Cancer Biomarkers

Kidney renal clear cell carcinoma (KIRC) belongs to renal cell carcinoma which is a very aggressive malignant tumor with poor prognosis and high mortality. The MKRN family includes three members MKRN1, MKRN2 and MKRN3, which are closely related to cancers, and have been involved in many studies.

This study aimed to explore the roles of MKRN family in KIRC.

The expression of MKRNs was analyzed using the UALCAN database, prognostic analysis was performed with the GEPIA2 and Kaplan-Meier Plotter database, and correlation analysis was assessed by GEPIA2. The CCK-8 and colony formation assay were performed to detect cell proliferation, wound healing assays were performed to detect cell migration, cell cycles were detected by flow cytometry analysis, GST pull-down and co-immunoprecipitation assays were performed to detect the interaction of proteins, and the expression of MKRNs, p53 and other proteins were detect by immunoblotting analysis or quantitative PCR (qPCR).

MKRN1 and MKRN2 were lowly expressed in KIRC samples compared to the corresponding normal tissues, and KIRC patients with high levels of MKRN1 and MKRN2 showed higher overall survival (OS) and disease free survival (DFS) rates. The overexpression of MKRN1 and MKRN2 inhibited the proliferation of human KIRC cells by arresting the cell cycles, but shows little effect on cells migration. The expression of MKRN1 and MKRN2 are correlated, and MKRN1 directly interacts with MKRN2. Moreover, both MKRN1 and MKRN2 were closely correlated with the expression of TP53 in KIRC tumor, and promoted the expression of p53 both at protein and mRNA levels.

Our study suggests that MKRN1 and MKRN2 serve as tumor suppressors in KIRC, and act as promising therapeutic targets for KIRC treatment.

Abdominal or back pain is a common symptom in pancreatic diseases. However, the role of pain in pancreatic neuroendocrine neoplasm (PNENs) has not been clarified.

In this study, we aimed to show the association between the pain and the grade of PNENs.

A total of 186 patients with pathologically confirmed PNENs were included in this study. Clinical features and histological or radiological findings (size, location, and vascular invasion and local organs invasion and distal metastasis) were collected. Logistic regression analyses were used to show the association between pain and grade of PNENs. Nomogram was developed based on associated factors to predict the higher grade of PNENs. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of size and nomogram model.

The prevalence of pain in the cohort was 30.6% ( n = 57). The vascular invasion and G3 PNENs were more common in the pain group ( P = 0.02, P < 0.01). The tumor size was larger and incident of higher grade of PNENs was higher in the pain group than the non-pain group ( p < 0.01). Age, pain and size were independent risk factors for G2/G3 or G3 PNENs. The odds ratio was 3.03 (95% CI: 1.67–7.91) and 3.32 (95% CI: 1.42–7.79) for pain, respectively. The nomogram model was developed to predict the G2/G3 or G3 PNENs. The area under the curve (AUC) of the nomogram model was 0.84 (95% CI, 0.77–0.91) in predicting the G2/G3 PNENs, and was 0.84 (95% CI, 0.78–0.91) in predicting the G3 PNENs.

Abdominal or back pain is associated with the grade of PNENs. The nomograms based on clinical features may be a powerful numerical tool for predicting the grade of PNENs.

Acute myeloid leukemia (AML) has a poor prognosis, and the current 5-year survival rate is less than 30%.

The present study was designed to identify the significant genes closely related to AML prognosis and predict the prognostic value by constructing a risk model based on their expression.

Using bioinformatics (Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, univariate and multivariate Cox regression analysis, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) analysis) to identify a prognostic gene signature for AML. Finally, The Cancer Genome Atlas (TCGA) database was used to validate this prognostic signature.

Based on univariate and multivariate Cox regression analysis, eighteen prognostic genes were identified, and the gene signature and risk score model were constructed. Multivariate Cox analysis showed that the risk score was an independent prognostic factor [hazard ratio (HR) = 1.122, 95% confidence interval (CI) = 1.067–1.180, P < 0.001]. ROC analysis showed a high predictive value of the risk model with an area under the curve (AUC) of 0.705.

This study evaluated a potential prognostic signature with eighteen genes and constructed a risk model significantly related to the prognosis of AML patients.

Regulatory T cells (Tregs) are central to determine immune response, thus targeting Tregs for immunotherapy is a promising strategy against tumor development and metastasis.

The objective of this study was to identify genes for targeting Tregs to improve the outcome of HCC.

We integrated expression data from different samples to remove batch effects and further applied embedding function in Scanpy to conduct sub-clustering of CD4 + T cells in HCC for each of two independent scRNA-seq data. The activity of transcription factors (TFs) was inferred by DoRothEA. Gene expression network analysis was performed in WGCNA R package. We finally used R packages (survminer and survival) to conduct survival analysis. Multiplex immunofluorescence analysis was performed to validate the result from bioinformatic analyses.

We found that regulator of G protein signaling 1 (RGS1) expression was significantly elevated in Tregs compared to other CD4 + T cells in two independent public scRNA-seq datasets, and increased RGS1 predicted inferior clinical outcome of HCC patients. Multiplex immunofluorescence analysis supported that the higher expression of RGS1 in HCC Tregs in tumor tissue compared to it in adjacent tissue. Moreover, RGS1 expression in Tregs was positively correlated with the expression of marker genes of Tregs, C-X-C chemokine receptor 4 (CXCR4), and three CXCR4-dependent genes in both scRNA-seq and bulk RNA-seq data. We further identified that these three genes were selectively expressed in Tregs as compared to other CD4 + T cells. The activities of two transcription factors, recombination signal binding protein for immunoglobulin kappa J region (RBPJ) and yin yang 1 (YY1), were significantly different in HCC Tregs with RGS1 high and RGS1 low.

Our findings suggested that RGS1 may regulate Treg function possibly through CXCR4 signaling and RGS1 could be a potential target to improve responses for immunotherapy in HCC.

We performed a bioinformatics analysis to screen for cell cycle-related differentially expressed genes (DEGs) and constructed a model for the prognostic prediction of patients with early-stage lung squamous cell carcinoma (LSCC).

From a gene expression omnibus (GEO) database, the GSE157011 dataset was randomly divided into an internal training group and an internal testing group at a 1:1 ratio, and the GSE30219, GSE37745, GSE42127, and GSE73403 datasets were merged as the external validation group. We performed single-sample gene set enrichment analysis (ssGSEA), univariate Cox analysis, and difference analysis, and identified 372 cell cycle-related genes. Additionally, we combined LASSO/Cox regression analysis to construct a prognostic model. Then, patients were divided into high-risk and low-risk groups according to risk scores. The internal testing group, discovery set, and external verification set were used to assess model reliability. We used a nomogram to predict patient prognoses based on clinical features and risk values. Clinical relevance analysis and the Human Protein Atlas (HPA) database were used to verify signature gene expression.

Ten cell cycle-related DEGs (EIF2B1, FSD1L, FSTL3, ORC3, HMMR, SETD6, PRELP, PIGW, HSD17B6, and GNG7) were identified and a model based on the internal training group constructed. From this, patients in the low-risk group had a higher survival rate when compared with the high-risk group. Time-dependent receiver operating characteristic (tROC) and Cox regression analyses showed the model was efficient and accurate. Clinical relevance analysis and the HPA database showed that DEGs were significantly dysregulated in LSCC tissue.

Our model predicted the prognosis of early-stage LSCC patients and demonstrated potential applications for clinical decision-making and individualized therapy.