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And Beyond由两名活泼的女性设计师约兰达．范登·布洛克（Jolanda vanden Broek）和布丽琦特，亨德里克斯（Brigitte Hendrix）搭档组成，她们于2005年毕业于阿姆斯特丹的里特维尔学术学院（Cerrit Rietveld Academy）的时装设计系，在上学的时候，两人经常一起做命题设计，在合作中渐渐发展成一对无话不谈的亲密搭档，相互之间的沟通也越来越默契。

女性的力量：And Beyond

Autophagy is a process in which intracellular components and dysfunctional organelles are delivered to the lysosome for degradation and recycling. Autophagy has various connections to a large number of human diseases, as its functions are essential for cell survival, bioenergetic homeostasis, organism development, and cell death regulation. In the past two decades, substantial effort has been made to identify the roles of autophagy in tumor suppression and promotion, neurodegenerative disorders, and other pathophysiologies. This review summarizes the current advances and discusses the unanswered questions in understanding the involvement of autophagy in pathogenic mechanisms of disease, primarily focusing on cancer and neurodegenerative diseases.

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Autophagy and disease: unanswered questions.

Recent studies have highlighted the biological significance of RNA N6-methyladenosine (m6A) modification in tumorigenicity and progression. However, it remains unclear whether m6A modifications also have potential roles in immune regulation and tumor microenvironment (TME) formation. Methods: In this study, we curated 23 m6A regulators and performed consensus molecular subtyping with NMF algorithm to determine m6A modification patterns and the m6A-related gene signature in colon cancer (CC). The ssGSEA and CIBERSORT algorithms were employed to quantify the relative infiltration levels of various immune cell subsets. An PCA algorithm based m6Sig scoring scheme was used to evaluate the m6A modification patterns of individual tumors with an immune response. Results: Three distinct m6A modification patterns were identified among 1307 CC samples, which were also associated with different clinical outcomes and biological pathways. The TME characterization revealed that the identified m6A patterns were highly consistent with three known immune profiles: immune-inflamed, immune-excluded, and immune-desert, respectively. Based on the m6Sig score, which was extracted from the m6A-related signature genes, CC patients can be divided into high and low score subgroups. Patients with lower m6Sig score was characterized by prolonged survival time and enhanced immune infiltration. Further analysis indicated that lower m6Sig score also correlated with greater tumor mutation loads, PD-L1 expression, and higher mutation rates in SMGs (e.g., PIK3CA and SMAD4). In addition, patients with lower m6Sig scores showed a better immune responses and durable clinical benefits in three independent immunotherapy cohorts. Conclusions: This study highlights that m6A modification is significantly associated with TME diversity and complexity. Quantitatively evaluating the m6A modification patterns of individual tumors will strengthen our understanding of TME characteristics and promote more effective immunotherapy strategies.

m6A regulator-based methylation modification patterns characterized by distinct tumor microenvironment immune profiles in colon cancer.

Autophagy, a highly conserved cellular proteolysis process, has been involved in non-small cell lung cancer (NSCLC). We tried to develop a prognostic prediction model for NSCLC patients based on the expression profiles of autophagy-associated genes. Univariate Cox regression analysis was used to determine autophagy-associated genes significantly correlated with overall survival (OS) of the TCGA lung cancer cohort. LASSO regression was performed to build multiple-gene prognostic signatures. We found that the 22-gene and 11-gene signatures could dichotomize patients with significantly different OS and independently predict the OS in TCGA lung adenocarcinoma (HR=2.801, 95% CI=2.252-3.486, P<0.001) and squamous cell carcinoma (HR=1.105, 95% CI=1.067-1.145, P<0.001), respectively. The prognostic performance of the 22-gene signature was validated in four GEO lung cancer cohorts. Moreover, GO, KEGG, and GSEA analyses unveiled several fundamental signaling pathways and cellular processes associated with the 22-gene signature in lung adenocarcinoma. We also constructed a clinical nomogram with a concordance index of 0.71 to predict the survival possibility of NSCLC patients by integrating clinical characteristics and the autophagy gene signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. Overall, we constructed and verified a novel autophagy-associated gene signature that could improve the individualized outcome prediction in NSCLC.

Prognostic Implications of Autophagy-Associated Gene Signatures in Non-Small Cell Lung Cancer

We postulated that expression differences of autophagy-related genes are instrumental in stratifying the risk of early relapse after surgery and evaluating the prognosis of patients with stages I-III colon cancer. Therefore, propensity score matching analysis was performed between patients in early relapse group and long-term survival group from GSE39582 test series and internal validation series. Using Cox regression model, a nine-autophagy-related signature (CAPN2, ATG16L2, TP63, SIRT1, RPS6KB1, PEX3, ATG5, UVRAG, NAF1) was established to classify patients into those at high risk of early relapse (high-risk group), and those at low risk of early relapse (low-risk group). Relapse-free survival (RFS) was significantly different between the two groups in test [hazard ratio (HR): 2.019, 95% confidence interval (CI): 1.362-2.992, P < 0.001], internal validation (HR: 2.464, 95% CI: 1.196-5.079, P < 0.001) and another two external validation series (GSE14333-HR: 2.250, 95% CI: 1.227-4.126, P = 0.007; GSE33113-HR: 5.552, 95% CI: 2.098-14.693, P < 0.001). Then, based on RFS, we developed a nomogram, integrating the nine-autophagy-related classifier and four clinicopathological risk factors to evaluate prognosis of stages I-III colon cancer patients. Time-dependent receiver operating curve at 2 years showed that the integrated signature (area under curve = 0.758) had better prognostic accuracy than American Joint Committee on Cancer TNM stage (area under curve = 0.620). In conclusion, we identified and built a nine-autophagy-related signature, a credible approach to early relapse prediction in stages I-III colon cancer patients, which can assist physicians in devising more efficient therapeutic strategies.

Prognostic and predictive value of an autophagy-related signature for early relapse in stages I-III colon cancer.

https://febs.onlinelibrary.wiley.com/doi/epdf/10.1002/1878-0261.12175

A robust gene signature for the prediction of early relapse in stage I–III colon cancer

The purpose of our study was to develop a multigene signature based on transcriptome profiles of both mRNAs and lncRNAs to identify a group of patients who are at high risk of early relapse in stages II-III colon cancer Firstly, propensity score matching was conducted between patients in early relapse group and long-term survival group from GSE39582 training series (N = 359) and patients were matched 1:1 Global transcriptome analysis was then performed between the paired groups to identify tumor specific mRNAs and lncRNAs Finally, using LASSO Cox regression model, we built a multigene early relapse classifier incorporating 15 mRNAs and three lncRNAs The prognostic and predictive accuracy of the signature was internally validated in 102 colon cancer patients and externally validated in other 241 patients In the training set, patients with high risk score were more likely to suffer from relapse than those with low risk score (HR: 267, 95% CI: 207-346, P < 0001) The results were validated in the internal validation set (HR: 223, 95% CI: 123-378, P = 0003) and external validation (HR 188, 95% CI 142-248; P < 0001) set Time-dependent receiver operating curve at 1 year showed that the integrated mRNA-lncRNA signature [area under curve (AUC) = 0742] had better prognostic accuracy than AJCC TNM stage (AUC = 0615) in the entire 702 patients In addition, survival decision curve analyses at 12 months revealed a good clinical usefulness of the integrated mRNA-lncRNA signature In conclusion, we successfully developed an integrated mRNA-lncRNA signature that can accurately predict early relapse

Transcriptome profiling reveals an integrated mRNA-lncRNA signature with predictive value of early relapse in colon cancer.

AIM: To hypothesize that in patients with colon cancer showing heavy intestinal wall invasion without distant metastasis (T4bN0-2M0), small tumor size would correlate with more aggressive tumor behaviors and therefore poorer cancer-specific survival (CSS).

METHODS: We analyzed T4bN0-2M0 colon cancer patients in the Surveillance, Epidemiology and End Results (SEER) database. A preliminary analysis of T4bN0-2M0 colon cancer patients at the Fudan University Shanghai Cancer Center is also presented.

RESULTS: A total of 1734 T4bN0-2M0 colon cancer patients from the SEER database were included. Kaplan-Meier analysis revealed decreasing CSS with decreasing tumor size (P < 0.001). Subgroup analysis showed a significant association between poorer CSS with smaller tumor size in T4bN0 patients (P = 0.024), and a trend of association in T4bN1 (P = 0.182) and T4bN2 patients (P = 0.191). Multivariate analysis identified tumor size as an independent prognostic factor for CSS in T4bN0-2M0 patients (P = 0.024). Preliminary analysis of Fudan University Shanghai Cancer Center samples suggested the 5-year CSS was 50.0%, 72.9% and 77.1% in patients with tumors ≤ 4.0 cm, 4.0-7.0 cm and ≥ 7.0 cm.

CONCLUSION: Smaller tumor size is associated with poorer CSS in the T4bN0-2M0 subset of colon cancer, particularly in the T4bN0M0 subgroup.

Yang Feng

Papers

Prognostic and predictive value of an autophagy-related signature for early relapse in stages I-III colon cancer.

A robust gene signature for the prediction of early relapse in stage I–III colon cancer

Transcriptome profiling reveals an integrated mRNA-lncRNA signature with predictive value of early relapse in colon cancer.

Smaller tumor size is associated with poor survival in T4b colon cancer.

Smaller tumor size is associated with poor survival in stage II colon cancer: An analysis of 7,719 patients in the SEER database