Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived from transcriptome analysis in pancreatic cancer

Abstract

Radiogenomics has attracted attention for predicting the molecular biological characteristics of tumors from clinical images, which are originally a collection of numerical values, such as computed tomography (CT) scans. A prediction model using genetic information is constructed using thousands of image features extracted and calculated from these numerical values. In the present study, RNA sequencing of pancreatic ductal adenocarcinoma (PDAC) tissues from 12 patients was performed to identify genes useful in evaluating clinical pathology, and 107 PDAC samples were immunostained to verify the obtained findings. In addition, radiogenomics analysis of gene expression was performed by machine learning using CT images and constructed prediction models. Bioinformatics analysis of RNA sequencing data identified integrin αV (ITGAV) as being important for clinicopathological factors, such as metastasis and prognosis, and the results of sequencing and immunostaining demonstrated a significant correlation (r=0.625, P=0.039). Notably, the ITGAV high-expression group was associated with a significantly worse prognosis (P=0.005) and recurrence rate (P=0.003) compared with the low-expression group. The ITGAV prediction model showed some detectability (AUC=0.697), and the predicted ITGAV high-expression group was also associated with a worse prognosis (P=0.048). In conclusion, radiogenomics predicted the expression of ITGAV in pancreatic cancer, as well as the prognosis.

Single-cell and spatial transcriptomic analyses reveal the cellular origins and drivers of liver metastasis from pancreatic cancer

“…Moreover, ITGAV was differentially expressed among three immune cell in ltration subtypes of metastatic pancreatic neuroendocrine tumors 33 . In pancreatic cancer, ITGAV could affect the prognosis and clinicopathological factors of tumor metastasis, and the patients with high ITGAV expression exhibited inferior prognosis and recurrence rate compared to patients with low ITGAV expression 34 . In addition, by upregulating the expression of ITGAV, A1B1 can promote ECM signal transduction, thereby accelerating progression of PDAC 35 .…”

ITGAV overexpression predicts poor prognosis in gastric cancer

“…In recent years, elevated ITGAV expression was reported to be closely related to tumor angiogenesis, and might play vital role in tumor progression and metastasis 17,18 . It was detected highly expressed in the tumor patients with hepatocellular cancer 18 ,esophageal adenocarcinoma 6 , osteosarcoma 19 ,pancreatic cancer 20,21 as well as gastric cancer 22 .…”

Artificial intelligence in pancreatic cancer

“…The gene expression profiles obtained in radiogenomics can be used as biomarkers to predict prognosis 241 . With the aid of ML, the radiologist used radiological images (CT, MRI) to detect multiple gene expression profiles in PC, including p53 status and PD-L1 expression 204 , FAP expression 205 , and ITGAV expression 206 . These genes had been shown to have predictive ability for the prognosis of PC.…”

“…These facts make it challenging to predict the prognosis of PC. Due to its excellent computational power, AI was used to analyze PC prognoses, including survival time [204][205][206][207][208][209][210][211][212][213][214][215][216][217][218][219][220][221], recurrence risk [78,[221][222][223][224], metastasis [225][226][227][228][229][230], therapy response [79][80][81][231][232][233][234][235][236][237][238][239][240], etc.…”