In-silico, interactomic and clinical validation based approach for screening and identification of miR biomarkers involved in Oral submucous fibrosis to Oral squamous cell carcinoma transition
ABSTRACT Oral Squamous Cell Carcinoma (OSCC) is common preventable disease when diagnosed early, but mostly its progression follows transition from oral potentially malignant disorders (OPMDs) like Oral Submucous Fibrosis (OSF). However, it is difficult to predict possibilities of progression in these premalignant lesions hence, identification of molecular biomarkers would have major clinical impact in early diagnosis and better prognosis. In this context microRNA’s(miR’s) provide better opportunities in malignancy prediction and demarcation in OSF to OSCC transition as they perform key regulatory roles in many tumorigenic processes. Here, we computationally screened differentially expressed miR’s of OSCC and OSF from public databases followed by construction of protein interaction networks and enrichment analyses. The relevant miR’s were validated using qPCR of total 93 samplesincluding 34 OSCC, 30 OSF and 29 control blood and tissue samples. We identified significant down regulation of miR-133a-3p in OSCC compared to controls and interesting up-regulation compared to OSCC and control. miR-9-5p was up-regulated in OSF as well as OSCC and down-regulated in OSF compared to OSCC. Therefore, these two miR’s may serve as risk stratification biomarkers with validation in larger categorical datasets..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 09. Okt. Zur Gesamtaufnahme - year:2023 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ukey, Shweta [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| Themen: |
|---|
| doi: |
10.1101/2020.10.05.20206904 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XBI01907834X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XBI01907834X | ||
| 003 | DE-627 | ||
| 005 | 20231205150441.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201010s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1101/2020.10.05.20206904 |2 doi | |
| 035 | |a (DE-627)XBI01907834X | ||
| 035 | |a (biorXiv)10.1101/2020.10.05.20206904 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ukey, Shweta |e verfasserin |0 (orcid)0000-0002-1272-4845 |4 aut | |
| 245 | 1 | 0 | |a In-silico, interactomic and clinical validation based approach for screening and identification of miR biomarkers involved in Oral submucous fibrosis to Oral squamous cell carcinoma transition |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a ABSTRACT Oral Squamous Cell Carcinoma (OSCC) is common preventable disease when diagnosed early, but mostly its progression follows transition from oral potentially malignant disorders (OPMDs) like Oral Submucous Fibrosis (OSF). However, it is difficult to predict possibilities of progression in these premalignant lesions hence, identification of molecular biomarkers would have major clinical impact in early diagnosis and better prognosis. In this context microRNA’s(miR’s) provide better opportunities in malignancy prediction and demarcation in OSF to OSCC transition as they perform key regulatory roles in many tumorigenic processes. Here, we computationally screened differentially expressed miR’s of OSCC and OSF from public databases followed by construction of protein interaction networks and enrichment analyses. The relevant miR’s were validated using qPCR of total 93 samplesincluding 34 OSCC, 30 OSF and 29 control blood and tissue samples. We identified significant down regulation of miR-133a-3p in OSCC compared to controls and interesting up-regulation compared to OSCC and control. miR-9-5p was up-regulated in OSF as well as OSCC and down-regulated in OSF compared to OSCC. Therefore, these two miR’s may serve as risk stratification biomarkers with validation in larger categorical datasets. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Vishnoi, Jeewan Ram |4 aut | |
| 700 | 1 | |a Choudhury, Chinmayee |4 aut | |
| 700 | 1 | |a Purohit, Purvi |4 aut | |
| 700 | 1 | |a Dwivedi, Shailendra |4 aut | |
| 700 | 1 | |a jain, Ankit |4 aut | |
| 700 | 1 | |a Chugh, Ankita |4 aut | |
| 700 | 1 | |a Elhence, Poonam |4 aut | |
| 700 | 1 | |a Pareek, Puneet |4 aut | |
| 700 | 1 | |a Misra, Sanjeev |4 aut | |
| 700 | 1 | |a Sharma, Praveen |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2023) vom: 09. Okt. |
| 773 | 1 | 8 | |g year:2023 |g day:09 |g month:10 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/2020.10.05.20206904 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XBI | ||
| 951 | |a AR | ||
| 952 | |j 2023 |b 09 |c 10 | ||