Efficient learning of Scale-Adaptive Nearly Affine Invariant Networks.

Recent research has demonstrated the significance of incorporating invariance into neural networks. However, existing methods require direct sampling over the entire transformation set, notably computationally taxing for large groups like the affine group. In this study, we propose a more efficient approach by addressing the invariances of the subgroups within a larger group. For tackling affine invariance, we split it into the Euclidean group E(n) and uni-axial scaling group US(n), handling invariance individually. We employ an E(n)-invariant model for E(n)-invariance and average model outputs over data augmented from a US(n) distribution for US(n)-invariance. Our method maintains a favorable computational complexity of O(N2) in 2D and O(N4) in 3D scenarios, in contrast to the O(N6) (2D) and O(N12) (3D) complexities of averaged models. Crucially, the scale range for augmentation adapts during training to avoid excessive scale invariance. This is the first time nearly exact affine invariance is incorporated into neural networks without directly sampling the entire group. Extensive experiments unequivocally confirm its superiority, achieving new state-of-the-art results in affNIST and SIM2MNIST classifications while consuming less than 15% of inference time and fewer computational resources and model parameters compared to averaged models.

AURKA Identified as Potential Lung Cancer Marker through Comprehensive Bioinformatic Analysis and Experimental Verification.

Non-small-cell lung cancer (NSCLC) is a malignancy with high overall morbidity and mortality due to a lack of reliable methods for early diagnosis and successful treatment of the condition. We identified genes that would be valuable for the diagnosis and prognosis of lung cancer. Common DEGs (DEGs) in three GEO datasets were selected for KEGG and GO enrichment analysis. A protein-protein interaction (PPI) network was constructed using the STRING database, and molecular complex detection (MCODE) identified hub genes. Gene expression profiling interactive analysis (GEPIA) and the Kaplan-Meier method analyzed hub genes expression and prognostic value. Quantitative PCR and western blotting were used to test for differences in hub gene expression in multiple cell lines. The CCK-8 assay was used to determine the IC50 of the AURKA inhibitor CCT137690 in H1993 cells. Transwell and clonogenic assays validated the function of AURKA in lung cancer, and cell cycle experiments explored its possible mechanism of action. Overall, 239 DEGs were identified from three datasets. AURKA, BIRC5, CCNB1, DLGAP5, KIF11, and KIF15 had shown great potential for lung cancer diagnosis and prognosis. In vitro experiments suggested that AURKA significantly influenced the proliferation and migration of lung cancer cells and activities related to the dysregulation of the cell cycle. AURKA, BIRC5, CCNB1, DLGAP5, KIF11, and KIF15 may be critical genes that influence the occurrence, development, and prognosis of NSCLC. AURKA significantly affects the proliferation and migration of lung cancer cells by disrupting the cell cycle.

Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment.

Introduction: Research has revealed that the tumor microenvironment (TME) is associated with the progression of malignancy. The combination of meaningful prognostic biomarkers related to the TME is expected to be a reliable direction for improving the diagnosis and treatment of non-small cell lung cancer (NSCLC). Method and Result: Therefore, to better understand the connection between the TME and survival outcomes of NSCLC, we used the "DESeq2" R package to mine the differentially expressed genes (DEGs) of two groups of NSCLC samples according to the optimal cutoff value of the immune score through the ESTIMATE algorithm. A total of 978 up-DEGs and 828 down-DEGs were eventually identified. A fifteen-gene prognostic signature was established via LASSO and Cox regression analysis and further divided the patients into two risk sets. The survival outcome of high-risk patients was significantly worse than that of low-risk patients in both the TCGA and two external validation sets (p-value < 0.05). The gene signature showed high predictive accuracy in TCGA (1-year area under the time-dependent ROC curve (AUC) = 0.722, 2-year AUC = 0.708, 3-year AUC = 0.686). The nomogram comprised of the risk score and related clinicopathological information was constructed, and calibration plots and ROC curves were applied, KEGG and GSEA analyses showed that the epithelial-mesenchymal transition (EMT) pathway, E2F target pathway and immune-associated pathway were mainly involved in the high-risk group. Further somatic mutation and immune analyses were conducted to compare the differences between the two groups. Drug sensitivity provides a potential treatment basis for clinical treatment. Finally, EREG and ADH1C were selected as the key prognostic genes of the two overlapping results from PPI and multiple Cox analyses. They were verified by comparing the mRNA expression in cell lines and protein expression in the HPA database, and clinical validation further confirmed the effectiveness of key genes. Conclusion: In conclusion, we obtained an immune-related fifteen-gene prognostic signature and potential mechanism and sensitive drugs underling the prognosis model, which may provide accurate prognosis prediction and available strategies for NSCLC.

X-box Binding Protein 1 is a Potential Immunotherapy Target in Ovarian Cancer.

The allure of potentially dramatic and durable responses to immunotherapy has driven the study of several immune checkpoint inhibitor (ICI) agents in ovarian cancer. However, the results of ICI therapy in ovarian cancer have been rather disappointing. It is important to understand the reasons for the poor efficacy of ICI in ovarian cancer and to look for new targets for immunotherapy. To solve this problem, ovarian cancer-associated datasets were individually collected from The Cancer Genome Atlas (TCGA)、International Cancer Genome Consortium (ICGC)、Genotype-Tissue Expression (GTEx), and comprehensively performed to expression, prognostic, pathological correlation, genomic and immunologic analyses of reported all immune checkpoints by Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Tumor and Immune System Interaction Database (TISIDB), cBio Cancer Genomics Portal (cBioPortal), and Kaplan-Meier Plotter. We concluded that those well-identified immune checkpoints might not be ideal targets for ovarian cancer immunotherapy. Intriguingly, the genomic alteration of X-box binding protein 1 (XBP1), the important mediator of chemotherapy-induced cancer immunogenic cell death, was found to be a potential coregulator of immune checkpoints in ovarian cancer. Importantly, XBP1 was detected to be highly expressed in ovarian cancer compared with normal ovarian tissue, and high XBP1 expression significantly benefits both overall survival (OS) and disease-free survival (DFS) of ovarian cancer patients. More importantly, XBP1 was further observed to be closely related to anti-tumor immunity in ovarian cancer, including multiple T-cell signatures and immunity-killing molecules. In conclusion, upregulating XBP1 rather than targeting immune checkpoints represents a potentially more efficient approach for ovarian cancer therapy.

Glycosylated Cathepsin V Serves as a Prognostic Marker in Lung Cancer.

Dysregulation of cysteine cathepsin protease activity is pivotal in tumorigenic transformation. However, the role of cathepsin protease in lung cancer remains unknown. Here, we analyzed GEO database and found that lung cancer presented high expression of cathepsin V (CTSV). We then performed immunohistochemistry assay in 73 paired lung cancer tissues and normal lung tissues and confirmed that CTSV is overexpressed in lung cancer and correlates with poor prognosis. The mass spectrometry experiment showed that the N-glycosylation locus of CTSV are N221 and N292, glycosylated CTSV (band 43 kDa) was particularly expressed in lung cancer samples and correlated with lymph node metastasis. Mechanistic studies showed that only glycosylated CTSV (43-kDa band) are secreted to extracellular matrix (ECM) and promoted the metastasis of lung cancer. Importantly, the Elisa detection in serum of 12 lung cancer patients and 12 healthy donors showed that the level of CTSV in serum distinguished lung cancer patients from healthy donors. Together, our findings reveal the clinical relevance of CTSV glycosylation and CTSV drives the metastasis of lung cancer, suggesting that the glycosylated CTSV in serum is a promising biomarker for lung cancer.

FBXO32 targets PHPT1 for ubiquitination to regulate the growth of EGFR mutant lung cancer.

BACKGROUND: Phosphohistidine phosphatase 1 (PHPT1) is an oncogene that has been reported to participate in multiple tumorigenic processes. As yet, however, the role of PHPT1 in lung cancer development remains uncharacterized. METHODS: RNA sequencing assay and 18 pairs of tumor and normal tissues from patients were analyzed to reveal the upregulation of PHPT1 in lung cancer, followed by confirming the biological function in vitro and in vivo. Next, Gene Set Enrichment Analysis, lung cancer samples, apoptosis assay, mass spectrometry experiments and western blotting were used to investigate the molecular mechanism underlying PHPT1 driven progression in epidermal growth factor receptor (EGFR)-mutant lung cancer. Finally, we performed cellular and animal experiments to explore the tumor suppressive function of F-box protein 32 (FBXO32). RESULTS: We found that PHPT1 is overexpressed in lung cancer patients and correlates with a poor overall survival. In addition, we found that the expression of PHPT1 is elevated in EGFR-mutant lung cancer cells and primary patient samples. Inhibition of PHPT1 expression in EGFR mutant lung cancer cells significantly decreased their proliferation and clonogenicity, and suppressed their in vitro tumor growth. Mechanistic studies revealed that activation of the ERK/MAPK pathway is driven by PHPT1. PHPT1 is required for maintaining drug resistance to erlotinib in EGFR mutant lung cancer cells. We found that FBXO32 acts as an E3 ubiquitin ligase for PHPT1, and that knockdown of FBXO32 leads to PHPT1 accumulation, activation of the ERK/MAPK pathway and promotion of the proliferation, clonogenicity and growth of lung cancer cells. CONCLUSIONS: Our findings indicate that PHPT1 may serve as a biomarker and therapeutic target for acquired erlotinib resistance in lung cancer patients carrying EGFR mutations.

Early oral intake after intra-abdominal gynecological oncology surgery.

To explore and evaluate the safety and efficacy of early oral intake following major abdominal gynecological oncology surgery. During an 11-month period, 60 gynecological oncology patients undergoing major intra-abdominal surgery were enrolled in a randomized controlled clinical trial of semiliquid diet (experimental group) compared with clear feeds (control group) 6 hours after operation. Patients were evaluated for nausea, vomiting, bowel sound, passage of flatus, body weight difference before and after operation, urine acetone, fasting blood sugar, and prealbumin level, and need for nasogastric tube decompression. There were significantly higher incidences of nausea, shorter time of regular diet resumption, and higher level of prealbumin on postoperative day 7 in patients from the experimental group than those from the control group (P < .05). No significant differences were found in vomiting, the time to development of bowel sound and passage of flatus, body weight difference before and after operation, urine acetone and fasting blood sugar on postoperative day 1, and prealbumin level on postoperative day 2 between the 2 groups. Early oral intake with semiliquid diet 6 hours after major intra-abdominal gynecological oncology surgery is safe and well tolerated.