Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach’s ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

The tumor microenvironment is a highly complex ecosystem of diverse cell types, which shape cancer biology and impact the responsiveness to therapy. Here, we analyze the microenvironment of esophageal squamous cell carcinoma (ESCC) using single-cell transcriptome sequencing in 62,161 cells from blood, adjacent nonmalignant and matched tumor samples from 11 ESCC patients. We uncover heterogeneity in most cell types of the ESCC stroma, particularly in the fibroblast and immune cell compartments. We identify a tumor-specific subset of CST1⁺ myofibroblasts with prognostic values and potential biological significance. CST1⁺ myofibroblasts are also highly tumor-specific in other cancer types. Additionally, a subset of antigen-presenting fibroblasts is revealed and validated. Analyses of myeloid and T lymphoid lineages highlight the immunosuppressive nature of the ESCC microenvironment, and identify cancer-specific expression of immune checkpoint inhibitors. This work establishes a rich resource of stromal cell types of the ESCC microenvironment for further understanding of ESCC biology.

Squamous cell carcinomas (SCCs) are aggressive malignancies. Previous report demonstrated that master transcription factors (TFs) TP63 and SOX2 exhibited overlapping genomic occupancy in SCCs. However, functional consequence of their frequent co-localization at super-enhancers remains incompletely understood. Here, epigenomic profilings of different types of SCCs reveal that TP63 and SOX2 cooperatively and lineage-specifically regulate long non-coding RNA (lncRNA) CCAT1 expression, through activation of its super-enhancers and promoter. Silencing of CCAT1 substantially reduces cellular growth both in vitro and in vivo, phenotyping the effect of inhibiting either TP63 or SOX2. ChIRP analysis shows that CCAT1 forms a complex with TP63 and SOX2, which regulates EGFR expression by binding to the super-enhancers of EGFR, thereby activating both MEK/ERK1/2 and PI3K/AKT signaling pathways. These results together identify a SCC-specific DNA/RNA/protein complex which activates TP63/SOX2-CCAT1-EGFR cascade and promotes SCC tumorigenesis, advancing our understanding of transcription dysregulation in cancer biology mediated by master TFs and super-enhancers.

Long non-coding RNAs (lncRNAs) are expressed in tissue-specific pattern, but it is not clear how these are regulated. We aimed to identify squamous cell carcinoma (SCC)-specific lncRNAs and investigate mechanisms that control their expression and function. We studied expression patterns and functions of 4 SCC-specific lncRNAs. We obtained 113 esophageal SCC (ESCC) and matched non-tumor esophageal tissues from a hospital in Shantou City, China, and performed quantitative reverse transcription PCR assays to measure expression levels of LINC01503. We collected clinical data from patients and compared expression levels with survival times. LINC01503 was knocked down using small interfering RNAs and oligonucleotides in TE7, TE5 and KYSE510 cell lines and overexpressed in KYSE30 cells. Cells were analyzed by chromatin immunoprecipitation sequencing, luciferase reporter assays, colony formation, migration and invasion, and mass spectrometry analyses. Cells were injected into nude mice and growth of xenograft tumors was measured. LINC01503 interaction with proteins was studied using fluorescence in situ hybridization, RNA pulldown, and RNA immunoprecipitation analyses. We identified a lncRNA, LINC01503, which is regulated by a super-enhancer and is expressed at significantly higher levels in esophageal and head and neck SCCs than in non-tumor tissues. High levels in SCCs correlated with shorter survival times of patients. The transcription factor TP63 bound to the super-enhancer at the LINC01503 locus and activated its transcription. Expression of LINC01503 in ESCC cell lines increased their proliferation, colony formation, migration and invasion. Knockdown of LINC01503 in SCC cells reduced their proliferation, colony formation, migration and invasion, and growth of xenograft tumors in nude mice. Expression of LINC01503 in ESCC cell lines reduced ERK2 dephosphorylation by DUSP6, leading to activation of ERK signaling via MAPK. LINC01503 disrupted the interaction between EBP1 and the p85 subunit of PI3K, increasing AKT signaling. We identified a lncRNA, LINC01503, that is increased in SCC cells compared with non-tumor cells. Increased expression of LINC01503 promotes ESCC cell proliferation, migration, invasion, and growth of xenograft tumors. It might be developed as a biomarker of aggressive SCCs in patients.

Esophageal cancer (EC) is a type of aggressive cancer without clinically relevant molecular subtypes, hindering the development of effective strategies for treatment. To define molecular subtypes of EC, we perform mass spectrometry-based proteomic and phosphoproteomics profiling of EC tumors and adjacent non-tumor tissues, revealing a catalog of proteins and phosphosites that are dysregulated in ECs. The EC cohort is stratified into two molecular subtypes—S1 and S2—based on proteomic analysis, with the S2 subtype characterized by the upregulation of spliceosomal and ribosomal proteins, and being more aggressive. Moreover, we identify a subtype signature composed of ELOA and SCAF4, and construct a subtype diagnostic and prognostic model. Potential drugs are predicted for treating patients of S2 subtype, and three candidate drugs are validated to inhibit EC. Taken together, our proteomic analysis define molecular subtypes of EC, thus providing a potential therapeutic outlook for improving disease outcomes in patients with EC.