Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

10.0K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
10.0K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

3.7K
3.7K
Ribosome Profiling02:24

Ribosome Profiling

4.2K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
4.2K
MicroRNAs01:22

MicroRNAs

4.1K
MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
4.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identifying Pediatric Long COVID: Comparing an EHR Algorithm to Manual Review.

Applied clinical informatics·2025
Same author

COVID-19 vaccination knowledge, attitudes, and practices within a majority Hispanic/Latino pediatric healthcare system.

Journal of pediatric nursing·2025
Same author

The impact of clinical genome sequencing in a global population with suspected rare genetic disease.

American journal of human genetics·2024
Same author

EHR-based Case Identification of Pediatric Long COVID: A Report from the RECOVER EHR Cohort.

medRxiv : the preprint server for health sciences·2024
Same author

Feasibility of functional precision medicine for guiding treatment of relapsed or refractory pediatric cancers.

Nature medicine·2024
Same author

De novo variants in GABRA4 are associated with a neurological phenotype including developmental delay, behavioral abnormalities and epilepsy.

European journal of human genetics : EJHG·2024

Related Experiment Video

Updated: Feb 16, 2026

RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma
10:26

RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma

Published on: March 11, 2014

28.3K

Non-coding RNAs profiling in head and neck cancers.

Daria Salyakina1, Nicholas F Tsinoremas1,2

  • 1Center for Computational Science, University of Miami, Coral Gables, FL, USA.

NPJ Genomic Medicine
|December 22, 2017
PubMed
Summary
This summary is machine-generated.

Non-coding RNAs (ncRNAs) are crucial in head and neck squamous cell carcinomas (HNSCs). This study reveals ncRNAs are upregulated in tumors, particularly HPV16-positive ones, and target key cancer genes.

More Related Videos

Laser-capture Microdissection of Human Prostatic Epithelium for RNA Analysis
07:42

Laser-capture Microdissection of Human Prostatic Epithelium for RNA Analysis

Published on: November 26, 2015

13.9K
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

17.2K

Related Experiment Videos

Last Updated: Feb 16, 2026

RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma
10:26

RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma

Published on: March 11, 2014

28.3K
Laser-capture Microdissection of Human Prostatic Epithelium for RNA Analysis
07:42

Laser-capture Microdissection of Human Prostatic Epithelium for RNA Analysis

Published on: November 26, 2015

13.9K
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

17.2K

Area of Science:

  • Genomics
  • Molecular Biology
  • Cancer Research

Background:

  • Most cancer studies focus on coding genes, but non-coding RNAs (ncRNAs) are increasingly recognized as key regulators.
  • Head and neck squamous cell carcinomas (HNSCs) offer a unique model to study ncRNA roles, especially concerning human papillomavirus (HPV) oncogenesis.

Purpose of the Study:

  • To characterize the ncRNA landscape in 442 HNSC tumors from The Cancer Genome Atlas (TCGA).
  • To compare ncRNA expression between HPV16-positive (HPV16+) and HPV-negative (HPV-) HNSC tumors.
  • To identify potential regulatory targets of differentially expressed ncRNAs in HNSC.

Main Methods:

  • Analysis of ncRNA expression profiles in 442 HNSC tumors.
  • Comparison of ncRNA expression based on HPV16 status.
  • Identification of potential regulatory targets for differentially expressed ncRNAs.

Main Results:

  • ncRNAs constitute 36% of all differentially expressed genes in HNSCs, with antisense RNAs being the most abundant type.
  • ncRNAs are significantly upregulated in HNSC tumors, especially in HPV16+ tumors, contrasting with downregulated protein-coding genes.
  • Pseudogenes, antisense RNAs, and short RNAs are elevated in HPV16+ tumors, while other long non-coding RNAs are upregulated across all HNSCs regardless of HPV status.
  • Putative regulatory targets of ncRNAs include known oncogenes, tumor suppressors, and growth factors, suggesting ncRNA involvement in regulating key cancer pathways due to HPV activity.

Conclusions:

  • ncRNAs are critical transcriptional components in HNSCs.
  • ncRNA expression patterns differ based on HPV status, highlighting their role in HPV-driven oncogenesis.
  • ncRNAs represent a promising area for future cancer research and therapeutic development.