High SAA1 Expression Predicts Advanced Tumors in Renal Cancer

Gene expression profiling and the isocitrate dehydrogenase mutational landscape of temozolomide‑resistant glioblastoma

Glioblastoma multiforme (GBM) is an aggressive brain cancer that occurs more frequently than other brain tumors. The present study aimed to reveal a novel mechanism of temozolomide resistance in GBM using bioinformatics and wet lab analyses, including meta-Z analysis, Kaplan-Meier survival analysis, protein-protein interaction (PPI) network establishment, cluster analysis of co-expressed gene networks, and hierarchical clustering of upregulated and downregulated genes. Next-generation sequencing and quantitative PCR analyses revealed downregulated [tyrosine kinase with immunoglobulin and epidermal growth factor homology domains 1 (TIE1), calcium voltage-gated channel auxiliary subunit α2Δ1 (CACNA2D1), calpain 6 (CAPN6) and a disintegrin and metalloproteinase with thrombospondin motifs 6 (ADAMTS6)] and upregulated [serum amyloid (SA)A1, SAA2, growth differentiation factor 15 (GDF15) and ubiquitin specific peptidase 26 (USP26)] genes. Different statistical models were developed for these genes using the Z-score for P-value conversion, and Kaplan-Meier plots were constructed using several patient cohorts with brain tumors. The highest number of nodes was observed in the PPI network was for ADAMTS6 and TIE1. The PPI network model for all genes contained 35 nodes and 241 edges. Immunohistochemical staining was performed using isocitrate dehydrogenase (IDH)-wild-type or IDH-mutant GBM samples from patients and a significant upregulation of TIE1 (P<0.001) and CAPN6 (P<0.05) protein expression was demonstrated in IDH-mutant GBM in comparison with IDH-wild-type GBM. Structural analysis revealed an IDH-mutant model demonstrating the mutant residues (R132, R140 and R172). The findings of the present study will help the future development of novel biomarkers and therapeutics for brain tumors.

Identification of a risk model for prognostic and therapeutic prediction in renal cell carcinoma based on infiltrating M0 cells

The tumor microenvironment (TME) comprises immune-infiltrating cells that are closely linked to tumor development. By screening and analyzing genes associated with tumor-infiltrating M0 cells, we developed a risk model to provide therapeutic and prognostic guidance in clear cell renal cell carcinoma (ccRCC). First, the infiltration abundance of each immune cell type and its correlation with patient prognosis were analyzed. After assessing the potential link between the depth of immune cell infiltration and prognosis, we screened the infiltrating M0 cells to establish a risk model centered on three key genes (TMEN174, LRRC19, and SAA1). The correlation analysis indicated a positive correlation between the risk score and various stages of the tumor immune cycle, including B-cell recruitment. Furthermore, the risk score was positively correlated with CD8 expression and several popular immune checkpoints (ICs) (TIGIT, CTLA4, CD274, LAG3, and PDCD1). Additionally, the high-risk group (HRG) had higher scores for tumor immune dysfunction and exclusion (TIDE) and exclusion than the low-risk group (LRG). Importantly, the risk score was negatively correlated with the immunotherapy-related pathway enrichment scores, and the LRG showed a greater therapeutic benefit than the HRG. Differences in sensitivity to targeted drugs between the HRG and LRG were analyzed. For commonly used targeted drugs in RCC, including axitinib, pazopanib, temsirolimus, and sunitinib, LRG had lower IC50 values, indicating increased sensitivity. Finally, immunohistochemistry results of 66 paraffin-embedded specimens indicated that SAA1 was strongly expressed in the tumor samples and was associated with tumor metastasis, stage, and grade. SAA1 was found to have a significant pro-tumorigenic effect by experimental validation. In summary, these data confirmed that tumor-infiltrating M0 cells play a key role in the prognosis and treatment of patients with ccRCC. This discovery offers new insights and directions for the prognostic prediction and treatment of ccRCC.

MMPs-related risk model identification and SAA1 promotes clear cell renal cell carcinoma migration via ERK-AP1-MMPs axis

Matrix Metalloproteinases (MMPs) have been demonstrated to be essential in facilitating the migration and metastasis of clear cell renal cell carcinoma (ccRCC). However, the ability of the MMP family to predict clinical outcomes and guide optimal therapeutic strategies for ccRCC patients remains incompletely understood. In this investigation, we initially conducted a thorough examination of the MMP family in pan-cancer. Notably, MMPs exhibited distinctive significance in ccRCC. Following this, we undertook an extensive analysis to evaluate the clinical value of MMPs and potential mechanisms by which MMPs contribute to the progression of ccRCC. A novel stratification method and prognostic model were developed based on MMPs in order to enhance the accuracy of prognosis prediction for ccRCC patients and facilitate personalized treatment. By conducting multi-omics analysis and transcriptional regulation analysis, it was hypothesized that SAA1 plays a crucial role in promoting ccRCC migration through MMPs. Subsequently, in vitro experiments confirmed that SAA1 regulates ccRCC cell migration via the ERK-AP1-MMPs axis. In conclusion, our study has explored the potential value of the MMP family as prognostic markers for ccRCC and as guides for medication regimens. Additionally, we have identified SAA1 as a crucial factor in the migration of ccRCC.

SAA1 regulated by S1P/S1PR1 promotes the progression of ESCC via β-catenin activation

Serum amyloid A1 (SAA1), an inflammation-related molecule, is associated with the malignant progression of many tumors. This study aimed to investigate the role of SAA1 in the progression of esophageal squamous cell carcinoma (ESCC) and its molecular mechanisms. The expression of SAA1 in ESCC tissues and cell lines was analyzed using bioinformatics analysis, western blotting, and reverse transcription-quantitative PCR (RT‒qPCR). SAA1-overexpressing or SAA1-knockdown ESCC cells were used to assess the effects of SAA1 on the proliferation, migration, apoptosis of cancer cells and the growth of xenograft tumors in nude mice. Western blotting, immunofluorescence and RT‒qPCR were used to investigate the relationship between SAA1 and β-catenin and SAA1 and sphingosine 1-phosphate (S1P)/sphingosine 1-phosphate receptor 1 (S1PR1). SAA1 was highly expressed in ESCC tissues and cell lines. Overexpression of SAA1 significantly promoted the proliferation, migration and the growth of tumors in nude mice. Knockdown of SAA1 had the opposite effects and promoted the apoptosis of ESCC cells. Moreover, SAA1 overexpression promoted the phosphorylation of β-catenin at Ser675 and increased the expression levels of the β-catenin target genes MYC and MMP9. Knockdown of SAA1 had the opposite effects. S1P/S1PR1 upregulated SAA1 expression and β-catenin phosphorylation at Ser675 in ESCC cells. In conclusion, SAA1 promotes the progression of ESCC by increasing β-catenin phosphorylation at Ser675, and the S1P/S1PR1 pathway plays an important role in its upstream regulation.

Comprehensive analysis of disulfidptosis-related genes: a prognosis model construction and tumor microenvironment characterization in clear cell renal cell carcinoma

BACKGROUND

Disulfidptosis, a form of cell death induced by abnormal intracellular accumulation of disulfides, is a newly recognized variety of cell death. Clear cell renal cell carcinoma (ccRCC) is a usual urological tumor that poses serious health risks. There are few studies of disulfidptosis-related genes (DRGs) in ccRCC so far.

METHODS

The expression, transcriptional variants, and prognostic role of DRGs were assessed. Based on DRGs, consensus unsupervised clustering analysis was performed to stratify ccRCC patients into various subtypes and constructed a DRG risk scoring model. Patients were stratified into high or low-risk groups by this model. We focused on assessing the discrepancy in prognosis, TME, chemotherapeutic susceptibility, and landscape of immune between the two risk groups. Finally, we validated the expression and explored the biological function of the risk scoring gene FLRT3 through in vitro experiments.

RESULTS

The different subtypes had significantly different gene expression, immune, and prognostic landscapes. In the two risk groups, the high-risk group had higher TME scores, more significant immune cell infiltration, and a higher probability of benefiting from immunotherapy, but had a worse prognosis. There were also remarkable differences in chemotherapeutic susceptibility between the two risk groups. In ccRCC cells, the expression of FLRT3 was shown to be lower and its overexpression caused a decrease in cell proliferation and metastatic capacity.

CONCLUSIONS

Starting from disulfidptosis, we established a new risk scoring model which can provide new ideas for doctors to forecast patient survival and determine clinical treatment plans.

Identification of SAA1 as a novel metastasis marker in ovarian cancer and development of a graphene-based detection platform for early assessment

BACKGROUND

Ovarian cancer (OC) is a prevalent gynecological malignancy with the highest mortality rate, which generally diagnosed at late stages due to the lack of effective early screening methods and the nonspecific symptoms. Hence, here we aim to identify new metastasis markers and develop a novel detection method with the characteristics of high sensitivity, rapid detection, high specificity, and low cost when compared with other conventional detection technologies.

METHODS

Blood from OC patients with or without metastasis were collected and analyzed by 4D Label free LC - MS/MS. Surgically resect samples from OC patients were collected for Single cell RNA sequencing (sc-RNA seq). Short hairpin RNA (shRNA) was used to silence SAA1 expression in SKOV3 and ID8 to verify the relationship between endogenous SAA1 and tumor invasion or metastasis. The functional graphene chips prepared by covalent binding were used for SAA1 detection.

RESULTS

In our study, we identified Serum Amyloid A1 (SAA1) as a hematological marker of OC metastasis by comprehensive analysis of proteins in plasma from OC patients with or without metastasis using 4D Label free LC - MS/MS and gene expression patterns from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Further validation using tumor tissues and plasma from human OC and mouse OC model confirmed the correlation between SAA1 and tumor metastasis. Importantly, sc-RNA seq of human OC samples revealed that SAA1 was specifically expressed in tumor cells and upregulated in the metastasis group. The functional role of SAA1 in metastasis was demonstrated through experiments in vitro and in vivo. Based on these findings, we designed and investigated a graphene-based platform for SAA1 detection to predict the risk of metastasis of OC patients.

CONCLUSION

Our study suggests that SAA1 is a biomarker of OC metastasis, and we have developed a rapid and highly sensitive platform using graphene chips to detection of plasma SAA1 for the early assessment of metastasis in OC patients.

FAP promotes clear cell renal cell carcinoma progression via activating the PI3K/AKT/mTOR signaling pathway

OBJECTIVE

Herein, we aimed at exploring the FAP expression in clear cell renal cell carcinoma (ccRCC) along with its clinical implication.

METHODS

Using computational tools analysis of different freely accessible gene databases, the expression pattern, clinical importance, co-expressed genes, and signaling pathways of FAP in ccRCC were thoroughly investigated. FAP expression was examined in clinical ccRCC specimens through qRT-PCR, western blotting and immunohistochemistry. Furthermore, in vitro and in vivo experiments were carried out using flow cytometry, CCK-8, wound-healing and Transwell assays, as well as xenograft tumor model, respectively.

RESULTS

FAP levels were found to be significantly elevated in ccRCC based on bioinformatic data from public databases. Patients who exhibited higher expression levels of FAP had poorer prognoses, according to Kaplan-Meier analysis of survival data. In addition, diagnostic and prognostic value of FAP in ccRCC was figured out by ROC curve and prognostic nomogram model. In vitro study revealed that the over-expression FAP accelerated cell proliferation, migration as well as invasion, and suppressed cell apoptosis, but silencing of FAP had the opposite effect. FAP suppression reduced the PI3K/AKT/mTOR pathway's stimulation, whereas FAP up-regulation increased the stimulation of the pathway. Blocking the PI3K/AKT/mTOR signaling pathway with the dual PI3K/mTOR inhibitor BEZ235 repressesed cancer-promoting effect of FAP. Additionally, we found that the downregulation of FAP was effective at slowing tumor progression in vivo.

CONCLUSION

It is possible that FAP could be a reliable biomarker for the diagnosis and prognosis of ccRCC because of its role in the ccRCC progression via triggering the PI3K/AKT/mTOR signaling pathway.

Stage-specific coexpression network analysis of Myc in cohorts of renal cancer

The present study investigates the molecular dynamics of Myc in normal precursors and in different stages (I/II/III/IV) of cohorts of renal cancer using two distinct yet complementary approaches: gene expression and gene coexpression. We also analysed the variation of coexpression networks of Myc through the stage-wise progression of renal cancer cohorts. Myc expression is significantly higher in stage I compared to normal tissue but changed inconsistently across stages of renal cancer. We identified that Myc consistently coexpressed with fourteen genes in the KIPAN [Pan-kidney cohort (KICH + KIRC + KIRP)] and eight in the KIRC (Kidney renal clear cell carcinoma) across all stages, providing potential prognostic and diagnostic biomarkers. Coexpression network complexity decreased from normal precursor tissues to associated tumour stage I in KIPAN and KIRC but was inconsistent after that. In the process of cancer development, there is generally lower cross-tissue cancer network homology observed among coexpressed genes with Myc during the normal to the stage I compared to the stage-wise progression of cancer. Overall, this research provides novel perceptions of the molecular causes of kidney cancer. It also highlights potential genes and pathways crucial for diagnosing and treating this disease.

Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs). Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases. Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model. Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p <0.049; HR = 1.84; 95% CI = 1.02-3.32) and the training set (p < 0.001; HR = 3.12, 95% CI = 2.23-4.37). The numbers of regulatory T cells (Tregs) were significantly positively correlated with the six immunity-related genes identified, with correlation coefficients were 0.385, 0.415, 0.399, 0.451, 0.485, and 0.333, respectively (p <0.001). Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.

Single-cell profiling reveals that SAA1+ epithelial cells promote distant metastasis of esophageal squamous cell carcinoma

Introduction

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers globally, with significant cell heterogeneity and poor prognosis. Distant metastasis in ESCC is one of the key factors that affects the prognosis of patients.

Methods and results

Starting with the analysis of ESCC single-cell sequencing data, we constructed a single-cell atlas of ESCC in detail and clarified the cell heterogeneity within tumor tissues. Through analysis of epithelial-mesenchymal transition (EMT) levels, gene expression, and pathway activation, we revealed the existence of a novel subpopulation of SAA1+ malignant cells in ESCC that are highly aggressive and closely associated with distant metastasis of ESCC. In vitro wound healing and transwell assays confirmed a strong invasion capacity of ESCC tumor cells with high expression of SAA1. Then, we constructed an effective and reliable prediction model based on the gene expression pattern of SAA1+ malignant cell subpopulations and confirmed that patients in the high-risk group had significantly worse prognosis than those in the low-risk group in the training cohort, internal verification cohort and external verification cohort.

Discussion

This manuscript contributes to exploration of the heterogeneity of ESCC tumor tissues and the search for new ESCC subpopulations with special biological functions. These results contribute to our understanding of the underlying mechanisms of distant metastasis of ESCC and thus provide a theoretical basis for improved therapies.

EMILIN2 is associated with prognosis and immunotherapy in clear cell renal cell carcinoma

Background: EMILIN2 is a platelet-associated elastin that regulates angiogenesis. It has recently been found to play an essential role in various tumors. Nevertheless, the mechanism of action of EMILIN2 in clear cell renal cell carcinoma (ccRCC) remains unclear. Methods: Samples from 33 cancers were obtained from UCSC Xena and The Cancer Genome Atlas (TCGA) database. The relationship between EMILIN2 expression and the clinicopathological characteristics and immune infiltration of ccRCC was investigated. Nonnegative matrix factorization (NMF) was used to classify ccRCC patients. A multigene risk prediction model of ccRCC was constructed using LASSO regression and multivariate regression analysis. A nomogram survival probability prediction map and calibration curve were constructed based on clinical information. Results: EMILIN2 is significantly overexpressed in ccRCC, a phenomenon that is associated with poor prognosis. Meanwhile, EMILIN2 expression is closely related to tumor immune infiltration in ccRCC. Patients with clear cell renal cell carcinoma were divided into two subtypes using NMF, with subtype 2 showed poor prognosis. Next, we established a risk score model for ccRCC based on the common differentially expressed genes (DEGs) between subtypes and groups based on EMILIN2 expression. The results indicated poor prognosis in the high-risk group in the training set and were confirmed in the validation set. Conclusion: Our findings suggest that EMILIN2 expression is closely associated with immune infiltration in ccRCC. EMILIN2 expression is negatively correlated with the prognosis of ccRCC patients. Here, we developed a tool that could predict the prognosis of ccRCC patients.

Single-cell transcriptomic analysis highlights origin and pathological process of human endometrioid endometrial carcinoma

Endometrial cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states. Current models do not adequately reflect oncogenic origin and pathological progression in patients. Here we use single-cell RNA sequencing to profile cells from normal endometrium, atypical endometrial hyperplasia, and endometrioid endometrial cancer (EEC), which altogether represent the step-by-step development of endometrial cancer. We find that EEC originates from endometrial epithelial cells but not stromal cells, and unciliated glandular epithelium is the source of EEC. We also identify LCN2 + /SAA1/2 + cells as a featured subpopulation of endometrial tumorigenesis. Finally, the stromal niche and immune environment changes during EEC progression are described. This study elucidates the evolution of cell populations in EEC development at single-cell resolution, which would provide a direction to facilitate EEC research and diagnosis.

A novel prognostic model based on six methylation-driven genes predicts overall survival for patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a lethal urological malignancy. DNA methylation is involved in the regulation of ccRCC occurrence and progression. This study aimed to establish a prognostic model based on DNA methylation to predict the overall survival (OS) of patients with ccRCC. To create this model, we used the transcriptome and DNA methylation data of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then used the MethylMix R package to identify methylation-driven genes, and LASSO regression and multivariate Cox regression analyses established the prognostic risk model, from which we derived risk scores. We incorporated these risk scores and clinical parameters to develop a prognostic nomogram to predict 3-, 5-, and 7-year overall survival, and its predictive power was validated using the ArrayExpress cohort. These analyses identified six methylation-driven genes (SAA1, FUT6, SPATA18, SHROOM3, AJAP1, and NPEPL1) that produced risk scores, which were sorted into high- and low-risk patient groups. These two groups differed in nomogram-predicted prognosis, the extent of immune cell infiltration, tumor mutational burden, and expected response to additional therapies. In conclusion, we established a nomogram based on six DNA methylation-driven genes with excellent accuracy for prognostic prediction in ccRCC patients. This nomogram model might provide novel insights into the epigenetic mechanism and individualized treatment of ccRCC.

Serum proteomics unveil characteristic protein diagnostic biomarkers and signaling pathways in patients with esophageal squamous cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is a common digestive tract malignant tumor with high incidence and dismal prognosis worldwide. However, the reliable biomarkers for clinical diagnosis and the underlying signaling pathways insights of ESCC are not unequivocally understood yet. The serum proteome may provide valuable clues for the early diagnosis of ESCC and the discovery of novel molecular insights.

Methods

In the current study, an optimized proteomics approach was employed to discover novel serum-based biomarkers for ESCC, and unveil abnormal signal pathways. Gene ontology (GO) enrichment analysis was done by Gene Set Enrichment Analysis (GSEA) and Metascape database, respectively. Pathway analysis was accomplished by GeneCards database. The correlation coefficient was assessed using Pearson and distance correlation analyses. Prioritized candidates were further verified in two independent validation sets by enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) staining.

Results

A total of 633 non-redundant proteins were identified in the serum of patients with ESCC, of which 59 and 10 proteins displayed a more than 1.5-fold increase or decrease compared with healthy controls. Verification was performed for six candidate biomarkers, including S100A8/A9, SAA1, ENO1, TPI1 and PGAM1. Receiver operating characteristics (ROC) curve plotting showed the high diagnostic sensitivity and specificity of these six protein molecules as a biomarker panel: the area under characteristic curve (AUC) is up to 0.945. Differentially expressed proteins were subjected to functional enrichment analysis, which revealed the dysregulation of signaling pathways mainly involved in glycolysis, TLR4, HIF-1α, Cori cycle, TCA cycle, folate metabolism, and platelet degranulation. The latter finding was all the more noteworthy as a strong positive correlation was discovered between activated glycolysis and TLR4 pathways and unfavorable clinicopathological TNM stages in ESCC.

Conclusions

Our findings propose a potential serum biomarker panel for the early detection and diagnosis of ESCC, which could potentially broaden insights into the characteristics of ESCC from the proteomic perspective.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-022-09357-x.

ZNF668: a new diagnostic predictor of kidney renal clear cell carcinoma

The most common pathological subtype of renal carcinoma is RCC, and its development is closely related to immune infiltration. In our study, we investigated the relationship between zinc finger protein 668 and the prognostic risk, clinical characteristics, overall survival and related pathways. We analyzed the association between ZNF668 and immune cell infiltration through the TIMER database. The results showed that the expression of ZNF668 in RCC was higher than that in normal tissues (P < 0.001). The high expression of ZNF668 is clinically relevant, such as tumor stage (P = 0.001) and TNM classification (T: P = 7.37 e-04; N: P = 0.008; M: P < 0.001). Survival analysis showed that patients with high ZNF668 expression had a significantly poor prognosis (P = 0.023). Univariate analysis showed a significant decrease in overall survival in RCC patients with high ZNF668 expression (P = 0.023). Immuno-cell infiltration showed a significant decrease in CD4+ T cell and dendritic cell infiltration in RCC patients with high expression of ZNF668. GO/KEGG analysis showed that multiple pathways were differentially enriched in the high expression pathway of ZNF668, such as complement activation, and estrogen signaling pathway. In conclusion, high ZNF668 expression is a predictor in RCC.