In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

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Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.

Background
Length of hospital stay (LOS) is a surrogate marker for patients' well-being during hospital treatment and is associated with health care costs. Identifying pretreatment factors associated with LOS in surgical patients may enable early intervention in order to reduce postoperative LOS.
Methods
This cohort study enrolled 157 patients with suspected or proven gynecological cancer at a tertiary cancer centre (2004-2006). Before commencing treatment, the scored Patient Generated - Subjective Global Assessment (PG-SGA) measuring nutritional status and the Functional Assessment of Cancer Therapy-General (FACT-G) scale measuring quality of life (QOL) were completed. Clinical and demographic patient characteristics were prospectively obtained. Patients were grouped into those with prolonged LOS if their hospital stay was greater than the median LOS and those with average or below average LOS.
Results
Patients' mean age was 58 years (SD 14 years). Preoperatively, 81 (52%) patients presented with suspected benign disease/pelvic mass, 23 (15%) with suspected advanced ovarian cancer, 36 (23%) patients with suspected endometrial and 17 (11%) with cervical cancer, respectively. In univariate models prolonged LOS was associated with low serum albumin or hemoglobin, malnutrition (PG-SGA score and PG-SGA group B or C), low pretreatment FACT-G score, and suspected diagnosis of cancer. In multivariable models, PG-SGA group B or C, FACT-G score and suspected diagnosis of advanced ovarian cancer independently predicted LOS.
Conclusions
Malnutrition, low quality of life scores and being diagnosed with advanced ovarian cancer are the major determinants of prolonged LOS amongst gynecological cancer patients. Interventions addressing malnutrition and poor QOL may decrease LOS in gynecological cancer patients.

Pretreatment malnutrition and quality of life - association with prolonged length of hospital stay among patients with gynecological cancer: A cohort study

Nutritional support during the hospital stay reduces mortality in patients with different types of cancers: secondary analysis of a prospective randomized trial.

DNA methylation can regulate the role of long noncoding RNAs (lncRNAs) in the development of lung adenocarcinoma (LUAD). The present study aimed to identify methylation-driven lncRNAs and mRNAs as biomarkers in the prognosis of LUAD using bioinformatics analysis. Differentially expressed RNAs were obtained using the edge R package from 535 LUAD tissues and 59 adjacent non-LUAD tissues. Differentially methylated genes were obtained using the limma R package from 475 LUAD tissues and 32 adjacent non-LUAD tissues. Methylation-driven mRNA and lncRNA were obtained using the MethylMix R package from 465 LUAD tissues with matched DNA methylation and RNA expression and 32 non-LUAD tissues with DNA methylation. Gene ontology and ConsensusPathDB pathway analysis were performed to identify functional enrichment of methylation-driven mRNAs. Univariate and multivariate Cox regression analyses were performed to identify the independent effect of each variable for predicting the prognosis of LUAD. Kaplan–Meier curve analysis of DNA methylation and gene expression might provide potential prognostic biomarkers for LUAD patients. A total of 99 methylation-driven mRNAs and 17 methylation-driven lncRNAs were obtained. Univariate and multivariate Cox regression analysis showed that 6 lncRNAs (FOXE1, HOXB13-AS1_2, VMO1, HIST1H3F, AJ003147.8, ASXL3) were retrieved to construct a predictive model associated with overall survival in LUAD patients. Combined DNA methylation and gene expression survival analysis revealed that 4 lncRNAs (AC023824.1, AF186192.1, LINC01354 and WASIR2) and 8 mRNAs (S1PR1, CCDC181, F2RL1, EFS, KLHDC9, MPV17L, GKN2, ITPRIPL1) might act as independent biomarkers for the prognosis of LUAD. Methylation-driven lncRNA and mRNA contribute to the survival of LUAD, and 4 lncRNAs and 8 mRNAs might be potential biomarkers for the prognosis of LUAD.

/pdf/methylation-and-transcriptome-analysis-reveal-lung-3pg9eb0blp.pdf

Methylation and transcriptome analysis reveal lung adenocarcinoma-specific diagnostic biomarkers

Targeting STAT3 inhibition to reverse cisplatin resistance.

AIMS Poor efficacy of chemotherapy drugs in non-small-cell lung cancer (NSCLC) is the key reason for the failure of treatment, but the mechanism of this remains largely unknown. Stromal cell-derived factor 1-alpha (SDF-1α/CXCL12) is a small chemotactic cytokine protein that plays an important role in tumor progression. In this study, we investigated the anti-apoptotic mechanism of the CXCL12/CXCR4 axis in response to cisplatin, a commonly used chemotherapeutic drug, in human lung adenocarcinoma A549 cells. METHODS CXCL12 blocks cisplatin-induced apoptosis in A549, and the results were shown by propidium iodide/annexin V staining in vitro. The mechanism of CXCL12 stimulating phosphorylation of STAT3 through CXCR4/JAK2 was demonstrated by immunofluorescence and Western blotting. The expression of CXCL12 and p-STAT3 in clinical specimens was examined by immunohistochemistry. RESULTS CXCL12 significantly decreased the ratio of apoptotic cells and stimulation of phospho-signal transducer and activator of transcription (p-STAT)-3 in a time-dependent manner through interaction with CXCR4. Among the signaling molecules downstream of CXCR4, the JAK2/STAT3 pathway plays a predominant role in the anti-apoptotic effect of CXCL12. Analysis of clinical specimens revealed that increased CXCL12 and p-STAT3 expression correlates with enhanced lung cancer progression. CONCLUSION These data suggest that CXCR4 contributes to CXCL12-mediated anti-apoptosis by activating JAK2/STAT3 pathway in NSCLC cells. Therefore, targeting CXCL12/CXCR4 signaling pathway reveals a potential therapeutic approach for NSCLC.

/pdf/cxcl12-suppresses-cisplatin-induced-apoptosis-through-4eit91vsmb.pdf

CXCL12 suppresses cisplatin-induced apoptosis through activation of JAK2/STAT3 signaling in human non-small-cell lung cancer cells.

Purpose: The consequences of malnutrition include increased risk of many complications. The assessment and management of nutritional problems are essential in supportive care of patients undergoing...

Nutritional status in patients with advanced lung cancer undergoing chemotherapy: a prospective observational study.

Lung adenocarcinoma (LUAD) poses a significant threat to public health worldwide, while the genetic and epigenetic abnormalities involved in the oncogenesis of LUAD remains unknown. This study aimed to identify and validate key genes during the development and progression of LUAD by multiomics analysis. First, Empirical Analysis of Digital Gene Expression Data in R (EdgeR) was used to identify differentially regulated genes between normal samples and LUAD samples. Then significance analysis of microarrays (SAM) was used to identify differentially methylated genes and regulated microRNAs (miRNAs) between normal samples and LUAD samples. Following that, Kyoto Encyclopedia of Genes and Genomes (KEGG)-enrichment analysis was used to analyze the function that these genes enriched in. A total of 4,816 genes, 419 miRNAs, and 4,476 methylated genes that were significantly differentially expressed corresponding to the normal tissues in LUAD were obtained, and some of the pathways these genes enriched in were the same. Moreover, 255 genes differentially methylated and expressed at the same time were also found, and these 255 genes were the target genes of the miRNAs differentially expressed in LUAD. Finally, nine genes (BRCA1, COL1A1, ESR1, FGFR2, HNF4A, IGFBP3, MET, MMP3, and PAK1) network analysis, and two of which were found to be related to the survival of LUAD patients. In summary, a total of nine genes that may play important roles in the development of LUAD were identified, and two (PAK1 and FGFR2) of them can be served as prognostic biomarkers for LUAD patients. The genes found in this study played different roles in the tumor progression of LUAD, indicating these genes may be considered as potential target genes for LUAD treatment.

Multiomics analysis on DNA methylation and the expression of both messenger RNA and microRNA in lung adenocarcinoma.

Background: Collagen type 1 alpha 1 chain (COL1A1) is an extracellular matrix protein comprising two alpha 1 chains and one alpha 2 chain. Our previous study identified that COL1A1 is the key gene during the development and progression of lung adenocarcinoma by multi-omics analysis. However, the clinical significance of COL1A1 expression in lung cancer samples remains largely unknown. Here, we aimed to evaluate the level of COL1A1 in lung cancer samples and correlate its level with the clinical outcome. Methods: COL1A1 gene expression in lung cancer samples was analyzed using the Oncomine database (www.oncomine.org). A total of 308 lung cancer samples (208 formalin-fixed paraffin-embedded tissues and 100 blood samples) were assessed for protein expression of COL1A1. Immunohistochemistry staining and enzyme-linked immunosorbent assay were used to detect COL1A1 expression in tissues and serum, respectively. Results: We identified an elevation of COL1A1 in mRNA level and gene amplification in lung cancer tissues compared with normal lung tissues. High COL1A1 expression was observed in lung cancer tissues and serum (P < 0.05), it was significantly correlated with the peripheral type tumor, the larger diameter of the tumor, the occurrence of lymph node metastases and distant metastases, a higher TNM stage, and smoking (P < 0.05). High COL1A1 expression was associated with poor progression-free survival (PFS) and chemoresistance in lung cancer patients (P < 0.05). Multivariable Cox-regression analysis showed that COL1A1 expression was an independent prognostic factor (P < 0.05). Furthermore, the area under the receiver operating characteristic (AUC) curve was 0.909 for the combined COL1A1 and carcinoembryonic antigen (CEA) measurement. Conclusion: Our findings revealed that COL1A1 could be used as a novel diagnostic, prognostic, and chemoresistance biomarker of human lung cancer, and these results provide a potential therapeutic strategy for lung cancer patients.

/pdf/collagen-type-1-alpha-1-chain-is-a-novel-predictive-56ku02yecf.pdf

Tie Lin

Papers

CXCL12 suppresses cisplatin-induced apoptosis through activation of JAK2/STAT3 signaling in human non-small-cell lung cancer cells.

Nutritional status in patients with advanced lung cancer undergoing chemotherapy: a prospective observational study.

Multiomics analysis on DNA methylation and the expression of both messenger RNA and microRNA in lung adenocarcinoma.

Collagen type 1 alpha 1 chain is a novel predictive biomarker of poor progression-free survival and chemoresistance in metastatic lung cancer.