Image_1_Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.jpeg

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.</p

Image_2_Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.jpeg

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.</p

Image_5_Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.jpeg

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.</p

Image_4_Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.jpeg

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.</p

Image_3_Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.jpeg

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.</p

Data_Sheet_1_CG and CHG Methylation Contribute to the Transcriptional Control of OsPRR37-Output Genes in Rice.PDF

Plant circadian clock coordinates endogenous transcriptional rhythms with diurnal changes of environmental cues. OsPRR37, a negative component in the rice circadian clock, reportedly regulates transcriptome rhythms, and agronomically important traits. However, the underlying regulatory mechanisms of OsPRR37-output genes remain largely unknown. In this study, whole genome bisulfite sequencing and high-throughput RNA sequencing were applied to verify the role of DNA methylation in the transcriptional control of OsPRR37-output genes. We found that the overexpression of OsPRR37 suppressed rice growth and altered cytosine methylations in CG and CHG sequence contexts in but not the CHH context (H represents A, T, or C). In total, 35 overlapping genes were identified, and 25 of them showed negative correlation between the methylation level and gene expression. The promoter of the hexokinase gene OsHXK1 was hypomethylated at both CG and CHG sites, and the expression of OsHXK1 was significantly increased. Meanwhile, the leaf starch content was consistently lower in OsPRR37 overexpression lines than in the recipient parent Guangluai 4. Further analysis with published data of time-course transcriptomes revealed that most overlapping genes showed peak expression phases from dusk to dawn. The genes involved in DNA methylation, methylation maintenance, and DNA demethylation were found to be actively expressed around dusk. A DNA glycosylase, namely ROS1A/DNG702, was probably the upstream candidate that demethylated the promoter of OsHXK1. Taken together, our results revealed that CG and CHG methylation contribute to the transcriptional regulation of OsPRR37-output genes, and hypomethylation of OsHXK1 leads to decreased starch content and reduced plant growth in rice.</p