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RNA-seq profiling of salivary transcriptome reveals potential diagnostic RNAs and oral microbial dysbiosis in oral squamous cell carcinoma

GSE176077 Homo sapiens (human) Non-coding RNA profiling 28 samples Submitted 2023/01/30 Platform GPL17303
At a glance

Non-coding RNA profiling · human · 28 samples

What it is
Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism
Homo sapiens (human)
Topic
Head and neck cancerThis series is part of our curated briefing on it.
Samples
28 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Background: The differential abundance of cell-free RNAs in bodily fluids is emerging as a promising tool for the non-invasive molecular diagnosis of cancer. Human saliva is considered a promising source of non-invasive biomarkers of diagnostic value for oral cancer detection. This study aims to identify diagnostic potent salivary RNAs in oral squamous cell carcinoma (OSCC)-patients by RNA-Sequencing.
Read the full summary ↓
Method: Unstimulated saliva was collected from 5 normal control (NC) individuals and 9 OSCC patients (PS) with prior consent and ethical committee approvals. Total RNA isolated from cell-free saliva (CFS) supernatant was used to prepare small RNA libraries and sequenced on the Ion Torrent S5 platform. The sequencing reads were aligned to the human genome (hg19) using Bowtie 2, and the differential expression analysis was performed using RUVSeq and DESeq2. Mapped reads were screened across miRBase (v22) annotations for miRNAs and Gencode (v19) annotation for other RNAs. Reads were quantified by the Featurecount (v1.4.6) module of the R-package. The microbial-RNA enrichment analysis was determined using the One Codex platform. Result: RNA-sequencing detected protein-coding transcripts (PCTs), long-intergenic RNAs (lincRNAs), microRNAs (miRNAs), small nuclear RNAs (snRNAs), transfer RNAs (tRNAs) and pseudogenes from the saliva of PS and HC samples. Transcriptome analyses revealed 89 PCTs, 18 lincRNAs and 6 miRNAs differentially expressed between PS and HC with a log2fold change ≥ 1 or ≤ -1 and p-value < 0.05. Gene ontology and pathway enrichment analyses indicated a significant correlation of the identified PCTs and miRNAs to various cancer-related pathways that may have implications in the pathogenesis of OSCC. Interestingly, unmapped non-human reads aligned to the microbial reference genomes. Further analyses of these microbial sequence reads revealed a significant microbial dysbiosis differentiating PS from HC. Metabolic pathways and functional analysis of the identified microbial phylotypes showed gene ontologies associated with inflammation, cell proliferation, ROS generation, and a range of metabolic processes. Conclusion: We report novel panels of differentially expressed PCTs, miRNAs and lincRNAs distinguishing PS from HC. Importantly, our results also provide evidence for oral microbial dysbiosis that appears to have pathological implications in OSCC. Summarily, this study provides a comprehensive landscape of salivary RNAs that can be exploited as non-invasive biomarkers for OSCC detection.
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE176077_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Also filed as BioProject PRJNA734811 and SRA study SRP322426. Searching any of these in the dataset finder brings you back here.

Samples in this study

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