Identification of crucial genes based on expression profiles of hepatocellular carcinomas by bioinformatics analysis

A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f-divergence based generative adversarial imputation method, called sc-fGAIN, for the scRNA-seq data imputation. Our studies identify four f-divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc-fGAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc-fGAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation variability. The flexibility offered by the f-divergence allows the sc-fGAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

A novel f -divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f -divergence based generative adversarial imputation method, called sc- f GAIN, for the scRNA-seq data imputation. Our studies identify four f -divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc- f GAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc- f GAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation bias. The flexibility offered by the f -divergence allows the sc- f GAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

A TCR mimic CAR T cell specific for NDC80 is broadly reactive with solid tumors and hematologic malignancies

Target identification for chimeric antigen receptor (CAR) T-cell therapies remains challenging due to the limited repertoire of tumor-specific surface proteins. Intracellular proteins presented in the context of cell surface HLA provide a wide pool of potential antigens targetable through T-cell receptor mimic antibodies. Mass spectrometry (MS) of HLA ligands from 8 hematologic and nonhematologic cancer cell lines identified a shared, non-immunogenic, HLA-A*02-restricted ligand (ALNEQIARL) derived from the kinetochore-associated NDC80 gene. CAR T cells directed against the ALNEQIARL:HLA-A*02 complex exhibited high sensitivity and specificity for recognition and killing of multiple cancer types, especially those of hematologic origin, and were efficacious in mouse models against a human leukemia and a solid tumor. In contrast, no toxicities toward resting or activated healthy leukocytes as well as hematopoietic stem cells were observed. This shows how MS can inform the design of broadly reactive therapeutic T-cell receptor mimic CAR T-cell therapies that can target multiple cancer types currently not druggable by small molecules, conventional CAR T cells, T cells, or antibodies.

Centromere protein F promotes progression of hepatocellular carcinoma through ERK and cell cycle-associated pathways

Hepatocellular carcinoma (HCC) is one of the deadliest cancer types worldwide. The centromere proteins (CENPs) are critical for the mitosis-related protein complex and are involved in kinetochore assembly and spindle checkpoint signaling during mitosis. However, the clinical significance of CENPs in the recurrence and progression of HCC remains poorly understood. Here, we examined the expression of all CENPs and their association with recurrence and survival of HCC patients using the global gene expression profile dataset established in our laboratory. The effect of silencing CENPF on cell viability, migration, and epithelial-mesenchymal transition (EMT) were detected using CCK-8, transwell, and western blot, respectively. RT-qPCR and western blot were performed to confirm the silencing of CENPF and the relationship between STAT5A and CENPF, while tumorigenesis was tested using the HCC Huh7 xenograft mouse model. Most of the CENPs is overexpressed in HCC, and overexpression of CENPF was significantly associated with the poor survival of HCC patients. CENPF promoted HCC cell lines migration and EMT progression. Knockdown CENPF inhibited cell growth activity against human HCC cells in vitro and xenograft tumors in vivo. Bioinformatics analysis revealed that CENPF genes are enriched in the cell cycle. Silencing CENPF arrested cell cycle at the G2/M phase and inhibited Cyclin B1 and Cyclin E1 expressions. Meanwhile, silencing CENPF prohibited phosphorylation of ERK and the expression of NEK2. Additionally, we found that STAT5A down-regulated CENPF expression and inhibited cancer cell growth viability. In conclusion, our data suggested that CENPF could be potentially developed into a theranostic biomarker to tackle HCC progression.

ENO3 promotes colorectal cancer progression by enhancing cell glycolysis

BACKGROUND

Colorectal cancer (CRC) is among the leading cause of cancer-related morbidity and mortality worldwide. Aerobic glycolysis, as a metabolic hallmark of cancer, plays an important role in CRC progression. Enolase 3 (ENO3) is a glycolytic enzyme that catalyzes 2-phosphoglycerate into phosphoenolpyruvate, while its role in CRC is still unknown.

METHODS

Bioinformatics analysis was performed to examine the expression changes and roles of ENO3 in CRC patients from public databases. Then, ENO3 expression was validated in CRC tissues using Quantitative real-time PCR (qRT-PCR), immunohistochemical (IHC) analysis, and western blot. Overexpression and silencing models were constructed using plasmid and lentivirus transfection. Cell viability, proliferation, and migration in vitro were applied to evaluate the protumoral effects of ENO3 on CRC. RNA sequencing and GO enrichment analysis of differentially expressed genes (DEGs) were performed to explore the underlying molecular mechanisms of ENO3 in CRC progression. The ATP and lactate production level were detected to assess cell glycolysis.

RESULTS

ENO3 was significantly up-regulated in CRC. High ENO3 expression was positively correlated with poor prognosis and higher clinical stages of CRC patients. ROC curve demonstrated the diagnostic value of ENO3 for CRC with the AUC of 0.802. Gain- and loss-of function experiments demonstrated that ENO3 significantly enhanced the proliferation and migration ability of CRC cells in vitro. After ENO3 knockdown, RNA sequencing screened out a list of DEGs which were enriched in the regulation of the glycolytic process. The detection of lactate production and ATP level verified the role of ENO3 in the glycolytic process.

CONCLUSION

Our findings illustrate that ENO3 could promote the progression of CRC by the enhancement of cell glycolysis, indicating the potential value of ENO3 as a novel biomarker and therapeutic target for CRC.

NCAPG promotes the oncogenesis and progression of non-small cell lung cancer cells through upregulating LGALS1 expression

BACKGROUND

Numerous common oncogenic driver events have been confirmed in non-small cell lung cancer (NSCLC). Although targeted therapy has revolutionized NSCLC treatment, some patients still do not respond. NCAPG, also known as non-SMC condensin I complex subunit G, was positively associated with proliferation and migration in several tumor types.

METHODS

We used transcriptional sequencing and TCGA database analysis to identify NCAPG as a new therapeutic target for NSCLC. The oncogenic roles of NCAPG in NSCLC tumor growth and metastasis were detected in vitro and in vivo. Ncapg^+/+ or Ncapg^+/- mice with urethane treatment were analyzed for oncogenesis of NSCLC.

RESULTS

We investigated NCAPG as a new oncogenic driver which promoted NSCLC tumorigenesis and progression. We used transcriptome sequencing and the Cancer Genome Atlas (TCGA) database analysis to screen and found that NCAPG was negatively correlated with NSCLC survival. Using immunohistochemistry, we demonstrated that NCAPG overexpression was an independent risk factor for NSCLC survival. Functionally, NCAPG knockdown inhibited proliferation, migration, and invasion of NSCLC cells in vitro and in vivo. We exposed wildtype or Ncapg^+/- mice to urethane and discovered that urethane-induced lung tumors were reduced in Ncapg^+/- mice. Mechanistically, the function of NCAPG in promoting initiation and progression of NSCLC was closely related to LGALS1, which was also upregulated in NSCLC and might interact directly with NCAPG.

CONCLUSIONS

This study indicates that NCAPG is one of the essential factors for NSCLC oncogenesis and progression, providing a new target for prognosis prediction and treatment of NSCLC.

ENO3 Inhibits Growth and Metastasis of Hepatocellular Carcinoma via Wnt/β-Catenin Signaling Pathway

β-enolase (ENO3) is a metalloenzyme that functions during glycolysis and has been revealed ectopic expression in different cancers. However, the function and underlying modulatory mechanisms of ENO3 in hepatocellular carcinoma (HCC) are still elusive. Here, we discovered that ENO3 was remarkably down-regulated in human HCC tissue in contrast to those in noncancerous tissue. Moreover, low expression of ENO3 was related to the poor prognosis of HCC patients. Overexpression of ENO3 suppressed proliferative, migratory, and invasive abilities of HCC cells both in vitro and in vivo, whereas knocking down ENO3 led to the opposite effect. In addition, we revealed that ENO3 repressed the epithelial-mesenchymal transition (EMT) process with its biomarker variations. Mechanistic research unveiled that ENO3 suppressed the Wnt/β-catenin signal, which subsequently modulated the transcription of its target genes associated with the proliferation and metastasis capacity of HCC cells. Taken together, our study uncovered that ENO3 acted as a tumor inhibitor in HCC development and implied ENO3 as a promising candidate for HCC treatment.

Inhibition of NCAPG expression inactivates the Wnt/β-catenin signal to suppresses endometrial cancer cell growth in vitro

Endometrial cancer (EC) ranks as the most prevalent malignancy occurring in the female genital tract. Non-SMC condensin I complex subunit G (NCAPG), a mitotic associated chromosomal condensing protein, is reported to be frequently abnormally expressed in several tumors and plays a vital role in carcinogenesis. Our study aimed to explore the effect of NCAPG on cell proliferation and apoptosis in EC cells and to determine the underlying mechanism. Expression and survival data of NCAPG in EC tissues were analyzed by bioinformatics methods. Cell proliferation was evaluated by EdU and CCK-8 assays. Apoptosis was assessed by flow cytometry analysis. Expression of NCAPG, proliferating cell nuclear antigen (PCNA), Ki67, Bcl-2, Bax, active caspase-3, active β-catenin, and c-Myc were determined by western blotting. NCAPG was highly expressed in EC tissues and cells and predicted poor survival for EC patients. NCAPG knockdown inhibited cell proliferation and induced apoptosis in EC cells. Additionally, NCAPG knockdown inactivated the Wnt/β-catenin pathway in EC cells. Mechanistically, β-catenin overexpression blocked the tumorigenic effects of NCAPG in EC cells. In conclusion, NCAPG silencing inhibited cell proliferation and induced apoptosis in EC cells via inhibiting the Wnt/β-catenin pathway.

Upregulation of CENPF is linked to aggressive features of osteosarcoma

Centromere protein F (CENPF) plays a key role in the regulation of the cell cycle. The present study revealed that CENPF was overexpressed in a variety of tumors and associated with the poor prognosis of osteosarcoma. The mRNA expression levels of CENPF were analyzed using the Gene Expression Profiling Interactive Analysis database and the protein levels of CENPF were detected in the specimens from patients with osteosarcoma using immunohistochemistry. Cell proliferation, cell cycle and flow cytometry assays were performed after the transfection of control or CENPF plasmids into osteosarcoma cells. A xenografts assay was used to determine the effects of CENPF on tumor growth in vivo. The results showed that CENPF was upregulated in osteosarcoma tissues and associated with high-grade tumor stage (P=0.023) and intraglandular dissemination (P=0.046). The transfection-induced depletion of CENPF in human osteosarcoma MG-63 and U-2 OS cell lines inhibited cell proliferation, stimulated apoptosis and induced cell cycle arrest. Induced CENPF depletion in MG-63 cells inhibited tumor growth of osteosarcoma cells in mice. These findings suggested that elevated CENPF levels contributed to increased cell proliferation by mediating apoptosis and cell cycle in osteosarcoma. Therefore, CENPF might be a potential biomarker for poor prognosis of osteosarcoma.

Estrogen Receptor-α Suppresses Liver Carcinogenesis and Establishes Sex-Specific Gene Expression

Estrogen protects females from hepatocellular carcinoma (HCC). To determine whether this protection is mediated by classic estrogen receptors, we tested HCC susceptibility in estrogen receptor-deficient mice. In contrast to a previous study, we found that diethylnitrosamine induces hepatocarcinogenesis to a significantly greater extent when females lack Esr1, which encodes Estrogen Receptor-α. Relative to wild-type littermates, Esr1 knockout females developed 9-fold more tumors. Deficiency of Esr2, which encodes Estrogen Receptor-β, did not affect liver carcinogenesis in females. Using microarrays and QPCR to examine estrogen receptor effects on hepatic gene expression patterns, we found that germline Esr1 deficiency resulted in the masculinization of gene expression in the female liver. Six of the most dysregulated genes have previously been implicated in HCC. In contrast, Esr1 deletion specifically in hepatocytes of Esr1 conditional null female mice (in which Cre was expressed from the albumin promoter) resulted in the maintenance of female-specific liver gene expression. Wild-type adult females lacking ovarian estrogen due to ovariectomy, which is known to make females susceptible to HCC, also maintained female-specific expression in the liver of females. These studies indicate that Esr1 mediates liver cancer risk, and its control of sex-specific liver gene expression involves cells other than hepatocytes.

Update on Hepatocellular Carcinoma: a Brief Review from Pathologist Standpoint

Hepatocellular carcinoma is one of the most common cancers and an important health problem all over the world. Its prognosis is poor. For better patient care, early diagnosis is essential. Although new imaging techniques have a big impact on hepatocellular carcinoma diagnosis, histopathological examination is still the gold standard for precise diagnosis. Histopathological evaluation gives exact diagnosis in the meaning of tumor size, histological subtypes, grading, and differential diagnosis from metastasis and other tumors. Immunohistochemistry as a part of diagnostic histopathological technique plays an important role in routine practice. Immunohistochemistry is useful for confirming of hepatocytic origin, supporting hepatocellular malignancy, and differential diagnosis. It also gives prognostic information. There are growing attempts to classify tumors by their molecular genetic signatures. This is also actual for hepatocellular carcinoma. This mini review focuses on the histopathology of hepatocellular carcinoma including subtypes; differential diagnosis and immunohistochemistry as an ancillary diagnostic tool, updated or added entities, i.e., combined hepatocellular-cholangiocarcinoma; small hepatocellular carcinoma; correlation with molecular studies; and future perspectives.

NCAPG upregulation mediated by four microRNAs combined with activation of the p53 signaling pathway is a predictor of poor prognosis in patients with breast cancer

The role of non-SMC condensin I complex subunit G (NCAPG) in breast cancer remains unclear. The present study used online databases, reverse transcription-quantitative PCR, flow cytometry and western blotting to determine the expression levels, prognosis and potential molecular mechanisms underlying the role of NCAPG in breast cancer. The association between NCAPG expression and several different clinicopathological parameters in patients with breast cancer was determined, and the results revealed that NCAPG expression was negatively associated with estrogen receptor and progesterone receptor positive status, but was positively associated with HER2 positive status, Nottingham Prognostic Index score and Scarff-Bloom-Richardson grade status. Furthermore, upregulated expression levels of NCAPG resulted in a poor prognosis in patients with breast cancer. A total of 27 microRNAs (miRNAs/miRs) were predicted to target NCAPG, among which four miRNAs (miR-101-3p, miR-195-5p, miR-214-3p and miR-944) were predicted to most likely regulate NCAPG expression in breast cancer. A total of 261 co-expressed genes of NCAPG were identified, including cell division cyclin 25 homolog C (CDC25C), and pathway enrichment analysis indicated that these co-expressed genes were significantly enriched in the p53 signaling pathway. CDC25C expression was downregulated in breast cancer and was associated with a poor prognosis. These findings suggested that upregulated NCAPG expression may be a prognostic biomarker of breast cancer.

NCAPG Induces Cell Proliferation in Cardia Adenocarcinoma via PI3K/AKT Signaling Pathway

Purpose

Previous studies have shown that non-SMC condensin I complex subunit G (NCAPG) overexpression is correlated to poor prognosis of multiple cancer types. Herein, we explored the underlying mechanism of NCAPG-mediated cardia adenocarcinoma (CA) proliferation and cell cycle regulation.

Methods

The protein profiling technology was used to analyze the gene expression in 20 CA and adjacent tissue samples. Differential genes were identified by bioinformatic analysis. Western blot and qRT-PCR-based analysis assessed the NCAPG expression levels in multiple CA cell lines. CA cell lines, SGC-7901 and AGS, were transfected with Lip 2000, and stably transfected cell lines were screened for NCAPG overexpression and downregulation. MTT and clone formation assays were employed to detect cell proliferation, and cell cycle phases were analyzed using flow cytometry. Western blot was performed to determine the NCAPG gene expression levels. Finally, we studied the tumorigenic effects of NCAPG in the mouse model and validated the cell experiment results using immunohistochemistry.

Results

A significant overexpression of NCAPG was found in CA tissues and CA cell lines. The outcomes of MTT and clone formation assays showed that NCAPG upregulation promoted cell proliferation. The outcomes of these analyses were further validated using nude mice as an in vivo tumor model. As per the outcome of Western blot and flow cytometry analysis, NCAPG regulated the G1 phase through the cyclins (CDK4, CDK6, and cyclin D1) overexpression and cell cycle inhibitors (P21 and P27) downregulation. Overexpressed NCAPG and silenced NCAPG, both in vitro and in vivo, resulted in abnormal activation of the PI3K/AKT signaling pathway in CA cells. We observed that NCAPG overexpression increased the levels of phosphorylated PI3K, AKT, and GSK3β; however, their total protein levels remained unchanged in CA cells.

Conclusion

As a CA oncogene, NCAPG promoted cell proliferation and regulated cell cycle through PI3K/AKT signaling pathway activation.

Integrative Analysis of DNA Methylation Identified 12 Signature Genes Specific to Metastatic ccRCC

Background: Abnormal epigenetic alterations can contribute to the development of human malignancies. Identification of these alterations for early screening and prognosis of clear cell renal cell carcinoma (ccRCC) has been a highly sought-after goal. Bioinformatic analysis of DNA methylation data provides broad prospects for discovery of epigenetic biomarkers. However, there is short of exploration of methylation-driven genes of ccRCC.

Methods: Gene expression data and DNA methylation data in metastatic ccRCC were sourced from the Gene Expression Omnibus (GEO) database. Differentially methylated genes (DMGs) at 5′-C-phosphate-G- 3′ (CpG) sites and differentially expressed genes (DEGs) were screened and the overlapping genes in DMGs and DEGs were then subject to gene set enrichment analysis. Next, the weighted gene co-expression network analysis (WGCNA) was used to search hub DMGs associated with ccRCC. Cox regression and ROC analyses were performed to screen potential biomarkers and develop a prognostic model based on the screened hub genes.

Results: Three hundred and fourteen overlapping DMGs were obtained from two independent GEO datasets. The turquoise module contained 79 hub DMGs, which represent the most significant module screened by WGCNA. Furthermore, a total of 12 hub genes (CETN3, DCAF7, GPX4, HNRNPA0, NUP54, SERPINB1, STARD5, TRIM52, C4orf3, C12orf51, and C17orf65) were identified in the TCGA database by multivariate Cox regression analyses. All the 12 genes were then used to generate the model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Furthermore, the prognostic model with the 12 methylation-driven genes demonstrated a good prediction of 5-year survival rates for ccRCC patients.

Conclusion: Integrative analysis of DNA methylation data identified 12 signature genes, which could be used as epigenetic biomarkers for prognosis of metastatic ccRCC. This prognostic model has a good prediction of 5-year survival for ccRCC patients.

Aberrant Expression of NCAPG is Associated with Prognosis and Progression of Gastric Cancer

Introduction

Gastric cancer (GC), one of the most prevalent malignancies, is the third-leading cause of cancer-related deaths globally. The aim of this study is to investigate the involvement of non-structural maintenance of chromosomes condensin I complex subunit G (NCAPG) in the prognosis of GC.

Methods

Western blotting and immunostaining were employed to measure the NCAPG level in gastric tissues and cells. Kaplan–Meier analysis was applied to analyze the prognostic value of NCAPG in GC. RNA interference was applied to investigate the influence of the NCAPG silencing on GC cell growth and spread.

Results

NCAPG overexpression was associated with several clinicopathologic characteristics, including nodal status (P = 0.0378), distant metastasis (P = 0.0088), staging (P = 0.0230), vascular invasion (P = 0.0012), and disease-free survival (P = 0.004). Kaplan–Meier analysis revealed that NCAPG overexpression was positively correlated to poor GC patients disease-free and overall survival (P = 0.004 and P < 0.001, respectively). Univariate Cox regression analysis showed that the overexpression of NCAPG was a prognostic biomarker of GC (P = 0.005). In cultured GC cells, the knockdown of NCAPG suppressed cell proliferation, migration and invasion. Meanwhile, further studies revealed that the NCAPG silencing induces the G0/G1 cell cycle arrest and accordingly represses cell division. Finally, Western blotting showed that NCPAG knockdown dysregulated cell cycle- and epithelial–mesenchymal transition-related molecules.

Conclusion

Overall, the results reveal that NCAPG overexpression is a candidate prognostic biomarker and potential therapeutic target in GC.

Prognostic Value of Enolase Gene Family in Colon Cancer

BACKGROUND Colorectal cancer (CRC), the most common gastrointestinal cancer, is associated with high mortality rates. Enolase is a major enzyme present in the glycolytic pathway. However, the functional significance of the enolase (ENO) gene family in the pathogenesis of CRC has been unclear. MATERIAL AND METHODS The data associated with 438 CRC patients from The Cancer Genome Atlas database were extracted for analysis. Survival analyses with Cox regression was performed to construct a prognostic signature. We investigated the processes that underlies the correlation between ENO genes and overall survival (OS) using gene set enrichment analysis (GSEA). We then developed a connectivity map to identify candidate target drugs for CRC. RESULTS The multivariate survival analysis showed that low expression of ENO2 and ENO3 had a significant correlation with longer OS. The joint-effects survival analysis indicated that the combined low expression of ENO2 and ENO3 was highly correlated with favorable OS. As indicated by the gene set enrichment analysis (GSEA), the ENO gene is involved in various biological pathways and has multiple roles. Potential pharmacological targets of ENO2 and ENO3 were constructed as well. CONCLUSIONS Low expression levels of both ENO2 and ENO3 were linked to a positive prognosis for CRC. Both ENO2 and ENO3 show promise as prognostic biomarkers for colon cancer patients.

A Single Gene Expression Set Derived from Artificial Intelligence Predicted the Prognosis of Several Lymphoma Subtypes; and High Immunohistochemical Expression of TNFAIP8 Associated with Poor Prognosis in Diffuse Large B-Cell Lymphoma

Objective: We have recently identified using multilayer perceptron analysis (artificial intelligence) a set of 25 genes with prognostic relevance in diffuse large B-cell lymphoma (DLBCL), but the importance of this set in other hematological neoplasia remains unknown. Methods and Results: We tested this set of genes (i.e., ALDOB, ARHGAP19, ARMH3, ATF6B, CACNA1B, DIP2A, EMC9, ENO3, GGA3, KIF23, LPXN, MESD, METTL21A, POLR3H, RAB7A, RPS23, SERPINB8, SFTPC, SNN, SPACA9, SWSAP1, SZRD1, TNFAIP8, WDCP and ZSCAN12) in a large series of gene expression comprised of 2029 cases, selected from available databases, that included chronic lymphocytic leukemia (CLL, n = 308), mantle cell lymphoma (MCL, n = 92), follicular lymphoma (FL, n = 180), DLBCL (n = 741), multiple myeloma (MM, n = 559) and acute myeloid leukemia (AML, n = 149). Using a risk-score formula we could predict the overall survival of the patients: the hazard-ratio of high-risk versus low-risk groups for all the cases was 3.2 and per disease subtype were as follows: CLL (4.3), MCL (5.2), FL (3.0), DLBCL not otherwise specified (NOS) (4.5), multiple myeloma (MM) (5.3) and AML (3.7) (all p values < 0.000001). All 25 genes contributed to the risk-score, but their weight and direction of the correlation was variable. Among them, the most relevant were ENO3, TNFAIP8, ATF6B, METTL21A, KIF23 and ARHGAP19. Next, we validated TNFAIP8 (a negative mediator of apoptosis) in an independent series of 97 cases of DLBCL NOS from Tokai University Hospital. The protein expression by immunohistochemistry of TNFAIP8 was quantified using an artificial intelligence-based segmentation method and confirmed with a conventional RGB-based digital quantification. We confirmed that high protein expression of TNFAIP8 by the neoplastic B-lymphocytes associated with a poor overall survival of the patients (hazard-risk 3.5; p = 0.018) as well as with other relevant clinicopathological variables including age >60 years, high serum levels of soluble IL2RA, a non-GCB phenotype (cell-of-origin Hans classifier), moderately higher MYC and Ki67 (proliferation index), and high infiltration of the immune microenvironment by CD163-positive tumor associated macrophages (CD163+TAMs). Conclusion: It is possible to predict the prognosis of several hematological neoplasia using a single gene-set derived from neural network analysis. High expression of TNFAIP8 is associated with poor prognosis of the patients in DLBCL.

Identification of Potential Biomarkers Associated with Basal Cell Carcinoma

Purpose

This work is aimed at identifying several molecular markers correlated with the diagnosis and development of basal cell carcinoma (BCC).

Methods

The available microarray datasets for BCC were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between BCC and healthy controls. Afterward, the functional enrichment analysis and protein-protein interaction (PPI) network analysis of these screened DEGs were performed. An external validation for the DEG expression level was also carried out, and receiver operating characteristic curve analysis was used to evaluate the diagnostic values of DEGs.

Result

In total, five microarray datasets for BCC were downloaded and 804 DEGs (414 upregulated and 390 downregulated genes) were identified. Functional enrichment analysis showed that these genes including CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1 were closely correlated with the cell process and PTCH1 played central roles in the BCC signaling pathway. Moreover, EGFR was a hub gene in the PPI network. The expression changes of six genes (CYFIP2, HOXB5, FOXN3, PTPN3, MARCKSL1, and FAS) were validated by an external GSE74858 dataset analysis. Finally, ROC analysis revealed that CYFIP2, HOXB5, PTPN3, MARCKSL1, PTCH1, and CDC20 could distinguish BCC and healthy individuals.

Conclusion

Nine gene signatures (CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1) may serve as promising targets for BCC detection and development.

MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer

Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.

Protein Kinase A Is Involved in Neuropathic Pain by Activating the p38MAPK Pathway to Mediate Spinal Cord Cell Apoptosis

Neuropathic pain is a serious clinical problem to be solved. This study is aimed at investigating protein kinase A (PKA) expression in neuropathic pain and its possible mechanisms of involvement. A neuropathic pain-related gene expression dataset was downloaded from Gene Expression Omnibus, and differentially expressed genes were screened using the R software. cytoHubba was used to screen for hub genes. A spared nerve injury (SNI) rat model was established, and the paw withdrawal threshold was determined using von Frey filaments. Western blotting and immunofluorescence were used to detect the expression and cellular localization, respectively, of key proteins in the spinal cord. Western blot, ELISA, and TUNEL assays were used to detect cell signal transduction, inflammation, and apoptosis, respectively. Pka was identified as a key gene involved in neuropathic pain. After SNI, mechanical allodynia occurred, PKA expression in the spinal cord increased, the p38MAPK pathway was activated, and spinal cord inflammation and apoptosis occurred in rats. PKA colocalized with neurons, astrocytes, and microglia, and apoptotic cells were mainly neurons. Intrathecal injection of a PKA inhibitor not only relieved mechanical hyperalgesia, inflammatory reaction, and apoptosis in SNI rats but also inhibited p38MAPK pathway activation. However, intrathecal injection of a p38MAPK inhibitor attenuated mechanical hyperalgesia, inflammation, and apoptosis, but did not affect PKA expression. In conclusion, PKA is involved in neuropathic pain by activating the p38MAPK pathway to mediate spinal cord cell apoptosis.

Identification of potential hub genes related to the progression and prognosis of hepatocellular carcinoma through integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related deaths among cancer patients. Genes correlated with the progression and prognosis of HCC are critically needed to be identified. In the present study, 3 Gene Expression Omnibus (GEO) datasets (GSE46408, GSE65372 and GSE84402) were used to analyze the differentially expressed genes (DEGs) between HCC and non-tumor liver tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to clarify the functional roles of DEGs. A protein-protein interaction network was established to screen the hub genes associated with HCC. The prognostic values of hub genes in HCC patients were analyzed using The Cancer Genome Atlas (TCGA) database. The expression levels of hub genes were validated based on ONCOMINE, TCGA and Human Protein Atlas (HPA) databases. Notably, 56 upregulated and 33 downregulated DEGs were markedly enriched under various GO terms and four KEGG terms. Among these DEGs, 10 hub genes with high connectivity degree were identified, including cyclin B1, cyclin A2, cyclin B2, condensin complex subunit 3, PDZ binding kinase, nucleolar and spindle-associated protein 1, aurora kinase A, ZW10 interacting kinetochore protein, protein regulator of cytokinesis 1 and kinesin family member 4A. The upregulated expression levels of these hub genes in HCC tissues were further confirmed by ONCOMINE, TCGA, and HPA databases. Additionally, the increased mRNA expression of each hub gene was related to the unfavorable disease-free survival and overall survival of HCC patients. The present study identified ten genes associated with HCC, which may help to provide candidate targets for the diagnosis and treatment of HCC.