Prognostic targets recognition of rectal adenocarcinoma based on transcriptomics

Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images

Colorectal cancer (CRC) is now the third most common malignancy to cause mortality worldwide, and its prognosis is of great importance. Recent CRC prognostic prediction studies mainly focused on biomarkers, radiometric images, and end-to-end deep learning methods, while only a few works paid attention to exploring the relationship between the quantitative morphological features of patients' tissue slides and their prognosis. However, existing few works in this area suffered from the drawback of choosing the cells randomly from the whole slides, which contain the non-tumor region that lakes information about prognosis. In addition, the existing works, which tried to demonstrate their biological interpretability using patients' transcriptome data, failed to show the biological meaning closely related to cancer. In this study, we proposed and evaluated a prognostic model using morphological features of cells in the tumor region. The features were first extracted by the software CellProfiler from the tumor region selected by Eff-Unet deep learning model. Features from different regions were then averaged for each patient as their representative, and the Lasso-Cox model was used to select the prognosis-related features. The prognostic prediction model was at last constructed using the selected prognosis-related features and was evaluated through KM estimate and cross-validation. In terms of biological meaning, Gene Ontology (GO) enrichment analysis of the expressed genes that correlated with the prognostically significant features was performed to show the biological interpretability of our model.With the help of tumor segmentation, our model achieved better statistical significance and better biological interpretability compared to the results without tumor segmentation. Statistically, the Kaplan Meier (KM) estimate of our model showed that the model using features in the tumor region has a higher C-index, a lower p-value, and a better performance on cross-validation than the model without tumor segmentation. In addition, revealing the pathway of the immune escape and the spread of the tumor, the model with tumor segmentation demonstrated a biological meaning much more related to cancer immunobiology than the model without tumor segmentation. Our prognostic prediction model using quantitive morphological features from tumor regions was almost as good as the TNM tumor staging system as they had a close C-index, and our model can be combined with the TNM tumor stage system to make a better prognostic prediction. And to the best of our knowledge, the biological mechanisms in our study were the most relevant to the immune mechanism of cancer compared to the previous studies.

Foxhead box D1 promotes the partial epithelial-to-mesenchymal transition of laryngeal squamous cell carcinoma cells via transcriptionally activating the expression of zinc finger protein 532

The presence of cervical lymph node metastases has been considered as the most important adverse prognostic factor for patients with laryngeal squamous cell carcinoma (LSCC). However, the underlying mechanisms remain to be fully revealed. In this study, we explored the expression profile of Foxhead box D1 (FOXD1), its association with epithelial-to-mesenchymal transition (EMT), and its downstream targets in LSCC. Bioinformatic analysis was performed based on the LSCC subset of The Cancer Genome Atlas-Head and Neck Squamous Cell Carcinoma (TCGA-HSNC) and Chromatin immunoprecipitation (ChIP)-seq data from Cistrome Data Browser. LSCC cell lines AMC-HN-8 and TU212 were used for in vitro studies. Results showed that FOXD1 upregulation was associated with poor prognosis of LSCC. FOXD1 knockdown reduced N-cadherin and Vimentin expression but increased E-cadherin expression in AMC-HN-8 cells. Its overexpression showed opposite effects in TU212 cells. FOXD1 could bind to the promoter of ZNF532 and activate its transcription. ZNF532 overexpression enhanced the invasion of both AMC-HN-8 and TU212 cells. In comparison, its knockdown significantly impaired their invasion. ZNF532 knockdown nearly abrogated the alterations of EMT markers caused by FOXD1 overexpression. Its overexpression largely rescued the phenotypes caused by FOXD1 knockdown. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that ZNF532 correlated genes are largely enriched in extracellular matrix regulations. LSCC patients with high ZNF532 expression (top 50%) had a significantly worse progression-free survival. In summary, this study confirmed that FOXD1 promotes partial-EMT of LSCC cells via transcriptionally activating the expression of ZNF532.