Integrative analysis from multi‐centre studies identifies a function‐derived personalized multi‐gene signature of outcome in colorectal cancer

Colorectal cancer (CRC) is highly heterogeneous leading to variable prognosis and treatment responses. Therefore, it is necessary to explore novel personalized and reproducible prognostic signatures to aid clinical decision‐making. The present study combined large‐scale gene expression profiles and clinical data of 1828 patients with CRC from multi‐centre studies and identified a personalized gene prognostic signature consisting of 46 unique genes (called function‐derived personalized gene signature [FunPGS]) from an integrated statistics and function‐derived perspective. In the meta‐training and multiple independent validation cohorts, the FunPGS effectively discriminated patients with CRC with significantly different prognosis at the individual level and remained as an independent factor upon adjusting for clinical covariates in multivariate analysis. Furthermore, the FunPGS demonstrated superior performance for risk stratification with respect to other recently reported signatures and clinical factors. The complementary value of the molecular signature and clinical factors was further explored, and it was observed that the composite signature called IMCPS greatly improved the predictive performance of survival estimation relative to molecular signatures or clinical factors alone. With further prospective validation in clinical trials, the FunPGS may become a promising and powerful personalized prognostic tool for stratifying patients with CRC in order to achieve an optimal systemic therapy.     

Identification of CD28 and PTEN as novel prognostic markers for cervical cancer

Cervical cancer (CC) is the most common malignant tumor with poor clinical outcome among women. Identification of novel biomarkers could be beneficial for the clinical diagnosis and treatment of CC. This study aimed to identify prognostic biomarkers for the prediction of prognostic status of CC patients, and explore the effect of the corresponding methylated genes in the occurrence and development of CC. The methylation microarray data of CC was extracted from The Cancer Genome Atlas (TCGA) dataset. The methylation genes associated with the prognostic status were identified based on the information of the relapse-free survival (RFS) of the CC patients. The prognostic gene pairs were further identified. Then, the prognostic signature was identified by the forward search algorithm based on the C-index method. The results were validated by independent dataset. Finally, the functional analysis was performed on the methylation genes. A total of 276 methylation genes and 2508 gene pairs associated with the prognostic status of the CC were identified. A signature composed of eight methylation gene pairs was obtained to predict the prognostic status of cervical patients. A series of genes that played an important role in the occurrence and development of CC were obtained by the functional enrichment analysis. To summary, a prognostic signature consisting of eight methylation gene pairs was obtained. Of note, the CD28 and PTEN gene pair were found to play important roles in the occurrence and development of CC.     

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.     

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.     

A 3‐miRNA signature predicts survival of patients with hypopharyngeal squamous cell carcinoma after post‐operative radiotherapy

Since the prognosis of hypopharyngeal squamous cell carcinoma (HSCC) remains poor, identification of miRNA as a potential prognostic biomarker for HSCC may help improve personalized therapy. In the 2 cohorts with a total of 511 patients with HSCC (discovery: N = 372 and validation: N = 139) after post‐operative radiotherapy, we used miRNA microarray and qRT‐PCR to screen out the significant miRNAs which might predict survival. Associations of miRNAs and the signature score of these miRNAs with survival were performed by Kaplan‐Meier survival analysis and multivariate Cox hazard model. Among 9 candidate, miRNAs, miR‐200a‐3p, miR‐30b‐5p, miR‐3161, miR‐3605‐5p, miR‐378b and miR‐4451 were up‐regulated, while miR‐200c‐3p, miR‐429 and miR‐4701 were down‐regulated after validation. Moreover, the patients with high expression of miR‐200a‐3p, miR‐30b‐5p and miR‐4451 had significantly worse overall survival (OS) and disease‐specific survival (DSS) than did those with low expression (log‐rank P < .05). Patients with a high‐risk score had significant worse OS and DSS than those with low‐risk score. Finally, after adjusting for other important prognostic confounders, patients with high expression of miR‐200a‐3p, miR‐30b‐5p and miR‐4451 had significantly high risk of overall death and death owing to HSCC and patients with a high‐risk score has approximately 2‐fold increased risk in overall death and death owing to HSCC compared with those with a low‐risk score. These findings indicated that the 3‐miRNA‐based signature may be a novel independent prognostic biomarker for patients given surgery and post‐operative radiotherapy, supporting that these miRNAs may jointly predict survival of HSCC.     

Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.     

Analysis of colorectal cancer glyco‐secretome identifies laminin β‐1 (LAMB1) as a potential serological biomarker for colorectal cancer

The high mortality rate in colorectal cancer is mostly ascribed to metastasis, but the only clinical biomarker available for disease monitoring and prognosis is the carcinoembryonic antigen (CEA). However, the prognostic utility of CEA remains controversial. In an effort to identify novel biomarkers that could be potentially translated for clinical use, we collected the secretomes from the colon adenocarcinoma cell line HCT‐116 and its metastatic derivative, E1, using the hollow fiber culture system, and utilized the multilectin affinity chromatography approach to enrich for the secreted glycoproteins (glyco‐secretome). The HCT‐116 and E1 glyco‐secretomes were compared using the label‐free quantitative SWATH‐MS technology, and a total of 149 glycoproteins were differentially secreted in E1 cells. Among these glycoproteins, laminin β‐1 (LAMB1), a glycoprotein not previously known to be secreted in colorectal cancer cells, was observed to be oversecreted in E1 cells. In addition, we showed that LAMB1 levels were significantly higher in colorectal cancer patient serum samples as compared to healthy controls when measured using ELISA. ROC analyses indicated that LAMB1 performed better than CEA at discriminating between colorectal cancer patients from controls. Moreover, the diagnostic performance was further improved when LAMB1 was used in combination with CEA.     

Up-regulation of KIF14 is a predictor of poor survival and a novel prognostic biomarker of chemoresistance to paclitaxel treatment in cervical cancer

Kinesin family member 14 (KIF14) is a member of kinesin family proteins which have been found to be dysregulated in various cancer types. However, the expression of KIF14 and its potential prognostic significance have not been investigated in cervical cancer. Real-time PCR was performed to assess the expression levels of KIF14 in 47 pairs of cervical cancer tissues and their matched normal tissues from patients who had not been exposed to chemotherapy as well as tissue samples from 57 cervical cancer patients who are sensitive to paclitaxel treatment and 53 patients who are resistant. The association between KIF14 expression levels in tissue and clinicopathological features or chemosensitivity was examined. Kaplan–Meier analysis and Cox proportional hazards model were applied to assess the correlation between KIF14 expression levels and overall survival (OS) of cervical cancer patients. KIF14 expression levels were significantly increased in cervical cancer tissues compared with matched non-cancerous tissues and it was higher in tissues of patients who are chemoresistant compared with those who are chemosensitive. KIF14 expression was positively associated with high tumour stage (P=0.0044), lymph node metastasis (P=0.0034) and chemoresistance (P<0.0001). Kaplan–Meier analysis showed that high KIF14 expression levels predicted poor survival in patients with (P=0.0024) or without (P=0.0028) paclitaxel treatment. Multivariate analysis revealed that KIF14 was an independent prognostic factor for OS. Our study suggests that KIF14 may serve as a predictor of poor survival and a novel prognostic biomarker of chemoresistance to paclitaxel treatment in cervical cancer.     

Development and validation of a 10‐gene prognostic signature for acute myeloid leukaemia

Acute myeloid leukaemia (AML) is the most common type of adult acute leukaemia and has a poor prognosis. Thus, optimal risk stratification is of greatest importance for reasonable choice of treatment and prognostic evaluation. For our study, a total of 1707 samples of AML patients from three public databases were divided into meta‐training, meta‐testing and validation sets. The meta‐training set was used to build risk prediction model, and the other four data sets were employed for validation. By log‐rank test and univariate COX regression analysis as well as LASSO‐COX, AML patients were divided into high‐risk and low‐risk groups based on AML risk score (AMLRS) which was constituted by 10 survival‐related genes. In meta‐training, meta‐testing and validation sets, the patient in the low‐risk group all had a significantly longer OS (overall survival) than those in the high‐risk group (P < .001), and the area under ROC curve (AUC) by time‐dependent ROC was 0.5854‐0.7905 for 1 year, 0.6652‐0.8066 for 3 years and 0.6622‐0.8034 for 5 years. Multivariate COX regression analysis indicated that AMLRS was an independent prognostic factor in four data sets. Nomogram combining the AMLRS and two clinical parameters performed well in predicting 1‐year, 3‐year and 5‐year OS. Finally, we created a web‐based prognostic model to predict the prognosis of AML patients ( https://tcgi.shinyapps.io/amlrs_nomogram/ ).     

Weighted gene correlation network analysis identifies RSAD2, HERC5, and CCL8 as prognostic candidates for breast cancer

As the most commonly diagnosed malignant tumor in female population, the prognosis of breast cancer is affected by complex gene interaction networks. In this research weighted gene co-expression network analysis (WGCNA) would be utilized to build a gene co-expression network to identify potential biomarkers for prediction the prognosis of patients with breast cancer. We downloaded GSE25065 from Gene Expression Omnibus database as the test set. GSE25055 and GSE42568 were utilized to validate findings in the research. Seven modules were established in the GSE25065 by utilizing average link hierarchical clustering. Three hub genes, RSAD2, HERC5, and CCL8 were screened out from the significant module (R ² = 0.44), which were considerably interrelated to worse prognosis. Within test dataset GSE25065, RSAD2, and CCL8 were correlated with tumor stage, grade, and lymph node metastases, whereas HERC5 was correlated with lymph node metastases and tumor grade. In the validation dataset GSE25055 and RSAD2 expression was correlated with tumor grade, stage, and size, whereas HERC5 was related to tumor stage and tumor grade, and CCL8 was associated with tumor size and tumor grade. Multivariable survival analysis demonstrated that RSAD2, HERC5, and CCL8 were independent risk factors. In conclusion, the WGCNA analysis conducted in this study screened out novel prognostic biomarkers of breast cancer. Meanwhile, further in vivo and in vitro studies are required to make the clear molecular mechanisms.     

Comprehensive analysis of mRNA‐level and miRNA‐level subpathway activities for identifying robust ovarian cancer prognostic signatures

Ovarian cancer (OvCa) causes the highest mortality among all gynaecologic cancers. A large number of mRNA‐ or miRNA‐based signatures were identified for OvCa patient prognosis. However, the comprehensive analysis of function‐level prognostic signatures is currently not considered in OvCa. In the present study, we respectively inferred subpathway activities from mRNA and miRNA levels based on high‐throughput expression profiles and reconstructed subpathways. Firstly, the activities of two tumour pathways were calculated and the difference between normal and tumour samples were analysed using multiple tumour types. Then, we calculated subpathway activities for OvCa based on the expression profiles from both mRNA and miRNA levels. Furthermore, based on these subpathway activity matrices, we performed bootstrap analysis to obtain sub‐training sets and utilized univariate method to identify robust OvCa prognostic subpathways. A comprehensive comparison of subpathway results between these two levels was performed. As a result, we observed subpathway mutual exclusion trend between the levels of mRNA and miRNA, which indicated the necessary of combining mRNA‐miRNA levels. Finally, by using ICGC data as testing sets, we utilized two strategies to verify survival predictive power of the mRNA‐miRNA combined subpathway signatures and performed comparisons with results from individual levels. It was confirmed that our framework displayed application to identify robust and efficient prognostic signatures for OvCa, and the combined signatures indeed exhibited advantages over individual ones. In the study, we took a step forward in relevant novel integrated functional signatures for OvCa prognosis.     

Integrated analysis identifies RAC3 as an immune-related prognostic biomarker associated with chemotherapy sensitivity in endometrial cancer

Endometrial cancer (EC) is one of the most common gynaecological malignant tumours with a high incidence, leading to urgent demands for exploring novel carcinogenic mechanisms and developing rational therapeutic strategies. The rac family of small GTPase 3 (RAC3) functions as an oncogene in various human malignant tumours and plays an important role in tumour development. However, the critical roles of RAC3 in the progression of EC need further investigation. Based on TCGA, single-cell RNA-Seq, CCLE and clinical specimens, we revealed that the RAC3 was specifically distributed in EC tumour cells compared to normal tissues and functioned as an independent diagnostic marker with a high area under curve (AUC) score. Meanwhile, the RAC3 expression in EC tissues was also correlated with a poor prognosis. In detail, the high levels of RAC3 in EC tissues were reversely associated with CD8⁺T cell infiltration and orchestrated an immunosuppressive microenvironment. Furthermore, RAC3 accelerated tumour cell proliferation and inhibited its apoptosis, without impacting cell cycle stages. Importantly, silencing RAC3 improved the sensitivity of EC cells to chemotherapeutic drugs. In this paper, we revealed that RAC3 was predominantly expressed in EC and significantly correlated with the progression of EC via inducing immunosuppression and regulating tumour cell viability, providing a novel diagnostic biomarker and a promising strategy for sensitizing chemotherapy to EC.