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

8.5K
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...
8.5K
Abnormal Proliferation02:23

Abnormal Proliferation

4.5K
Under normal conditions, most adult cells remain in a non-proliferative state unless stimulated by internal or external factors to replace lost cells. Abnormal cell proliferation is a condition in which the cell's growth exceeds and is uncoordinated with normal cells. In such situations, cell division persists in the same excessive manner even after cessation of the stimuli, leading to persistent tumors. The tumor arises from the damaged cells that replicate to pass the damage to the...
4.5K
Negative Regulator Molecules01:23

Negative Regulator Molecules

35.2K
Positive regulators allow a cell to advance through cell cycle checkpoints. Negative regulators have an equally important role as they terminate a cell’s progression through the cell cycle—or pause it—until the cell meets specific criteria.
35.2K
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

10.6K
The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
10.6K
The Nucleolus02:55

The Nucleolus

8.7K
The nucleolus is the most prominent substructure of the nucleus. When it was first discovered, it was considered to be an isolated organelle that forms fibrils and granules. In 1931, the relationship between the nucleolus and chromosomes was first described by Heitz. He observed that the appearance and size of nucleolus varies depending on the stage of the cell cycle. He also noticed constricted regions on different chromosomes clustered together at definite cell cycle stages. These regions,...
8.7K
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

875
The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
875
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Regulator Of Nonsense Transcripts 3b Is A Prognostic Biomarker And Associated With Immune Cell Infiltration In Lung Squamous Cell And Hepatocellular Carcinoma

Regulator of nonsense transcripts 3B is a prognostic biomarker and associated with immune cell infiltration in lung squamous cell and hepatocellular carcinoma

Pengcheng Li1, Mi Zhou1, Xiaoli Gan1

  • 1Hepatic Surgery Center, Clinical Medicine Research Centre for Hepatic Surgery of Hubei Province, and Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China.

Discover Oncology
|September 27, 2024

Related Experiment Videos

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
07:23

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Published on: June 15, 2016

8.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

653
Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts
11:19

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

14.9K

View abstract on PubMed

Summary
This summary is machine-generated.

RENT3B expression impacts liver and lung cancer prognosis differently. High RENT3B correlates with poor outcomes in liver cancer but better outcomes in lung cancer, linked to distinct immune infiltration patterns.

Area of Science:

  • Oncology
  • Immunology
  • Genomics

Background:

  • The role of RENT3B in cancer pathogenesis is not well understood.
  • Investigating RENT3B's association with immune infiltration is crucial for understanding tumor behavior.

Purpose of the Study:

  • To explore the relationship between RENT3B expression and immune infiltration in liver hepatocellular carcinoma (LIHC) and lung squamous cell carcinoma (LUSC).
  • To determine RENT3B's potential as a prognostic biomarker in these cancers.

Main Methods:

  • Utilized ONCOMINE and TIMER databases for RENT3B expression analysis.
  • Assessed survival correlations using PrognoScan, GEPIA, and Kaplan-Meier plotter.
  • Examined RENT3B's association with tumor microenvironment immune cells and markers via TIMER.
  • Analyzed prognostic impact within immune cell subgroups and evaluated RENT3B promoter methylation.
Keywords:
Immune infiltrationPan-cancerPrognosis analysisRENT3B

Related Experiment Videos

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
07:23

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Published on: June 15, 2016

8.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

653
Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts
11:19

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

14.9K

Main Results:

  • RENT3B levels were elevated in both LIHC and LUSC.
  • High RENT3B expression correlated with poor prognosis in LIHC but favorable prognosis in LUSC.
  • RENT3B showed differential correlations with immune cell infiltration and markers between LIHC and LUSC.
  • No significant differences in RENT3B promoter methylation were found between tumor and normal tissues.

Conclusions:

  • RENT3B significantly influences tumor prognosis in LIHC and LUSC.
  • RENT3B's association with immune infiltration varies by cancer type.
  • RENT3B emerges as a potential prognostic biomarker for LIHC and LUSC.