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

Benign non-immune cells in tumor microenvironment

The tumor microenvironment (TME) is a highly complex and continuous evolving ecosystem, consisting of a diverse array of cellular and non-cellular components. Among these, benign non-immune cells, including cancer-associated fibroblasts (CAFs), adipocytes, endothelial cells (ECs), pericytes (PCs), Schwann cells (SCs) and others, are crucial factors for tumor development. Benign non-immune cells within the TME interact with both tumor cells and immune cells. These interactions contribute to tumor progression through both direct contact and indirect communication. Numerous studies have highlighted the role that benign non-immune cells exert on tumor progression and potential tumor-promoting mechanisms via multiple signaling pathways and factors. However, these benign non-immune cells may play different roles across cancer types. Therefore, it is important to understand the potential roles of benign non-immune cells within the TME based on tumor heterogeneity. A deep understanding allows us to develop novel cancer therapies by targeting these cells. In this review, we will introduce several types of benign non-immune cells that exert on different cancer types according to tumor heterogeneity and their roles in the TME.

Deciphering the generation of heterogeneity in esophageal squamous cell carcinoma metastasis via single-cell multiomics analysis

BACKGROUND

Chromatin accessibility plays a crucial role in mediating transcriptional dysregulation and heterogeneity in Esophageal Squamous Cell Carcinoma (ESCC). Examining the chromatin accessibility of ESCC at single-cell level is imperative to understand how it activates oncogenes and contributes to the onset and metastasis of ESCC.

METHODS

We performed single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) on cancerous and adjacent noncancerous tissues from four ESCC patients who were pathological staged as T1a, T2b, T3b, or T4a, to investigate whether regulatory elements are pivotal in instigating cellular heterogeneity during ESCC metastasis. In conjunction, we integrated these data with 55 scRNA-seq datasets, ChIP-seq or CUT&Tag sequencing data, Hi-C sequencing data, bulk RNA-seq data, and bulk ATAC-seq data from ESCC cell lines to dissect the mechanisms underlying the heterogeneity of ESCC and tumor microenvironment (TME).

RESULTS

Our study identified enhancer-specific activation within epithelial cells orchestrated by the three-dimensional structure of chromatin that regulates SERPINH1 transcription, and promotes the epithelial-mesenchymal transition (EMT) and metastasis of ESCC. Additionally, chromatin element activation facilitated the expression of TNFSF4 in CD8 + exhausted T cells, thereby activating Tregs. Furthermore, we observed that chromatin accessibility promoted the differentiation of tumor-associated macrophages (TAMs) and cancer associated fibroblasts (CAFs).

CONCLUSIONS

In summary, utilizing multiomics analyses, we have revealed chromatin accessibility maps and illuminated the intricate molecular mechanisms that underlie cellular heterogeneity during ESCC metastasis, offering valuable insights to further advance research on tumor progression and deterioration.

Blocking SLC7A11 attenuates the proliferation of esophageal squamous cell carcinoma cells

The role of ferroptosis-associated gene SLC7A11 in esophageal cancer progression is largely unknown, therefore, the effects of blocking SLC7A11 on esophageal squamous cell carcinoma (ESCC) cells are evaluated. Results showed that SLC7A11 was overexpressed in ESCC tissues both in mRNA and protein levels. Blocking SLC7A11 using Erastin suppressed the proliferation and colony formation of ESCC cells, decreased cellular ATP levels, and improved ROS production. Sixty-three SLC7A11-binding proteins were identified using the IP-MS method, and these proteins were enriched in four signaling pathways, including spliceosome, ribosome, huntington disease, and diabetic cardiomyopathy. The deubiquitinase inhibitors PR-619, GRL0617, and P 22077 could reduce at least 40% protein expression level of SLC7A11 in ESCC cells, and PR-619 and GRL0617 exhibited suppressive effects on the cell viability and colony formation ability of KYSE30 cells, respectively. Erastin downregulated GPX4 and DHODH and also reduced the levels of β-catenin, p-STAT3, and IL-6 in ESCC cells. In conclusion, SLC7A11 was overexpressed in ESCC, and blocking SLC7A11 using Erastin mitigated malignant phenotypes of ESCC cells and downregulated key ferroptosis-associated molecules GPX4 and DHODH. The therapeutic potential of targeting SLC7A11 should be further evaluated in the future.

Single-cell transcriptomic analysis reveals the landscape of epithelial-mesenchymal transition molecular heterogeneity in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a prevalent and highly lethal malignant disease. The epithelial-mesenchymal transition (EMT) is crucial in promoting ESCC development. However, the molecular heterogeneity of ESCC and the potential inhibitory strategies targeting EMT remain poorly understood. In this study, we analyzed high-resolution single-cell transcriptome data encompassing 209,231 ESCC cells from 39 tumor samples and 16 adjacent samples obtained from 44 individuals. We identified distinct cell populations exhibiting heterogeneous EMT characteristics and identified 87 EMT-associated molecules. The expression profiles of these EMT-associated molecules showed heterogeneity across different stages of ESCC progression. Moreover, we observed that EMT primarily occurred in early-stage tumors, before lymph node metastasis, and significantly promoted the rapid deterioration of ESCC. Notably, we identified SERPINH1 as a potential novel marker for ESCC EMT. By classifying ESCC patients based on EMT gene sets, we found that those with high EMT exhibited poorer prognosis. Furthermore, we predicted and experimentally validated drugs targeting ESCC EMT, including dactolisib, docetaxel, and nutlin, which demonstrated efficacy in inhibiting EMT and metastasis in ESCC. Through the integration of scRNA-seq, RNA-seq, and TCGA data with experimental validation, our comprehensive analysis elucidated the landscape of EMT during the entire course of ESCC development and metastasis. These findings provide valuable insights and a reference for refining ESCC clinical treatment strategies.

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.

Single-cell transcriptomics reveals heterogeneity in esophageal squamous epithelial cells and constructs models for predicting patient prognosis and immunotherapy

Background

Esophageal squamous cell carcinoma (ESCC), characterized by its high invasiveness and malignant potential, has long been a formidable challenge in terms of treatment.

Methods

A variety of advanced analytical techniques are employed, including single-cell RNA sequencing (scRNA-seq), cell trajectory inference, transcription factor regulatory network analysis, GSVA enrichment analysis, mutation profile construction, and the inference of potential immunotherapeutic drugs. The purpose is to conduct a more comprehensive exploration of the heterogeneity among malignant squamous epithelial cell subgroups within the ESCC microenvironment and establish a model for predicting the prognosis and immunotherapy outcomes of ESCC patients.

Results

An analysis was conducted through scRNA-seq, and three Cluster of malignant epithelial cells were identified using the infer CNV method. Cluster 0 was found to exhibit high invasiveness, whereas Cluster 1 displayed prominent characteristics associated with epithelial-mesenchymal transition. Confirmation of these findings was provided through cell trajectory analysis, which positioned Cluster 0 at the initiation stage of development and Cluster 1 at the final developmental stage. The abundance of Cluster 0-2 groups in TCGA-LUAD samples was assessed using ssGSEA and subsequently categorized into high and low-expression groups. Notably, it was observed that Cluster 0-1 had a significant impact on survival (p<0.05). Furthermore, GSVA enrichment analysis demonstrated heightened activity in hallmark pathways for Cluster 0, whereas Cluster 1 exhibited notable enrichment in pathways related to cell proliferation. It is noteworthy that a prognostic model was established utilizing feature genes from Cluster 0-1, employing the Lasso and stepwise regression methods. The results revealed that in TCGA and GSE53624 cohorts, the low-risk group demonstrated significantly higher overall survival and increased levels of immune infiltration. An examination of four external immunotherapy cohorts unveiled that the low-risk group exhibited improved immunotherapeutic efficacy. Additionally, more meaningful treatment options were identified for the low-risk group.

Conclusion

The findings revealed distinct interactions between malignant epithelial cells of ESCC and subgroups within the tumor microenvironment. Two cell clusters, strongly linked to survival, were pinpointed, and a signature was formulated. This signature is expected to play a crucial role in identifying and advancing precision medicine approaches for the treatment of ESCC.

SAA1 identified as a potential prediction biomarker for metastasis of hepatocellular carcinoma via multi-omics approaches

Background

Metastasis is the major cause of high recurrence and mortality of hepatocellular carcinoma (HCC). Unfortunately, there are few reports on effective biomarkers of HCC metastasis. Previous studies have reported that SAA1 may be a predictor and prognostic biomarker for multiple malignant tumors. However, the role of SAA1 in HCC has not yet been investigated.

Methods

We applied RNA sequencing and proteomics analysis to investigate the expression landscape of HCC cell lines and patient serum, respectively. SAA1 is a common key gene and listed as a candidate biomarker of HCC metastasis. It was validated in two cell lines, 107 participants serum, and 63 matched HCC and adjacent non-tumorous liver tissues. Human Protein Atlas (HPA), Genotype-Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) datasets were integrated to explore SAA1 expression among various cell types and organs. The diagnostic and prognostic value of SAA1 in HCC were determined through receiver operating characteristic (ROC) and Kaplan-Meier curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network were constructed for SAA1, as well as for its co-expressed genes. We further analyzed the correlation between SAA1 and co-expression genes.

Results

We found 7 differentially expressed genes (DEGs) and 14 differentially expressed proteins (DEPs) were related to HCC metastasis. SAA1, a key candidate biomarker, was highly enriched in hepatocytes and liver organ, and it was also highly expressed in HCC cells and the serum and tissues of HCC patients. The results of ROC curve analysis indicated that SAA1 had better predictive values for distinguishing HCC metastasis from non-metastasis. Kaplan-Meier curve analysis revealed that HCC patients with higher SAA1 expression had worse overall survival.

Conclusions

Our findings provide new insights into HCC metastasis by identifying candidate gene prediction biomarkers for HCC metastasis.