A novel prognostic immunoscore based on The Cancer Genome Atlas to predict overall survival in colorectal cancer patients

Colorectal cancer (CRC) is highly prevalent worldwide. The relationship between the infiltration of immunocytes in CRC and clinical outcome has been investigated in recent years. The present study aims to construct a new prognostic signature using an immunocyte panel. Our novel prognostic immunoscore included 13 types of immunocytes, which were identified by least absolute shrinkage and selection operator (LASSO)-Cox regression. The time-dependent receiver operating characteristic (ROC) curve and Kaplan–Meier survival estimates were applied to evaluate the prognostic ability. Compared with the signature based on a single immune marker (i.e., CD8 mRNA expression and CD8⁺ expressing T cells), the novel prognostic immunoscore possessed better specificity and sensitivity of prognosis (area under the curves (AUCs) are 0.852, 0.856, and 0.774 for 1-, 2-, and 3-year survival times, respectively). Significant differences were identified between the high and low immunoscore groups in overall survival and disease-free survival in training and validation cohorts. Combining the immunoscore with clinical information may provide a more accurate prognosis for CRC. The immunoscore can identify patients with poor outcomes in the high Tumor Mutational Burden (TMB) group, who may benefit the most from immunotherapy. The immunoscore was also closely related to two immune checkpoints (i.e., PD-L1 and PD-1, r = 0.3087 and r = 0.3341, respectively). Collectively, our study demonstrates that the novel prognostic immunoscore reported here may be useful in distinguishing different prognoses and may improve the clinical management of patients with CRC.     

Identification of novel prognostic alternative splicing signature in papillary renal cell carcinoma

Papillary renal cell carcinoma (pRCC) is a heterogeneous disease containing multifocal or solitary tumors with an aggressive phenotype. Increasing evidence has indicated the involvement of aberrant splicing variants in renal cell cancer, while systematic profiling of aberrant alternative splicing (AS) in pRCC was lacking and largely unknown. In the current study, comprehensive profiling of AS events were performed based on the integration of pRCC cohort from the Cancer Genome Atlas database and SpliceSeq software. With rigorous screening and univariate Cox analysis, a total of 2077 prognoses AS events from 1642 parent genes were identified. Then, stepwise least absolute shrinkage and selection operator method-penalized Cox regression analyses with 10-fold cross-validation followed by multivariate Cox regression were used to construct the prognostic AS signatures within each AS type. And a final 21 AS event-based signature was proposed which showed potent prognostic capability in stratifying patients into low- and high-risk subgroups (P < .0001). Furthermore, time-dependent receiver operating characteristics curves confirmed that the final AS signature was effective and robust in predicting overall survival for pRCC patients with the area under the curve above 0.9 from 1 to 5 years. In addition, splicing correlation network was built to uncover the potential regulatory pattern among prognostic splicing factors and candidate AS events. Besides, gene set enrichment analysis revealed the involvement of these candidates AS events in tumor-related pathways including extracellular matrix organization, oxidative phosphorylation, and P53 signaling pathways. Taken together, our results could contribute to elucidating the underlying mechanism of AS in the oncogenesis process and broaden the novel field of prognostic and clinical application of molecule-targeted approaches in pRCC.     

Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis

Background: Lung adenocarcinoma (LUAD) is the most frequent histological type of lung cancer, and its incidence has displayed an upward trend in recent years. Nevertheless, little is known regarding effective biomarkers for LUAD.Methods: The robust rank aggregation method was used to mine differentially expressed genes (DEGs) from the gene expression omnibus (GEO) datasets. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to extract hub genes from the protein–protein interaction (PPI) network. The expression of the hub genes was validated using expression profiles from TCGA and Oncomine databases and was verified by real-time quantitative PCR (qRT-PCR). The module and survival analyses of the hub genes were determined using Cytoscape and Kaplan–Meier curves. The function of KIF4A as a hub gene was investigated in LUAD cell lines.Results: The PPI analysis identified seven DEGs including BIRC5, DLGAP5, CENPF, KIF4A, TOP2A, AURKA, and CCNA2, which were significantly upregulated in Oncomine and TCGA LUAD datasets, and were verified by qRT-PCR in our clinical samples. We determined the overall and disease-free survival analysis of the seven hub genes using GEPIA. We further found that CENPF, DLGAP5, and KIF4A expressions were positively correlated with clinical stage. In LUAD cell lines, proliferation and migration were inhibited and apoptosis was promoted by knocking down KIF4A expression.Conclusion: We have identified new DEGs and functional pathways involved in LUAD. KIF4A, as a hub gene, promoted the progression of LUAD and might represent a potential therapeutic target for molecular cancer therapy.     

Identification of prognostic biomarkers for breast cancer brain metastases based on the bioinformatics analysis

PurposeThe prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs.Patients and methodsThe gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes.ResultsA total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways.ConclusionOur study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.     

Unlocking the potential of survival data for model organisms through a new database and online analysis platform: SurvCurv

Lifespan measurements, also called survival records, are a key phenotype in research on aging. If external hazards are excluded, aging alone determines the mortality in a population of model organisms. Understanding the biology of aging is highly desirable because of the benefits for the wide range of aging‐related diseases. However, it is also extremely challenging because of the underlying complexity. Here, we describe SurvCurv, a new database and online resource focused on model organisms collating survival data for storage and analysis. All data in SurvCurv are manually curated and annotated. The database, available at www.ebi.ac.uk/thornton-srv/databases/SurvCurv/ , offers various functions including plotting, Cox proportional hazards analysis, mathematical mortality models and statistical tests. It facilitates reanalysis and allows users to analyse their own data and compare it with the largest repository of model‐organism data from published experiments, thus unlocking the potential of survival data and demographics in model organisms.     

Analysis of key genes and signaling pathways involved in Helicobacter pylori‐associated gastric cancer based on The Cancer Genome Atlas database and RNA sequencing data

Background

Helicobacter pylori (H. pylori) infection is associated with the development of gastric cancer, although the mechanism is unclear. Herein, this study aimed to clarify the key genes and signaling pathways involved in H. pylori pathogenesis based on The Cancer Genome Atlas (TCGA) database and RNA sequencing analysis.

Materials and Methods

Forty‐nine gastric cancer samples (16 with H. pylori and 33 without H. pylori) and 35 cancer‐adjacent normal samples from TCGA database were analyzed by bioinformatics. The differentially expressed genes between H. pylori‐positive and H. pylori‐negative patients were verified in 18 gastric cancer (GC) samples (9 with H. pylori and 9 without H. pylori), which were analyzed using RNA sequencing. Survival analysis was carried out to explore associations between the differentially expressed genes and prognosis. Bioinformatics analysis was performed to determine the signaling pathways associated with H. pylori.

Results

The baseline level of clinical features from TCGA database and RNA sequencing showed no differences between the H. pylori‐positive and H. pylori‐negative GC groups (P > 0.05). TP53 was shown to be upregulated in the H. pylori‐positive group in both TCGA database and RNA sequencing data, which also showed higher expression in the GC tissues than in adjacent normal tissues (P < 0.05). CCDC151, CHRNB2, GMPR2, HDGFRP2, and VSTM2L were shown to be downregulated in the H. pylori‐positive group by both TCGA database and RNA sequencing, which also showed lower expression in the GC tissues than in adjacent normal tissues (P < 0.05). GC patients with low expression levels of HDGFRP2 had a poor prognosis (P < 0.05). Thirty‐three signaling pathways and 10 biological processes were found to be positively associated with H. pylori infection (P < 0.05, FDR < 0.05).

Conclusions

These results indicate that some genes (TP53, CCDC151, CHRNB2, GMPR2, HDGFRP2, VSTM2L) and previously unidentified signaling pathways (eg, the Hippo signaling pathway) might play an important role in H. pylori‐associated GC.     

Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas

Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.     

The identification of new biomarkers for bladder cancer: A study based on TCGA and GEO datasets

Bladder cancer (BC) is one of the most common neoplastic diseases worldwide. With the highest recurrence rate among all cancers, treatment of BC only improved a little in the last 30 years. Available biomarkers are not sensitive enough for the diagnosis of BC, whereas the standard diagnostic method, cystoscopy, is an invasive test and expensive. Hence, seeking new biomarkers of BC is urgent and challenging. With that order, we screened the overlapped differentially expressed genes (DEGs) of GSE13507 and TCGA BLCA datasets. Subsequent protein–protein interactions network analysis recognized the hub genes among these DEGs. Further functional analysis including Gene Ontology and KEGG pathway analysis and gene set enrichment analysis were processed to investigate the role of these genes and potential underlying mechanisms in BC. Kaplan–Meier analysis and Cox hazard ratio analysis were carried out to clarify the diagnostic and prognostic role of these genes. In conclusion, our present study demonstrated that ACTA2, CDC20, MYH11, TGFB3, TPM1, VIM, and DCN are all potential diagnostic biomarkers for BC. And may also be potential treatment targets for clinical implication in the future.     

Relative impact of earlier diagnosis and improved treatment on survival for colorectal cancer: A US database study among elderly patients

PurposesTo estimate what proportion of improvement in relative survival was attributable to smaller stage/size due to early detection and what proportion was attributable to cancer chemotherapy in patients with colorectal cancer (CRC).MethodsWe studied 69,718 patients with CRC aged ≥66 years in 1992–2009 from Surveillance, Epidemiology, and End Results registries. Study periods were categorized into three periods according to the major changes or advances in screening and chemotherapy regimens: (1) Period-1 (1992–1995), during which there was no evidence-based recommendation for routine CRC screening and 5-fluorouracil was the mainstay for chemotherapy; (2) Period-2 (1996–2000), during which evidences and guidelines supported the use of fecal occult blood test (FOBT) and sigmoidoscopy for routine CRC screening; and (3) Period-3 (2001–2009), during which Medicare Program added the full coverage for colonoscopy screening to average-risk individuals, and several newly developed chemotherapy regimens were approved. Outcome variables included the likelihood of being diagnosed at an early stage or with a small tumor size, and improvement in relative survival.ResultsCompared to period-1, likelihood of being diagnosed with early stage CRC increased by 20% in period-2 (odds ratio = 1.2, 95%CI: 1.1–1.2) and 30% in period-3 (1.3, 1.2–1.4); and likelihood of being diagnosed with small-size CRC increased by 60% in period-2 and 110% in period-3. Similarly, 5-year overall relative survival increased from 51% in period-1 to 56% in period-2 and 60% in period-3. Increase in survival attributable to migration in stage/size was 9% in period-2 and 20% in period-3, while the remaining survival improvement during period-2 and period-3 were largely attributable to more effective chemotherapy regimens (≥71.6%) and other treatment factors (≤25%).ConclusionsImprovements in CRC screening resulted in a migration of CRC toward earlier tumor stage and smaller size, which contributed to ≤20% of survival increase. Survival improvement over the past 2 decades was largely explained by more effective chemotherapy regimens (≥71.6%).     

基于时空分析的生态保护与修复试点工程实施效果评估——以赣州市为例

以“山水林田湖草生命共同体”为中心思想,从生态系统的格局和质量两个方面,对赣州市山水林田湖生态保护与修复试点工程实施效果进行了综合评估。结果如下：（1）从生态系统格局来看,2015到2018年,赣州市森林、农田和城镇生态系统面积明显增加,超过40%的草地生态系统转变为森林生态系统,且近45%的城镇生态系统面积增量由草地生态系统贡献。此外,多数自然生态系统的斑块破碎化加剧,森林生态系统破碎化现象最为明显,最大斑块指数从54.36降低到37.41,而半自然生态系统最大斑块指数增大。（2）从生态系统质量来看,赣州市归一化植被指数稳定在0.7以上并呈增长趋势,水土流失综合治理面积从16543.8 km²增长到18550.4 km²,重点流域水质基本稳定在Ⅱ、Ⅲ级,城镇生态系统受土壤重金属污染的风险较小,但部分县区农田生态系统受一种或多种土壤重金属污染的风险较大。整体而言,赣州市山水林田湖生态保护与修复试点工程取得了显著成效,较好的完成了实施方案中的规划目标。在进一步的生态保护与修复工作中,应重视赣州市自然生态系统斑块破碎化严重地区、水质出现波动较大的河流断面以及农田生态系统受土壤重金属污染威胁较大的县区。