Computed tomography-measured bone mineral density as a surrogate marker of survival after resection of colorectal liver metastases

Bone mineral density as a prognostic marker in patients with biliary tract cancer undergoing surgery

Background

Biliary tract cancer (BTC) is one of the most aggressive malignancies and surgery represents the only curative treatment approach. However, even in patients with complete tumor resection 5-year survival rates are below 30%. So far, prognostic markers to assess the outcome of these patients are lacking. We therefore evaluated bone mineral density (BMD) as a prognostic tool in patients receiving surgery for BTC.

methods

76 BTC patients undergoing tumor resection in our clinic (Duesseldorf cohort) as well as an external validation cohort of 34 BTC patients (Cologne cohort) were included. BMD was analyzed at the first lumbar vertebra, using routine CT scans which has been proven comparable to DXA.

Results

Median overall survival (OS) of the Duesseldorf cohort after surgery was 527 days, one- and five-year survival probabilities were 62 and 18%. Patients with BMD above 156.5 HU had significantly improved OS (1435 days vs. 459 days; p = 0.002). The prognostic value for BMD was confirmed using Cox-regression analysis, as well as an external validation cohort. In subgroup analysis the prognostic effect of BMD was only present in female patients, suggesting sex specific differences.

Conclusion

BMD is a valuable, easily accessible and independent prognostic marker in patients receiving liver surgery for BTC.

Post-diagnosis adiposity and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta-analysis

The adiposity influence on colorectal cancer prognosis remains poorly characterised. We performed a systematic review and meta-analysis on post-diagnosis adiposity measures (body mass index [BMI], waist circumference, waist-to-hip ratio, weight) or their changes and colorectal cancer outcomes. PubMed and Embase were searched through 28 February 2022. Random-effects meta-analyses were conducted when at least three studies had sufficient information. The quality of evidence was interpreted and graded by the Global Cancer Update Programme (CUP Global) independent Expert Committee on Cancer Survivorship and Expert Panel. We reviewed 124 observational studies (85 publications). Meta-analyses were possible for BMI and all-cause mortality, colorectal cancer-specific mortality, and cancer recurrence/disease-free survival. Non-linear meta-analysis indicated a reverse J-shaped association between BMI and colorectal cancer outcomes (nadir at BMI 28 kg/m2). The highest risk, relative to the nadir, was observed at both ends of the BMI distribution (18 and 38 kg/m2), namely 60% and 23% higher risk for all-cause mortality; 95% and 26% for colorectal cancer-specific mortality; and 37% and 24% for cancer recurrence/disease-free survival, respectively. The higher risk with low BMI was attenuated in secondary analyses of RCTs (compared to cohort studies), among studies with longer follow-up, and in women suggesting potential methodological limitations and/or altered physiological state. Descriptively synthesised studies on other adiposity-outcome associations of interest were limited in number and methodological quality. All the associations were graded as limited (likelihood of causality: no conclusion) due to potential methodological limitations (reverse causation, confounding, selection bias). Additional well-designed observational studies and interventional trials are needed to provide further clarification.

Prognostic and metabolic characteristics of a novel cuproptosis-related signature in patients with hepatocellular carcinoma

Cuproptosis is a novel discovered mode of programmed cell death. To identify the molecular regulatory patterns related to cuproptosis, this study was designed for exploring the correlation between cuproptosis-related genes (CRGs) and the prognosis, metabolism, and treatment of hepatocellular carcinoma (HCC). Cancer Genome Atlas (TCGA) database was used to screen 363 HCC samples, which were categorized into 2 clusters based on the expression of CRGs. Survival analysis demonstrated that overall survival (OS) was better in Cluster 1 than Cluster 2 which might to be relevant to differences in metabolic based on functional analysis. With LASSO regression analysis and univariate COX regression, 8 prognosis-related genes were screened, a differently expressed genes (DEGs) were then constructed (HCC patients' DEGs)-based signature. The signature's stability was also validated in the 2 independent cohorts and test cohorts (GSE14520, HCC dataset in PCAWG). The 1-year, 3-year, and 5-year area under the curve (AUC) were 0.756, 0.706, and 0.722, respectively. The signature could also well predict the response to chemotherapy, targeted and transcatheter arterial chemoembolization (TACE) by providing a risk score. Moreover, the correlation was uncovered by the research between the metabolism and risk score. In conclusion, a unique cuproptosis-related signature that be capable of predicting patients' prognosis with HCC, and offered valuable insights into chemotherapy, TACE and targeted therapies for these patients has been developed.

Identification of novel prognostic targets in glioblastoma using bioinformatics analysis

Background

Glioblastoma (GBM) is the most malignant grade of glioma. Highly aggressive characteristics of GBM and poor prognosis cause GBM-related deaths. The potential prognostic biomarkers remain to be demonstrated. This research builds up predictive gene targets of expression alterations in GBM utilizing bioinformatics analysis.

Methods and results

The microarray datasets (GSE15824 and GSE16011) associated with GBM were obtained from Gene Expression Omnibus (GEO) database to identify the differentially expressed genes (DEGs) between GBM and non-tumor tissues. In total, 719 DEGs were obtained and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for function enrichment analysis. Furthermore, we constructed protein–protein Interaction (PPI) network among DEGs utilizing Search Tool for the Retrieval of Interacting Genes (STRING) online tool and Cytoscape software. The DEGs of degree > 10 was selected as hub genes, including 73 upregulated genes and 21 downregulated genes. Moreover, MCODE application in Cytoscape software was employed to identify three key modules involved in GBM development and prognosis. Additionally, we used the Gene expression profiling and interactive analyses (GEPIA) online tool to further confirm four genes involving in poor prognosis of GBM patients, including interferon-gamma-inducible protein 30 (IFI30), major histocompatibility complex class II-DM alpha (HLA-DMA), Prolyl 4-hydroxylase beta polypeptide (P4HB) and reticulocalbin-1 (RCN1). Furthermore, the correlation analysis indicated that the expression of IFI30, an acknowledged biomarker in glioma, was positively correlated with HLA-DMA, P4HB and RCN1. RCN1 expression was positively correlated with P4HB and HLA-DMA. Moreover, qRT-PCR and immunohistochemistry analysis further validated the upregulation of four prognostic markers in GBM tissues.

Conclusions

Analysis of multiple datasets combined with global network information and experimental verification presents a successful approach to uncover the risk hub genes and prognostic markers of GBM. Our study identified four risk- and prognostic-related gene signatures, including IFI30, HLA-DMA, P4HB and RCN1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of GBM.