Computational Recognition of a Regulatory T-cell-specific Signature With Potential Implications in Prognosis, Immunotherapy, and Therapeutic Resistance of Prostate Cancer.

Prostate cancer, recognized as a "cold" tumor, has an immunosuppressive microenvironment in which regulatory T cells (Tregs) usually play a major role. Therefore, identifying a prognostic signature of Tregs has promising benefits of improving survival of prostate cancer patients. However, the traditional methods of Treg quantification usually suffer from bias and variability. Transcriptional characteristics have recently been found to have a predictive power for the infiltration of Tregs. Thus, a novel machine learning-based computational framework has been presented using Tregs and 19 other immune cell types using 42 purified immune cell datasets from GEO to identify Treg-specific mRNAs, and a prognostic signature of Tregs (named "TILTregSig") consisting of five mRNAs (SOCS2, EGR1, RRM2, TPP1, and C11orf54) was developed and validated to monitor the prognosis of prostate cancer using the TCGA and ICGC datasets. The TILTregSig showed a stronger predictive power for tumor immunity compared with tumor mutation burden and glycolytic activity, which have been reported as immune predictors. Further analyses indicate that the TILTregSig might influence tumor immunity mainly by mediating tumor-infiltrating Tregs and could be a powerful predictor for Tregs in prostate cancer. Moreover, the TILTregSig showed a promising potential for predicting cancer immunotherapy (CIT) response in five CIT response datasets and therapeutic resistance in the GSCALite dataset in multiple cancers. Our TILTregSig derived from PBMCs makes it possible to achieve a straightforward, noninvasive, and inexpensive detection assay for prostate cancer compared with the current histopathological examination that requires invasive tissue puncture, which lays the foundation for the future development of a panel of different molecules in peripheral blood comprising a biomarker of prostate cancer.

[Corrigendum] MicroRNA-148a inhibits breast cancer migration and invasion by directly targeting WNT-1.

Subsequently to the publication of the above article, an interested reader drew to the authors' attention that the data panel for the MDA­MB­231/migration/NC experiment in Fig. 2B on p. 1428 was strikingly similar to the data shown for the MDA­MB­231/invasion/Blank experiment in Fig. 2C, such that these data appeared to have been derived from the same original source. The authors have referred back to their original data, and realize that the data panel was selected incorrectly for Fig. 2B. The corrected version of Fig. 2, showing the correct data for the MDA­MB­231/migration/NC experiment in Fig. 2B, is shown on the next page. The authors regret the error that was made during the preparation of this figure, and can confirm that the error in the assembly of this figure did not adversely affect the conclusions reported in the study. The authors are grateful to the Editor of Oncology Reports for allowing them the opportunity to publish a Corrigendum, and all the authors agree to this Corrigendum. Furthermore, they apologize to the readership for any inconvenience caused. [the original article was published in Oncology Reports 35: 1425­1432, 2016; DOI: 10.3892/or.2015.4502].

A Immune-Related Signature Associated with TME Can Serve as a Potential Biomarker for Survival and Sorafenib Resistance in Liver Cancer.

OBJECTIVE: Although many curative treatments are being applied in the clinic, a significant number of patients with liver hepatocellular carcinoma (LIHC) suffer from drug resistance. The tumour microenvironment (TME) has been found to be closely associated with resistance, suggesting that identification of predictive biomarkers related to the TME for resistance in LIHC will be very rewarding. However, there has been no study dedicated to identifying a TME-related biomarker that has the potential to predict resistance in LIHC. METHODS: An integrated analysis was conducted based on data of patients with LIHC suffering from drug resistance from the TCGA database and four GEO datasets. Subsequently, we also validated the expression levels of the identified genes in paraffin-embedded LIHC samples by immunohistochemistry. RESULTS: In this study, we developed a robust and acute TME-related signature consisted of five immune-related genes (FABP6, CD4, PRF1, EREG and COLEC10) that could independently predict both the RFS and OS of LIHC patients. Moreover, the TME-related signature was significantly associated with the immune score, immune cytolytic activity (CYT), HLA, interferon (IFN) response and tumour-infiltrating lymphocytes (TILs), and it might influence tumour immunity mainly by affecting B cells, CD8+ T cells and dendritic cells. Furthermore, our analysis also indicated that the TME-related signature was correlated with the immunotherapy response and had an enormous potential to predict sorafenib resistance in LIHC. CONCLUSION: Our findings demonstrated a TME-related signature which can independently predict both the RFS and OS of LIHC patients, highlighting the predictive potential of the signature for immunotherapy response and sorafenib resistance, potentially enabling more precise and personalized sorafenib treatment in LIHC in the future.

Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Predicting Survival in Patients With HNSCC.

Head and neck squamous cell cancer (HNSCC) is one of the most common types of cancer worldwide. There have been many reports suggesting that biomarkers explored via database mining plays a critical role in predicting HNSCC prognosis. However, a single biomarker for prognostic analysis is not adequate. Additionally, there is growing evidence indicating that gene signature could be a better choice for HNSCC prognosis. We performed a comprehensive analysis of mRNA expression profiles using clinical information of HNSCC patients from The Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis (GSEA) was performed, and we found that a set of genes involved in epithelial mesenchymal transition (EMT) contributed to HNSCC. Cox proportional regression model was used to identify a four-gene (WIPF1, PPIB, BASP1, PLOD2) signature that were significantly associated with overall survival (OS), and all the four genes were significantly upregulated in tumor tissues. We successfully classified the patients with HNSCC into high-risk and low-risk groups, where in high-risk indicated poorer patient prognosis, indicating that this gene signature might be a novel potential biomarker for the prognosis of HNSCC. The prognostic ability of the gene signature was further validated in an independent cohort from the Gene Expression Omnibus (GEO) database. In conclusion, we identified a four-EMT-based gene signature which provides the potentiality to serve as novel independent biomarkers for predicting survival in HNSCC patients, as well as a new possibility for individualized treatment of HNSCC.

Identification of DNA-Repair-Related Five-Gene Signature to Predict Prognosis in Patients with Esophageal Cancer.

Esophageal cancer (ESCA) is a leading cause of cancer-related mortality, with poor prognosis worldwide. DNA damage repair is one of the hallmarks of cancer. Loss of genomic integrity owing to inactivation of DNA repair genes can increase the risk of cancer progression and lead to poor prognosis. We aimed to identify a novel gene signature related to DNA repair to predict the prognosis of ESCA patients. Based on gene expression profiles of ESCA patients from The Cancer Genome Atlas and gene set enrichment analysis, 102 genes related to DNA repair were identified as candidates. After stepwise Cox regression analysis, we established a five-gene prognostic model comprising DGCR8, POM121, TAF9, UPF3B, and BCAP31. Kaplan-Meier survival analysis confirmed a strong correlation between the prognostic model and survival. Moreover, we verified the clinical value of the prognostic signature under the influence of different clinical parameters. We found that small-molecule drugs (trametinib, selumetinib, and refametinib) could help to improve patient survival. In summary, our study provides a novel and promising prognostic signature based on DNA-repair-related genes to predict survival of patients with ESCA. Systematic data mining provides a theoretical basis for further exploring the molecular pathogenesis of ESCA and identifying therapeutic targets.

Overexpression of PTPRN Promotes Metastasis of Lung Adenocarcinoma and Suppresses NK Cell Cytotoxicity.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common diagnostic histologic subtype of non-small cell lung cancer, but the role of receptor-type tyrosine-protein phosphatase-like N (PTPRN) in LUAD has not been studied. METHODS: We conducted a bioinformatic analysis to identify the expression of PTPRN on LUAD data from the Cancer Genome Atlas (TCGA) and the relationship between PTPRN and overall survival of LUAD patients. The effects of PTPRN on the migration ability of LUAD cells and the underlying mechanisms were investigated by in vitro and in vivo assays (i.e., wound healing assay, transwell assay, western blotting, xenograft model, and immunohistochemistry). Gene-set enrichment analysis and computational resource were used to analyze the correlation between PTPRN and different tumor-infiltrating immune cells (TIICs). Lactate dehydrogenase assay and Enzyme-linked immunosorbent assay were conducted to examine natural killer (NK) cell cytotoxicity. RESULTS: In our study, we found that PTPRN was up-regulated in LUAD and related to metastasis of LUAD patients. Besides, PTPRN was correlated with poor prognosis in the TCGA-LUAD dataset. PTPRN overexpression promoted LUAD cell migration and the expression of EMT markers by influencing MEK/ERK and PI3K/AKT signaling. Moreover, PTPRN expression was significantly associated with TIICs, especially NK cells. A549 and H1299 cells overexpressed PTPRN inhibited NK cell cytotoxicity. CONCLUSION: Taken together, these findings demonstrated that PTPRN might be a potential and novel therapeutic target modulating antitumor immune response in treatment of LUAD.

NF-κB-activated SPRY4-IT1 promotes cancer cell metastasis by downregulating TCEB1 mRNA via Staufen1-mediated mRNA decay.

Previous study demonstrated that most long non-coding RNAs (lncRNAs) function as competing endogenous RNAs or molecular sponges to negatively modulate miRNA and regulate tumor development. However, the molecular mechanisms of lncRNAs in cancer are not fully understood. Our study describes the role of the lncRNA SPRY4 intronic transcript 1 (SPRY4-IT1) in cancer metastasis by mechanisms related to Staufen1 (STAU1)-mediated mRNA decay (SMD). Briefly, we found that, high SPRY4-IT1 expression was associated with aggressiveness and poor outcome in human colorectal, breast and ovarian cancer tissues. In addition, functional assays revealed that SPRY4-IT1 significantly promoted colorectal, breast and ovarian cancer metastasis in vitro and in vivo. Mechanistically, microarray analyses identified several differentially-expressed genes upon SPRY4-IT1 overexpression in HCT 116 colorectal cancer cells. Among them, the 3'-UTR of transcription elongation factor B subunit 1 (TCEB1) mRNA can base-pair with the Alu element in the 3'-UTR of SPRY4-IT1. Moreover, SPRY4-IT1 was found to bind STAU1, promote STAU1 recruitment to the 3'-UTR of TCEB1 mRNA, and affect TCEB1 mRNA stability and expression, resulting in hypoxia-inducible factor 1α (HIF-1α) upregulation, and thereby affecting cancer cell metastasis. In addition, STAU1 depletion abrogated TCEB1 SMD and alleviated the pro-metastatic effect of SPRY4-IT1 overexpression. Significantly, we revealed that SPRY4-IT1 is also transactivated by NF-κB/p65, which activates SPRY4-IT1 to inhibit TCEB1 expression, and subsequently upregulate HIF-1α. In conclusion, our results highlight a novel mechanism of cytoplasmic lncRNA SPRY4-IT1 in which SPRY4-IT1 affecting TCEB1 mRNA stability via STAU1-mediated degradation during cancer metastasis.

N6-Methyladenosine-Related RNA Signature Predicting the Prognosis of Ovarian Cancer.

BACKGROUND: N6-Methyladenosine (m6A) RNA methylation is the most universal mRNA modification in eukaryotic cells. M6A mRNA modification affects almost every phases of RNA processing, including splicing, decay, export, translation and expression. Several patents have reported the application of m6A mRNA modification in cancer diagnosis and treatment. Ovarian cancer is the leading cause of death among all gynecological cancers. It is urgent to identify new biomarkers for early diagnosis and prognosis of ovarian cancer. OBJECTIVE: In the current study, we aimed to evaluate the m6A RNA methylation regulators and m6A related genes and establish a new gene signature panel for the prognosis of ovarian cancer. METHODS: We downloaded the mutations data, FPKM data and corresponding clinical information of 373 patients with Ovarian Cancer (OC) from the TCGA database. We performed LASSO regression analysis and multivariate cox regression analysis to develop a risk-identifying gene signature panel. RESULTS: A total of 317 candidate m6A RNA methylation related genes were obtained. Finally, 12 - genes (WTAP, LGR6, ZC2HC1A, SLC4A8, AP2A1, NRAS, CUX1, HDAC1, CD79A, ACE2, FLG2 and LRFN1) were selected to establish the signature panel. We analyzed the genetic alterations of the selected 12 -genes in OC using cBioPortal database. Among the 373 patients, 368 patients have mutations. The results showed that all queried genes were altered in 137 of 368 cases (37.23%). The 12-gene signature panel was confirmed as an independent prognostic indicator (P =2.29E-18, HR = 1.699, 95% CI = 1.508-1.913). CONCLUSION: We established an effective m6A-related gene signature panel consisted of 12 -genes, which can predict the outcome of patients with OC. The high risk score indicates unfavorable survival. Our study provided novel insights into the relationship between m6A and OC. This gene signature panel will be helpful in identifying poor prognostic patients with OC and could be a promising prognostic indicator in clinical practice.

m6A RNA Methylation Regulators Impact Prognosis and Tumor Microenvironment in Renal Papillary Cell Carcinoma.

Accumulating evidence has proven that N6-methyladenosine (m6A) RNA methylation plays an essential role in tumorigenesis. However, the significance of m6A RNA methylation modulators in the malignant progression of papillary renal cell carcinoma (PRCC) and their impact on prognosis has not been fully analyzed. The present research set out to explore the roles of 17 m6A RNA methylation regulators in tumor microenvironment (TME) of PRCC and identify the prognostic values of m6A RNA methylation regulators in patients afflicted by PRCC. We investigated the different expression patterns of the m6A RNA methylation regulators between PRCC tumor samples and normal tissues, and systematically explored the association of the expression patterns of these genes with TME cell-infiltrating characteristics. Additionally, we used LASSO regression to construct a risk signature based upon the m6A RNA methylation modulators. Two-gene prognostic risk model including IGF2BP3 and HNRNPC was constructed and could predict overall survival (OS) of PRCC patients from the Cancer Genome Atlas (TCGA) dataset. The prognostic signature-based risk score was identified as an independent prognostic indicator in Cox regression analysis. Moreover, we predicted the three most significant small molecule drugs that potentially inhibit PRCC. Taken together, our study revealed that m6A RNA methylation regulators might play a significant role in the initiation and progression of PRCC. The results might provide novel insight into exploration of m6A RNA modification in PRCC and provide essential guidance for therapeutic strategies.

LncRNA CBR3-AS1 regulates of breast cancer drug sensitivity as a competing endogenous RNA through the JNK1/MEK4-mediated MAPK signal pathway.

BACKGROUND: Adriamycin (ADR) resistance is one of the main obstacles to improving the clinical prognosis of breast cancer patients. Long noncoding RNAs (lncRNAs) can regulate cell behavior, but the role of these RNAs in the anti-ADR activity of breast cancer remains unclear. Here, we aim to investigate the imbalance of a particular long noncoding RNA, lncRNA CBR3 antisense RNA 1 (CBR3-AS1), and its role in ADR resistance. METHODS: Microarray analysis of ADR-resistant breast cancer cells was performed to identify CBR3-AS1. CCK-8 and colony formation assays were used to detect the sensitivity of breast cancer cells to ADR. Dual-luciferase reporter, RNA pulldown, IHC and western blot analyses were used to verify the relationship between the expression of CBR3-AS1, miRNA and target genes. For in vivo experiments, the effect of CBR3-AS1 on breast cancer resistance was observed in a xenograft tumor model. The role of CBR3-AS1 in influencing ADR sensitivity was verified by clinical breast cancer specimens from the TCGA, CCLE, and GDSC databases. RESULTS: We found that CBR3-AS1 expression was significantly increased in breast cancer tissues and was closely correlated with poor prognosis. CBR3-AS1 overexpression promoted ADR resistance in breast cancer cells in vitro and in vivo. Mechanistically, we identified that CBR3-AS1 functioned as a competitive endogenous RNA by sponging miR-25-3p. MEK4 and JNK1 of the MAPK pathway were determined to be direct downstream proteins of the CBR3-AS1/miR-25-3p axis in breast cancer cells. CONCLUSIONS: In summary, our findings demonstrate that CBR3-AS1 plays a critical role in the chemotherapy resistance of breast cancer by mediating the miR-25-3p and MEK4/JNK1 regulatory axes. The potential of CBR3-AS1 as a targetable oncogene and therapeutic biomarker of breast cancer was identified.

Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer.

NLRP3 inflammasome was introduced as a double-edged sword in tumorigenesis and influenced immunotherapy response by modulating host immunity. However, a systematic assessment of the NLRP3-inflammasome-related genes across human cancers is lacking, and the predictive role of NLRP3 inflammasome in cancer immunotherapy (CIT) response remains unexplored. Thus, in this study, we performed a pan-cancer analysis of NLRP3-inflammasome-related genes across 24 human cancers. Out of these 24 cancers, 15 cancers had significantly different expression of NLRP3-inflammasome-related genes between normal and tumor samples. Meanwhile, Cox regression analysis showed that the NLRP3 inflammasome score could be served as an independent prognostic factor in skin cutaneous melanoma. Further analysis indicated that NLRP3 inflammasome may influence tumor immunity mainly by mediating tumor-infiltrating lymphocytes and macrophages, and the effect of NLRP3 inflammasome on immunity is diverse across tumor types in tumor microenvironment. We also found that the NLRP3 inflammasome score could be a stronger predictor for immune signatures compared with tumor mutation burden (TMB) and glycolytic activity, which have been reported as immune predictors. Furthermore, analysis of the association between NLRP3 inflammasome and CIT response using six CIT response datasets revealed the predictive value of NLRP3 inflammasome for immunotherapy response of patients in diverse cancers. Our study illustrates the characterization of NLRP3 inflammasome in multiple cancer types and highlights its potential value as a predictive biomarker of CIT response, which can pave the way for further investigation of the prognostic and therapeutic potentials of NLRP3 inflammasome.

Integrative Analysis of DNA Methylation and Gene Expression to Determine Specific Diagnostic Biomarkers and Prognostic Biomarkers of Breast Cancer.

Background: DNA methylation is a common event in the early development of various tumors, including breast cancer (BRCA), which has been studies as potential tumor biomarkers. Although previous studies have reported a cluster of aberrant promoter methylation changes in BRCA, none of these research groups have proved the specificity of these DNA methylation changes. Here we aimed to identify specific DNA methylation signatures in BRCA which can be used as diagnostic and prognostic markers. Methods: Differentially methylated sites were identified using the Cancer Genome Atlas (TCGA) BRCA data set. We screened for BRCA-differential methylation by comparing methylation profiles of BRCA patients, healthy breast biopsies and blood samples. These differential methylated sites were compared to nine main cancer samples to identify BRCA specific methylated sites. A BayesNet model was built to distinguish BRCA patients from healthy donors. The model was validated using three Gene Expression Omnibus (GEO) independent data sets. In addition, we also carried out the Cox regression analysis to identify DNA methylation markers which are significantly related to the overall survival (OS) rate of BRCA patients and verified them in the validation cohort. Results: We identified seven differentially methylated sites (DMSs) that were highly correlated with cell cycle as potential specific diagnostic biomarkers for BRCA patients. The combination of 7 DMSs achieved ~94% sensitivity in predicting BRCA, ~95% specificity comparing healthy vs. cancer samples, and ~88% specificity in excluding other cancers. The 7 DMSs were highly correlated with cell cycle. We also identified 6 methylation sites that are highly correlated with the OS of BRCA patients and can be used to accurately predict the survival of BRCA patients (training cohort: likelihood ratio = 70.25, p = 3.633 × 10-13, area under the curve (AUC) = 0.784; validation cohort: AUC = 0.734). Stratification analysis by age, clinical stage, Tumor types, and chemotherapy retained statistical significance. Conclusion: In summary, our study demonstrated the role of methylation profiles in the diagnosis and prognosis of BRCA. This signature is superior to currently published methylation markers for diagnosis and prognosis for BRCA patients. It can be used as promising biomarkers for early diagnosis and prognosis of BRCA.

A five-mRNA signature associated with post-translational modifications can better predict recurrence and survival in cervical cancer.

High mortality of patients with cervical cancer (CC) stresses the imperative of prognostic biomarkers for CC patients. Additionally, the vital status of post-translational modifications (PTMs) in the progression of cancers has been reported by numerous researches. Therefore, the purpose of this research was to dig a prognostic signature correlated with PTMs for CC. We built a five-mRNA (GALNTL6, ARSE, DPAGT1, GANAB and FURIN) prognostic signature associated with PTMs to predict both disease-free survival (DFS) (hazard ratio [HR] = 3.967, 95% CI = 1.985-7.927; P < .001) and overall survival (HR = 2.092, 95% CI = 1.138-3.847; P = .018) for CC using data from The Cancer Genome Atlas database. Then, the robustness of the signature was validated using GSE44001 and the Human Protein Atlas (HPA) database. CIBERSORT algorithm analysis displayed that activated CD4 memory T cell was also an independent indicator for DFS (HR = 0.426, 95% CI = 0.186-0.978; P = .044) which could add additional prognostic value to the signature. Collectively, the PTM-related signature and activated CD4 memory T cell can provide new avenues for the prognostic predication of CC. These findings give further insights into effective treatment strategies for CC, providing opportunities for further experimental and clinical validations.

A Combined four-mRNA Signature Associated with Lymphatic Metastasis for Prognosis of Colorectal Cancer.

Background: Colorectal cancer (CRC) is one of the most common malignant tumors in the world. Lymph node metastasis (LNM) is a common mode of metastasis of CRC. However, the combined mRNA biomarkers associated with LNM of CRC that can effectively predict CRC prognosis have not been reported yet. Methods: To identify biomarkers that are associated with LNM, we collected data from the The Cancer Genome Atlas (TCGA) database. The edgeR package was searched to seek LNM-related genes by comparisons between cancer samples and normal colorectal tissues and between LNM and non-LNM (NLNM) of CRC. Univariate and multivariate regression analysis of genes in the intersection to build gene signature associated with independent prognosis of CRC, and then verified by Kaplan-Meier curve and log-rank test, receiver operating characteristic (ROC) curve was used to determine the efficiency of survival prediction of our four-mRNA signature. Finally, the potential molecular mechanisms and properties of these gene signature were also explored with functional and pathway enrichment analysis. Results: 329 mRNAs were up-regulated in CRC tissues with LNM, and 8461 mRNAs were up-regulated in CRC tissues, the intersection is 100 mRNAs. After univariate and multivariate Cox regression analysis of 100 mRNAs, a novel four LNM related mRNAs (EPHA8, KRT85, GABRA3, and CLPSL1) were screened as independent prognostic indicators of CRC. Surprisingly, the four-mRNA signature can predict the prognosis of CRC patients independently of clinical factors andthe area under the curve (AUC) of the ROC is 0.730. The novel four-mRNA signature was used to identify high and low-risk groups. Stratified analysis indicated the risk score based on four-mRNA signature was an independent prognostic indicator for female, T3+T4, N1+N2 ,stage III+IV and patients with no new tumor event. Functional annotation of this risk model in high-risk patients revealed that pathways associated with neuroactive ligand-receptor interaction, estrogen signaling pathway, and steroid hormone biosynthesis. Conclusions: By conducting TCGA data mining, our study demonstrated that a four-mRNA signature associated with LNM can be used as a combined biomarker for independent prognosis of CRC.

Expression signature of six-snoRNA serves as novel non-invasive biomarker for diagnosis and prognosis prediction of renal clear cell carcinoma.

Increasing evidence has verified that small nucleolar RNAs (snoRNAs) play significant roles in tumorigenesis and exhibit prognostic value in clinical practice. In the study, we analysed the expression profile and clinical relevance of snoRNAs from TCGA database including 530 ccRCC (clear cell renal cell carcinoma) and 72 control cases. By using univariate and multivariate Cox analysis, we established a six-snoRNA signature and divided patients into high-risk or low-risk groups. We found patients in high-risk group had significantly shorter overall survival and recurrence-free survival than those in low-risk group in test series, validation series and entire series by Kaplan-Meier analysis. We also confirmed this signature had a great accuracy and specificity in 64 clinical tissue cases and 50 serum samples. Then, depending on receiver operating characteristic curve analysis we found the six-snoRNA signature was an superior indicator better than conventional clinical factors (AUC = 0.732). Furthermore, combining the signature with TNM stage or Fuhrman grade were the optimal indicators (AUC = 0.792; AUC = 0.800) and processed the clinical applied value for ccRCC. Finally, we found the SNORA70B and its hose gene USP34 might directly regulate Wnt signalling pathway to promote tumorigenesis in ccRCC. In general, our study established a six-snoRNA signature as an independent and superior diagnosis and prognosis indicator for ccRCC.

Development of a novel gene signature in patients without Helicobacter pylori infection gastric cancer.

Gastric cancer (GC) is one of the most fatal common cancers in worldwide. Helicobacter pylori (H. pylori) infection is closely related to the development of GC, although the mechanism is still unclear. In our study, we aim to develop a robust messenger RNA (mRNA) signature associated with H. pylori (-) GC that can sensitively and efficiently predict the prognostic. The RNA-seq expression profile and corresponding clinical data of 598 gastric cancer samples and 63 normal samples obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database. Using gene set enrichment analysis H. pylori (+) GC and H. pylori (-) GC patients and normal samples to select certain genes for further analysis. Using univariate and multivariate Cox regression model to establish a gene signature for predicting the overall survival (OS). Finally, we identified G2/M related seven-mRNA signature (TGFB1, EGF, MKI67, ILF3, INCENP, TNPO2, and CHAF1A) closely related to the prognosis of patients with H. pylori (-) GC. The seven-mRNA signature was identified to act as an independent prognostic biomarker by stratified analysis and multivariate Cox regression analysis. It was also validated on two test groups from TCGA and GSE15460 and shown that patients with high-risk scores based on the expression of the seven mRNAs had significantly shorter survival times compared to patients with low-risk scores (P < .0001). In this study, we developed a seven-mRNA signature related to G2/M checkpoint from H. pylori (-) GCs that as an independent biomarker potentially with a good performance in predicting OS and might be valuable for the clinical management for patients with GC.

Identification of a panel of mitotic spindle-related genes as a signature predicting survival in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most malignant tumor types worldwide. Our objective was to identify a genetic signature that could predict the prognosis of patients with LUAD. We extracted gene data sets from The Cancer Genome Atlas and obtained differentially expressed genes that were highly expressed at every stage. These genes were analyzed using gene set enrichment analysis to obtain four biological processes associated with LUAD. Subsequently, Cox univariate and multivariate analyses were performed to generate four optimized models (G2M checkpoint, E2F targets, mitotic spindle, and glycolysis). We identified a mitotic spindle-related signature (KIF15, BUB1, CCNB2, CDK1, KIF4A, DLGAP5, ECT2, and ANLN), which could be an independent prognostic indicator, to predict the prognosis of patients with LUAD. This new discovery should offer opportunities to explore the pathogenesis of LUAD and prove clinically useful in predicting LUAD patient prognosis.

Bioinformatic profiling identifies a platinum-resistant-related risk signature for ovarian cancer.

Most high-grade serous ovarian cancer (HGSOC) patients develop resistance to platinum-based chemotherapy and recur. Many biomarkers related to the survival and prognosis of drug-resistant patients have been delved by mining databases; however, the prediction effect of single-gene biomarker is not specific and sensitive enough. The present study aimed to develop a novel prognostic gene signature of platinum-based resistance for patients with HGSOC. The gene expression profiles were obtained from Gene Expression Omnibus and The Cancer Genome Atlas database. A total of 269 differentially expressed genes (DEGs) associated with platinum resistance were identified (P < .05, fold change >1.5). Functional analysis revealed that these DEGs were mainly involved in apoptosis process, PI3K-Akt pathway. Furthermore, we established a set of seven-gene signature that was significantly associated with overall survival (OS) in the test series. Compared with the low-risk score group, patients with a high-risk score suffered poorer OS (P < .001). The area under the curve (AUC) was found to be 0.710, which means the risk score had a certain accuracy on predicting OS in HGSOC (AUC > 0.7). Surprisingly, the risk score was identified as an independent prognostic indicator for HGSOC (P < .001). Subgroup analyses suggested that the risk score had a greater prognostic value for patients with grade 3-4, stage III-IV, venous invasion and objective response. In conclusion, we developed a seven-gene signature relating to platinum resistance, which can predict survival for HGSOC and provide novel insights into understanding of platinum resistance mechanisms and identification of HGSOC patients with poor prognosis.