunotherapy Response

The therapeutic activation of antitumour immunity by immune checkpoint inhibitors (ICIs) is a significant advance in cancer medicine, not least due to the prospect of long-term remission. However, many patients are unresponsive to ICI therapy and may experience serious side effects; companion biomarkers are urgently needed to help inform ICI prescribing decisions. We present the IMMUNETS networks of gene coregulation in five key immune cell types and their application to interrogate control of nivolumab response in advanced melanoma cohorts. The results evidence a role for each of the IMMUNETS cell types in ICI response and in driving tumour clearance with independent cohorts from TCGA. As expected, ‘immune hot’ status, including T cell proliferation, correlates with response to first-line ICI therapy. Genes regulated in NK, dendritic, and B cells are the most prominent discriminators of nivolumab response in patients that had previously progressed on another ICI. Multivariate analysis controlling for tumour stage and age highlights CIITA and IKZF3 as candidate prognostic biomarkers. IMMUNETS provide a resource for network biology, enabling context-specific analysis of immune components in orthogonal datasets. Overall, our results illuminate the relationship between the tumour microenvironment and clinical trajectories, with potential implications for precision medicine.

Bacterial Involvement in Progression and Metastasis of Colorectal Neoplasia

While the gut microbiome is composed of numerous bacteria, specific bacteria within the gut may play a significant role in carcinogenesis, progression, and metastasis of colorectal carcinoma (CRC). Certain microbial species are known to be associated with specific cancers; however, the interrelationship between bacteria and metastasis is still enigmatic. Mounting evidence suggests that bacteria participate in cancer organotropism during solid tumor metastasis. A critical review of the literature was conducted to better characterize what is known about bacteria populating a distant site and whether a tumor depends upon the same microenvironment during or after metastasis. The processes of carcinogenesis, tumor growth and metastatic spread in the setting of bacterial infection were examined in detail. The literature was scrutinized to discover the role of the lymphatic and venous systems in tumor metastasis and how microbes affect these processes. Some bacteria have a potent ability to enhance epithelial–mesenchymal transition, a critical step in the metastatic cascade. Bacteria also can modify the microenvironment and the local immune profile at a metastatic site. Early targeted antibiotic therapy should be further investigated as a measure to prevent metastatic spread in the setting of bacterial infection.

Centromere Protein I (CENP-I) Is Upregulated in Gastric Cancer, Predicts Poor Prognosis, and Promotes Tumor Cell Proliferation and Migration

This study aimed to investigate the expression and cellular function of the centromeric family of proteins (CENPs), especially centromere protein I (CENP-I), in gastric cancer (GC) and identified its clinical significance and cellular functions. CENP-I expression in GC was studied by cDNA microarray, quantitative real-time PCR (qRT-PCR), and immunohistochemistry (IHC), and using datasets from The Cancer Genome Atlas (TCGA), UALCAN, and Gene Expression Omnibus (GEO) databases. Microarray and bioinformatic analyses identified upregulated CENP-A/E/F/H/I/K/P/W and HJURP in stomach adenocarcinoma (STAD), but not in signet ring cell carcinoma (SRCC). Significantly higher CENP-I mRNA expression was also confirmed in 40 pairs of GC tissues than in paired normal gastric tissues by qRT-PCR (P<.001). IHC showed that elevated CENP-I expression was associated with higher tumor stage, lymph node invasion, increased HER2-positive rate (36.7% vs 10.0%), and intestinal Lauren classification in 69 GC samples compared to paired paracancerous normal tissues. The survival of the high-CENP-I group members was poor compared with that of the low-CENP-I group (P = .0011). Cox univariate regression analysis identified tumor size (P = .008), HER2 status (P = .027), and CENP-I expression (P = .049) were independent prognostic factors of GC. The cellular function of CENP-I was studied in MKN45 and MKN28 GC cell lines in vitro. Cell proliferation, migration, and apoptosis were determined using CCK-8, transwell assay, TUNEL assay, and flow cytometry. Our results showed that CENP-I promoted GC cell proliferation, inhibited apoptosis, facilitated cell migration, and induced epithelial–mesenchymal transition (EMT), possibly by activating the AKT pathway. CENP-I expression was correlated with genetic signatures of the proliferative subtype of GC, characterized by intestinal Lauren classification, HER2 amplification, and TP53 mutation. In conclusion, this study revealed an elevated CENP-I expression in GC, which was associated with malignant features and poor prognosis of GC patients, and identified its function in modulating cell proliferation, apoptosis, and migration.

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

High mRNA Expression of CENPL and Its Significance in Prognosis of Hepatocellular Carcinoma Patients

Centromere proteins (CENPs) are the main constituent proteins of kinetochore, which are essential for cell division. In recent years, several studies have revealed that several CENPs were aberrantly expressed in hepatocellular carcinoma (HCC). However, numerous centromere proteins have not been studied in HCC. In this study, we used databases of Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), the Kaplan-Meier Plotter, cBioPortal, the Human Protein Atlas (HPA), and TIMER (Tumor Immune Estimation Resource) and immunohistochemical staining of clinical specimens to investigate the expression of 15 major centromere proteins in HCC to evaluate their potential prognostic value. We found that the mRNA levels of 4 out of 15 centromere proteins (CENPL, CENPQ, CENPR, and CENPU) were significantly higher in HCC than in normal tissues, and their mRNA levels were associated with the tumor stages (p values < 0.01). Patients with higher mRNA levels of CENPL had poorer overall survival, progression-free survival, relapse-free survival, and disease-specific survival (p values < 0.05). Furthermore, the higher levels of CENPL mRNA were associated with worse overall survival in males without hepatitis virus infection (p values < 0.05). The protein expression level of CENPL in human HCC tissue was higher than that in normal liver tissue. In addition, the expression of CENPL was positively correlated with the levels of the tumor-infiltrating lymphocytes. The results suggest that the high mRNA expression of CENPL may be a potential predictor of prognosis in HCC patients.

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

BACKGROUND

Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment.

RESULTS

The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes - one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups.

CONCLUSION

A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies - non-treatment and treatment - are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

Downregulated SPINK4 is associated with poor survival in colorectal cancer

BACKGROUND

SPINK4 is known as a gastrointestinal peptide in the gastrointestinal tract and is abundantly expressed in human goblet cells. The clinical significance of SPINK4 in colorectal cancer (CRC) is largely unknown.

METHODS

We retrieved the expression data of 1168 CRC patients from 3 Gene Expression Omnibus (GEO) datasets (GSE24551, GSE39582, GSE32323) and The Cancer Genome Atlas (TCGA) to compare the expression level of SPINK4 between CRC tissues and normal colorectal tissues and to evaluate its value in predicting the survival of CRC patients. At the protein level, these results were further confirmed by data mining in the Human Protein Atlas and by immunohistochemical staining of samples from 81 CRC cases in our own center.

RESULTS

SPINK4 expression was downregulated in CRC compared with that in normal tissues, and decreased SPINK4 expression at both the mRNA and protein levels was associated with poor prognosis in CRC patients from all 3 GEO datasets, the TCGA database and our cohort. Additionally, lower SPINK4 expression was significantly related to higher TNM stage. Moreover, in multivariate regression, SPINK4 was confirmed as an independent indicator of poor survival in CRC patients in all databases and in our own cohort.

CONCLUSIONS

We concluded that reduced expression of SPINK4 relates to poor survival in CRC, functioning as a novel indicator.

An integrated mRNA-lncRNA signature for relapse prediction in laryngeal cancer

Patients with laryngeal cancer with early relapse usually have a poor prognosis. In this study, we aimed to identify a multi-gene signature to improve the relapse prediction in laryngeal cancer. One microarray data set GSE27020 (training set, N = 109) and one RNA-sequencing data set (validation set, N = 85) were included into the analysis. In the training set, the microarray expression profile was re-annotated into an mRNA-long noncoding RNA (lncRNA) biphasic profile. Then, LASSO Cox regression model identified nine relapse-related RNA (eight mRNA and one lncRNA), and a risk score was calculated for each sample according to the model coefficients. Patients with high-risk showed poorer relapse-free survival than patients with low risk (hazard ratios (HR): 6.189, 95% confidence interval (CI): 3.075-12.460, P < 0.0001). The risk score demonstrated good accuracy in predicting the relapse (area under time-dependent receiver-operating characteristic (AUC): 0.859 at 1 year, 0.822 at 3 years, and 0.815 at 5 years). The results were validated in the validation set (HR: 3.762, 95% CI: 1.594-8.877, P = 0.011; AUC: 0.770 at 1 year, 0.769 at 3 years, and 0.728 at 5 years). The multivariate analysis reached consistent results after adjustment by multiple confounders. When compared with a 27-gene signature, a 2-lncRNA signature, and Tumor-Node-Metastasis stage, the risk score also showed better performance (P < 0.05). In conclusion, we successfully developed a robust mRNA-lncRNA signature that can accurately predict the relapse in laryngeal cancer.

Upregulation of SOX18 in colorectal cancer cells promotes proliferation and correlates with colorectal cancer risk

Background

Since colorectal cancer (CRC) is one of the most common malignant tumors worldwide, we aimed to identify the role of sex determining region Y (SRY)-box 18 (SOX18) in CRC.

Methods

RT-PCR and immunohistochemistry were employed to detect the expression of SOX18 in CRC samples. We then identified the effect of SOX18 on cell proliferation, cell cycle, and apoptosis by cell counting kit-8 (CCK-8), flow cytometry, and annexin V/PI staining, respectively. The effect of silencing SOX18 expression in CRC development was evaluated by using a xenograft mouse model.

Results

First, we found that SOX18 was overexpressed in CRC tissues and cell lines and that SOX18 levels in CRC tissues were positively associated with advanced clinical stages, vascular invasion, and lymph node metastasis. Furthermore, patients with higher expression of SOX18 had a lower survival rate. Overexpression of SOX18 significantly promoted cell proliferation, promoted S cell cycle progression, and inhibited cell apoptosis. Conversely, downregulation of SOX18 clearly weakened cell proliferation, induced G0/G1 cell cycle phase arrest, and gave rise to cell apoptosis. The results showed that shSOX18 significantly inhibited tumor growth and weight. Ki67 expression was also decreased by SOX18 silencing treatment.

Conclusion

Our study indicates that SOX18 may have a carcinogenic effect on CRC, which might provide novel insights into CRC prevention and treatment.