Comprehensive analysis of metastasis-related genes reveals a gene signature predicting the survival of colon cancer patients

Pan-cancer drivers of metastasis

Metastasis remains a leading cause of cancer-related mortality, irrespective of the primary tumour origin. However, the core gene regulatory program governing distinct stages of metastasis across cancers remains poorly understood. We investigate this through single-cell transcriptome analysis encompassing over two hundred patients with metastatic and non-metastatic tumours across six cancer types. Our analysis revealed a prognostic core gene signature that provides insights into the intricate cellular dynamics and gene regulatory networks driving metastasis progression at the pan-cancer and single-cell level. Notably, the dissection of transcription factor networks active across different stages of metastasis, combined with functional perturbation, identified SP1 and KLF5 as key regulators, acting as drivers and suppressors of metastasis, respectively, at critical steps of this transition across multiple cancer types. Through in vivo and in vitro loss of function of SP1 in cancer cells, we revealed its role in driving cancer cell survival, invasive growth, and metastatic colonisation. Furthermore, tumour cells and the microenvironment increasingly engage in communication through WNT signalling as metastasis progresses, driven by SP1. Further validating these observations, a drug repurposing analysis identified distinct FDA-approved drugs with anti-metastasis properties, including inhibitors of WNT signalling across various cancers.

Calcium and integrin-binding protein 1-like interacting with an integrin α-cytoplasmic domain facilitates cellular immunity in Helicoverpa armigera

Integrins are transmembrane receptor heterodimers composed of α and β subunits. They are known to mediate extracellular signals to promote cell adhesion and spreading, and are therefore essential for cellular immunity. However, proteins that bind to integrin cytoplasmic domains and mediate intracellular signaling to promote cell adhesion require identification. Calcium and integrin-binding protein 1 (CIB1) that binds to the integrin α-cytoplasmic domain has rarely been examined in insects. In this study, we found that 20-hydroxyecdysone promoted cell phagocytosis and spreading in Helicoverpa armigera. Transcriptomic analyses of hemocytes identified an integrin α gene (HaINTα-PS1) whose expression could be induced by either 20-hydroxyecdysone injection or bead challenge. Isothermal titration calorimetry assays showed that H. armigera CIB1-like (HaCIB1-like) weakly bound to the cytoplasmic domain of HaINTα-PS1 in the presence of calcium. HaINTα-PS1 or HaCIB1-like knockdown inhibited hemocytic encapsulation and phagocytosis, and plasmatocyte spreading. Moreover, HaCIB1-like overexpression in a H. armigera epidermal cell line overexpanded cells and impaired cell phagocytosis. Thus, insect CIB1-like potentially interacted with integrin α-cytoplasmic domain and facilitated cell adhesion. This study enriches our understanding of the molecular mechanism underlying integrin-mediated cellular immunity in insects.

Expression of TSP50, SERCA2 and IL-8 in Colorectal Adenoma and Carcinoma: Correlation to Clinicopathological Factors

Background: Colorectal cancer (CRC) is the third most common type of cancer, it is considered a genetically heterogeneous disease with different molecular pathways being involved in its initiation and progression. Testes-specific protease 50 (TSP50) gene is a member of cancer/testis antigens that encodes for threonine protease enzyme. Overexpression of TSP50 was found to enhance the progression and invasion of breast cancer and other malignant tumors. SERCA2 is widely expressed in several body tissues; its aberrant expression has been involved in many cancers. IL-8 is an inflammatory cytokine. Alongside its role in inflammation, its expression was reported to induce the migration of tumor cells. Aim: Study the expression of TSP50, SERCA2 and IL-8 in colorectal adenoma (CRA), CRC and normal colonic tissues to compare the expression of these biomarkers in relation to clinicopathological parameters and prognostic factors. Results: TSP50, SERCA2 and IL-8 expression varied between normal colonic tissues, CRA and CRC. Significant statistical association was detected between the three biomarkers' overexpression and degree of dysplasia in CRA. Also, significant statistical relation was found between the three biomarkers' overexpression and presence of lympho-vascular invasion, advanced TNM staging and high intra-tumoral inflammatory infiltrate. Multivariable analysis showed that the overexpression of the three biomarkers is significantly associated with worse prognosis. Conclusion: The expression of TSP50, SERCA2 and IL-8 was different between the normal tissue and neoplastic colorectal tissue on one hand and between CRA and CRC on the other. Increased expression of these biomarkers in neoplastic epithelial cells of colorectal carcinoma is associated with adverse prognostic factors and could be considered as independent prognostic factors.

Comprehensive Analysis of Gene Expression Profiles Identifies a P4HA1-Related Gene Panel as a Prognostic Model in Colorectal Cancer Patients

Objective: Colorectal cancer (CRC) is the leading cause of mortality worldwide. Growing evidence suggests that the current pathological staging system is inadequate for efficient and accurate prognosis. In this study, we aim to build a prognosis model to predict the survival outcome of CRC patients by using gene expression profiles from The Cancer Genome Atlas (TCGA). Materials and Methods: Univariate and multivariate Cox regression analysis were used to assess the relationship between clinical factors and P4HA1 expression regarding the prognosis of patients with colon adenocarcinoma (COAD). The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to select prognostic differential expression genes (DEGs) for the construction of prognostic risk score model. Kaplan-Meier and receiver operating characteristic (ROC) survival analysis were used to assess the performance of the model on both TCGA cohort and an independent dataset GSE39582. Results: Overexpression of P4HA1 was confirmed to be associated with poor clinical outcome of colon cancer patients in both TCGA and GSE39582 cohorts. Using the TCGA cohort, we identified 1528 DEGs related to elevated P4HA1 expression, and we established a 11-gene panel to construct the prognostic risk score model by LASSO Cox regression analysis based on their expression profiles. The 11-gene signature was further validated in the independent dataset GSE39582. Time-dependent ROC curves indicated good performance of our model in predicting 1, 2, and 3-years overall survival in COAD patients. Additionally, gene set enrichment analysis indicated that the 11-gene signature was related to pathways involved in tumor progression. Conclusions: Together, we have established a 11-gene signature significantly associated with prognosis in COAD patients, which could serve as a promising tool for clinical application in the future.

Identification of Distinct Molecular Patterns and a Four-Gene Signature in Colon Cancer Based on Invasion-Related Genes

Background

The pathological stage of colon cancer cannot accurately predict recurrence, and to date, no gene expression characteristics have been demonstrated to be reliable for prognostic stratification in clinical practice, perhaps because colon cancer is a heterogeneous disease. The purpose was to establish a comprehensive molecular classification and prognostic marker for colon cancer based on invasion-related expression profiling.

Methods

From the Gene Expression Omnibus (GEO) database, we collected two microarray datasets of colon cancer samples, and another dataset was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) further underwent univariate analysis, least absolute shrinkage, selection operator (LASSO) regression analysis, and multivariate Cox survival analysis to screen prognosis-associated feature genes, which were further verified with test datasets.

Results

Two molecular subtypes (C1 and C2) were identified based on invasion-related genes in the colon cancer samples in TCGA training dataset, and C2 had a good prognosis. Moreover, C1 was more sensitive to immunotherapy. A total of 1,514 invasion-related genes, specifically 124 downregulated genes and 1,390 upregulated genes in C1 and C2, were identified as DEGs. A four-gene prognostic signature was identified and validated, and colon cancer patients were stratified into a high-risk group and a low-risk group. Multivariate regression analyses and a nomogram indicated that the four-gene signature developed in this study was an independent predictive factor and had a relatively good predictive capability when adjusting for other clinical factors.

Conclusion

This research provided novel insights into the mechanisms underlying invasion and offered a novel biomarker of a poor prognosis in colon cancer patients.

Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC.

Methods

The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort.

Results

We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

Recent Advances in Our Knowledge of mCRC Tumor Biology and Genetics: A Focus on Targeted Therapy Development

Metastatic colorectal cancer (mCRC) remains a highly lethal malignancy although considerable progress has resulted from characterizing molecular alterations such as RAS mutation status and extent of microsatellite instability (MSI) to guide optimal use of available therapies. The availability of gene expression profiling, next generation sequencing technologies, proteomics analysis and other technologies provides high resolution information on individual tumors, including metastatic lesions to better define intra-tumor and inter-tumor heterogeneity. Recent literature applying this information to further customize personalized therapies is reviewed. Current biomarker-based stratification used to select optimal therapy that is personalized to the mutation profile of individual tumors is described. Recent literature using whole exome sequencing of metastatic lesions and primary CRC tumors and other advanced technologies to more fully elucidate the tumor biology specific to mCRC sub-types and to develop more precise therapies that improve outcomes is also reviewed.

Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

MOTIVATION

Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor.

RESULTS

Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line-derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times (P = .048) and in patients with pancreatic cancer treated with gemcitabine (P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

Identification of DNA repair-related genes predicting pathogenesis and prognosis for liver cancer

Background

Liver cancer (LC) is one of the most fatal cancers throughout the world. More efficient and sensitive gene signatures that could accurately predict survival in LC patients are vitally needed to promote a better individualized and effective treatment.

Material/methods

422 LC and adjacent normal tissues with both RNA-Seq and clinical data in TCGA were embedded in our study. Gene set enrichment analysis (GSEA) was applied to identify genes and hallmark gene sets that are more valuable for liver cancer therapy. Cox regression analysis was used to identify genes related to overall survival (OS) and build the prediction model. cBioPortal database was used to examine the alterations of the panel mRNA signature. ROC curves and Kaplan–Meier curves were used to validate the prediction model. Besides, the expression of the genes in the model were validated using quantitative real-time PCR in clinical tissue specimens.

Results

The panel of DNA repair-related mRNA signature consisted of seven mRNAs: RFC4 (replication factor C subunit 4), ZWINT (ZW10 interacting kinetochore protein), UPF3B (UPF3B regulator of nonsense mediated mRNA decay), NCBP2 (nuclear cap binding protein subunit 2), ADA (adenosine deaminase), SF3A3 (splicing factor 3a subunit 3) and GTF2H1 (general transcription factor IIH subunit 1). On-line analysis of cBioPortal database found that the expression of the panel mRNA has a wide variation ranging from 7 to 10%. All the mRNAs were significantly upregulated in LC tissues compared to normal tissues (P < 0.05). The risk model is closely related to the OS of LC patients. The hazard ratio (HR) is 2.184 [95% CI (confidence interval) 1.523–3.132] and log-rank P-value < 0.0001. For clinical specimen validation, we found that all of the genes in the model upregulated in liver cancer tissues versus normal liver tissues, which was consistent with the results predicted.

Conclusions

Our study demonstrated a mRNA signature including seven mRNA for prognosis prediction of LC. This panel gene signature provides a new criterion for accurate diagnosis and therapeutic target of LC.

Melatonin Inhibits the Progression of Oral Squamous Cell Carcinoma via Inducing miR-25-5p Expression by Directly Targeting NEDD9

Melatonin exerts anti-cancer roles in various types of cancers. However, to the best of our knowledge, its role in oral squamous cell carcinoma (OSCC) is unknown. The present study aimed to investigate the role of melatonin and its underlying mechanism in OSCC. MTT, colony formation, wound healing, and transwell invasion assays proved that melatonin played anti-tumor effects in OSCC cells by inhibiting cell viability, proliferation, migration, and invasion in a concentration-dependent manner. The RT-qPCR analysis showed that miR-25-5p was significantly upregulated after melatonin treatment. Further, miR-25-5p might be involved in melatonin-induced inhibitory effects on the biological behavior of OSCC. The expression of miR-25-5p was decreased in tumor tissues and OSCC cells detected by RT-qPCR. MTT assay, colony formation assay, and TUNEL staining indicated miR-25-5p overexpression inhibited OSCC cell viability, proliferation, and induced OSCC cell apoptosis. Furthermore, wound healing, transwell invasion assay, and animal experiments suggested that miR-25-5p might exert suppressive effects on the migration, invasion, and tumor formation of OSCC cells, while miR-25-5p knockdown exhibited the opposite effects in OSCC cells. Bioinformatics analysis, western blot analysis, and luciferase reporter assay suggested that neural precursor cell expressed developmentally downregulated protein 9 (NEDD9) was proved to be a putative target for miR-25-5p. The role of NEDD9 in inhibiting OSCC cell proliferation, invasion, and migration was verified with NEDD9 siRNA transfection. Thus, melatonin exerted anti-proliferative, anti-invasive, and anti-migrative effects on OSCC via miR-25-5p/NEDD9 pathway. Melatonin could be applied as a potential novel drug on treating OSCC.

A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.

The Role of Interleukins in Colorectal Cancer

Despite great progress has been made in treatment strategies, colorectal cancer (CRC) remains the predominant life-threatening malignancy with the feature of high morbidity and mortality. It has been widely acknowledged that the dysfunction of immune system, including aberrantly expressed cytokines, is strongly correlated with the pathogenesis and progression of colorectal cancer. As one of the most well-known cytokines that were discovered centuries ago, interleukins are now uncovering new insights into colorectal cancer therapy. Herein, we divide currently known interleukins into 6 families, including IL-1 family, IL-2 family, IL-6 family, IL-8 family, IL-10 family and IL-17 family. In addition, we comprehensively reviewed the oncogenic or antitumour function of each interleukin involved in CRC pathogenesis and progression by elucidating the underlying mechanisms. Furthermore, by providing interleukins-associated clinical trials, we have further driven the profound prospect of interleukins in the treatment of colorectal cancer.

NEDD9 overexpression predicts poor prognosis in solid cancers: a meta-analysis

Background: The oncogenicity of neural precursor cell-expressed developmentally down-regulated 9 (NEDD9) has been demonstrated in multiple cancer types. However, the prognostic value of NEDD9 in some solid cancers remains controversial. Thus, this meta-analysis was conducted to evaluate the relationship between NEDD9 expression survival rates in solid tumors.

Method: Our meta-analysis included studies searched from various search engines with specific inclusion criteria and exclusion criteria. Combined HRs for overall survival (OS) and disease-free survival (DFS) or progression-free survival (PFS) or recurrence-free survival (RFS) or cancer-specific survival (CSS) were assessed using fixed-effects and random-effects models. The source of heterogeneity was identified by subgroup analysis. Additionally, publication bias was assessed using funnel plot and Egger’s regression asymmetry test.

Result: Eighteen studies with a total of 2,476 patients were retrieved for analysis. Pooled HRs and 95% CIs were calculated. Both OS (HR=1.82; 95% CI: 1.43–2.31) and DFS/PFS/RFS/CSS (HR=2.54; 95% CI: 1.93–3.33) indicated that NEDD9 overexpression is associated with poor OS in cancer patients with solid tumors.

Conclusion: NEDD9 overexpression might be a potential marker to predict prognosis in solid cancer patients.