A computational framework to trace tumor tissue-of-origin of 19 cancer types based on RNA sequencing

Abstract

Carcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. Most CUP patients have poor prognosis since no therapy targeting TOO is allowed. Thus, it’s critical to develop accurate computational methods to infer TOO. While qPCR or microarray-based methods are effective in predicting TOO for most cancer types, the overall prediction accuracy is yet to be improved. Here, we propose a computational framework to trace TOO of 19 cancer types based on RNA sequencing (RNA-seq). Specifically, we download the RNA-seq data of 7000+ tissue samples covering 19 cancer types with known TOO from TCGA. By feature selection, 90 genes are finally selected to train a random forest model for TOO inference; the 90 genes are enriched in both tissue-specific functions and tissue-general functions. The cross-validation accuracy of our framework reaches 97.55% across all cancer types. Furthermore, we collected an independent cohort of samples in GEO as testing samples. The accuracy on the independent data is 74% despite the differences in experiment procedures and pipelines. In conclusion, we develop an accurate yet robust computational framework for identifying TOO, which might be promising in clinical applications.

Identification of novel cuproptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of breast cancer patients

“…Although the mortality rate of BC has decreased due to the early detection and advanced treatment, the morbidity rate continues to increase annually ( 31 – 33 ). The role of cuproptosis in cancer was complex.…”

MAGEA11 as a STAD Prognostic Biomarker Associated with Immune Infiltration

“…Therefore, the identification of diagnostic and prognostic markers in gastric cancer has rapidly become a very important subject area. There are many types of diagnostic and prognostic biomarkers for cancers, including gene expression [ 35 ], histopathological images [ 36 , 37 , 38 ], tissue microbes [ 39 ], and tumor microenvironment [ 40 ], among which gene expression might be the most studied. At present, there is no clear research report on the role of MAGEA11 in human gastric cancer, and it is worthy of attention to explore the expression of MAGEA11 and its effect on gastric cancer.…”