Untargeted metabolomics based on liquid chromatography−mass spectrometry is affected by nonlinear batch effects, which cover up biological effects, result in nonreproducibility, and are difficult to be calibrate. In this study, we propose a novel deep learning model, called Normalization Autoencoder (NormAE), which is based on nonlinear autoencoders (AEs) and adversarial learning. An additional classifier and ranker are trained to provide adversarial regularization during the training of the AE model, latent representations are extracted by the encoder, and then the decoder reconstructs the data without batch effects. The NormAE method was tested on two real metabolomics data sets. After calibration by NormAE, the quality control samples (QCs) for both data sets gathered most closely in a PCA score plot (average distances decreased from 56.550 and 52.476 to 7.383 and 14.075, respectively) and obtained the highest average correlation coefficients (from 0.873 and 0.907 to 0.997 for both). Additionally, NormAE significantly improved biomarker discovery (median number of differential peaks increased from 322 and 466 to 1140 and 1622, respectively). NormAE was compared with four commonly used batch effect removal methods. The results demonstrated that using NormAE produces the best calibration results.

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The metabolic profiling of biofluids using untargeted metabolomics provides a promising choice to discover metabolite biomarkers for clinical cancer diagnosis. However, metabolite biomarkers discovered in biofluids may not necessarily reflect the pathological status of tumor tissue, which makes these biomarkers difficult to reproduce. In this study, we developed a new analysis strategy by integrating the univariate and multivariate correlation analysis approach to discover tumor tissue derived (TTD) metabolites in plasma samples. Specifically, untargeted metabolomics was first used to profile a set of paired tissue and plasma samples from 34 colorectal cancer (CRC) patients. Next, univariate correlation analysis was used to select correlative metabolite pairs between tissue and plasma, and a random forest regression model was utilized to define 243 TTD metabolites in plasma samples. The TTD metabolites in CRC plasma were demonstrated to accurately reflect the pathological status of tumor tissue and have great potential for metabolite biomarker discovery. Accordingly, we conducted a clinical study using a set of 146 plasma samples from CRC patients and gender-matched polyp controls to discover metabolite biomarkers from TTD metabolites. As a result, eight metabolites were selected as potential biomarkers for CRC diagnosis with high sensitivity and specificity. For CRC patients after surgery, the survival risk score defined by metabolite biomarkers also performed well in predicting overall survival time (p = 0.022) and progression-free survival time (p = 0.002). In conclusion, we developed a new analysis strategy which effectively discovers tumor tissue related metabolite biomarkers in plasma for cancer diagnosis and prognosis.

Ovarian cancer (OvCa) is the leading cause of death among all gynecological malignancies, and recurrent OvCa is almost always incurable. In this study, we developed a signature based on long non-coding RNAs (lncRNAs) associated with OvCa recurrence to facilitate personalized OvCa therapy. lncRNA expression data were extracted from GSE9891 and GSE30161. LASSO (least absolute shrinkage and selection operator) penalized regression was used to identify an lncRNA-based signature using the GSE9891 training cohort. The signature was then validated in GSE9891 internal and GSE30161 external validation cohorts. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to explore the possible functions of identified lncRNAs. A six-lncRNA signature (RUNX1-IT1, MALAT1, H19, HOTAIRM1, LOC100190986 and AL132709.8) was identified in the training cohort and validated in internal and external validation cohorts using the LASSO method (P < 0.05). This signature was also independent of other clinical factors according to multivariate and sub-group analyses. The identified lncRNAs are involved in cancer-related biological processes and pathways. We selected a highly reliable signature based on six lncRNAs associated with OvCa recurrence. This six-lncRNA signature is a promising method to personalize ovarian cancer therapy and may improve patient quality of life quality according to patients’ condition in the future.

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Renal clear cell carcinoma (RCC) patients who do not achieve optimal control of progression with immune checkpoint blockade (ICB) should be further studied. Unsupervised consensus clustering was used to group 525 RCC patients based on two typical ICB pathways, CTLA-4 and pogrammed death 1 (PD-1)/ programmed death-ligand 1 (PD-L1), as well as two new discovered regulators, CMTM6 and CMTM4. Three immune molecular subtypes (IMMSs) with different clinical and immunological characteristics were identified (type I, II, and III), among which there were more stage I and low-grade tumors in type I RCC than in type II and III. The proportion of males was highest in type II RCC. Overall survival of type II and III was similar (5.2 and 6 years) and statistically shorter than that of type I (7.6 years) before and after adjusting for age and gender. When conducting stratified analysis, our IMMSs were able to identify high-risk patients among middle-aged patients, males, and stage IV patients.Among the differentially expressed genes, approximately 84% were highly expressed in type II and III RCC. Genes related to ICB (CTLA-4, CD274, and PDCD1LG2) and cytotoxic lymphocytes (CD8A, GZMA, and PRF1) were all highly expressed in type II and III RCC. These results documented that patients with type II and III cancer may be more sensitive to anti-CTLA-4 therapy, anti-PD-1/PD-L1 therapy, and a combination of immunotherapies. High expression of CMTM4 in type I RCC (69%) and a statistically significant interaction of CD274 and CMTM6 indicated that CMTM4/6 might be new therapy targets for type I, who are resistant to ICB.

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