DataSheet_1_Stemness Refines the Classification of Colorectal Cancer With Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options.docx

BackgroundStemness refers to the capacities of self-renewal and repopulation, which contributes to the progression, relapse, and drug resistance of colorectal cancer (CRC). Mounting evidence has established the links between cancer stemness and intratumoral heterogeneity across cancer. Currently, the intertumoral heterogeneity of cancer stemness remains elusive in CRC.MethodsThis study enrolled four CRC datasets, two immunotherapy datasets, and a clinical in-house cohort. Non-negative matrix factorization (NMF) was performed to decipher the heterogeneity of cancer stemness. Multiple machine learning algorithms were applied to develop a nine-gene stemness cluster predictor. The clinical outcomes, multi-omics landscape, potential mechanisms, and immune features of the stemness clusters were further explored.ResultsBased on 26 published stemness signatures derived by alternative approaches, we decipher two heterogeneous clusters, low stemness cluster 1 (C1) and high stemness cluster 2 (C2). C2 possessed a higher proportion of advanced tumors and displayed worse overall survival and relapse-free survival compared with C1. The MSI-H and CMS1 tumors tended to enrich in C1, and the mesenchymal subtype CMS4 was the prevalent subtype of C2. Subsequently, we developed a nine-gene stemness cluster predictor, which robustly validated and reproduced our stemness clusters in three independent datasets and an in-house cohort. C1 also displayed a generally superior mutational burden, and C2 possessed a higher burden of copy number deletion. Further investigations suggested that C1 enriched numerous proliferation-related biological processes and abundant immune infiltration, while C2 was significantly associated with mesenchyme development and differentiation. Given results derived from three algorithms and two immunotherapeutic cohorts, we observed C1 could benefit more from immunotherapy. For patients with C2, we constructed a ridge regression model and further identified nine latent therapeutic agents, which might improve their clinical outcomes.ConclusionsThis study proposed two stemness clusters with stratified prognosis, multi-omics landscape, potential mechanisms, and treatment options. Current work not only provided new insights into the heterogeneity of cancer stemness, but also shed light on optimizing decision-making in immunotherapy and chemotherapy.</p

DataSheet_2_Stemness Refines the Classification of Colorectal Cancer With Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options.xlsx

BackgroundStemness refers to the capacities of self-renewal and repopulation, which contributes to the progression, relapse, and drug resistance of colorectal cancer (CRC). Mounting evidence has established the links between cancer stemness and intratumoral heterogeneity across cancer. Currently, the intertumoral heterogeneity of cancer stemness remains elusive in CRC.MethodsThis study enrolled four CRC datasets, two immunotherapy datasets, and a clinical in-house cohort. Non-negative matrix factorization (NMF) was performed to decipher the heterogeneity of cancer stemness. Multiple machine learning algorithms were applied to develop a nine-gene stemness cluster predictor. The clinical outcomes, multi-omics landscape, potential mechanisms, and immune features of the stemness clusters were further explored.ResultsBased on 26 published stemness signatures derived by alternative approaches, we decipher two heterogeneous clusters, low stemness cluster 1 (C1) and high stemness cluster 2 (C2). C2 possessed a higher proportion of advanced tumors and displayed worse overall survival and relapse-free survival compared with C1. The MSI-H and CMS1 tumors tended to enrich in C1, and the mesenchymal subtype CMS4 was the prevalent subtype of C2. Subsequently, we developed a nine-gene stemness cluster predictor, which robustly validated and reproduced our stemness clusters in three independent datasets and an in-house cohort. C1 also displayed a generally superior mutational burden, and C2 possessed a higher burden of copy number deletion. Further investigations suggested that C1 enriched numerous proliferation-related biological processes and abundant immune infiltration, while C2 was significantly associated with mesenchyme development and differentiation. Given results derived from three algorithms and two immunotherapeutic cohorts, we observed C1 could benefit more from immunotherapy. For patients with C2, we constructed a ridge regression model and further identified nine latent therapeutic agents, which might improve their clinical outcomes.ConclusionsThis study proposed two stemness clusters with stratified prognosis, multi-omics landscape, potential mechanisms, and treatment options. Current work not only provided new insights into the heterogeneity of cancer stemness, but also shed light on optimizing decision-making in immunotherapy and chemotherapy.</p

Table4_Gene Expression Profile Reveals a Prognostic Signature of Non–MSI-H/pMMR Colorectal Cancer.XLSX

Studies have demonstrated that non–MSI-H/pMMR colorectal cancer (CRC) has a worse prognosis and relapse rate than microsatellite instability-high (MSI-H)/mismatch repair deficient (dMMR) CRC. Hence, searching for a novel tool to advance the prognostic management of non–MSI-H/pMMR CRC is vital. In this study, using three independent public cohorts and a clinical in-house cohort, we developed and validated a microsatellite stable–associated signature (MSSAS). The initial signature establishment was performed in GSE39582 (n = 454). This was followed by independent validation of this signature in The Cancer Genome Atlas–CRC (n = 312), GSE39084 (n = 54), and in-house cohort (n = 146). As a result, MSSAS was proven to be an independent risk factor for overall survival and relapse-free survival in non–MSI-H/pMMR CRC. Receiver operating characteristic analysis showed that MSSAS had a stable and accurate performance in all cohorts for 1, 3, and 5 years, respectively. Further analysis suggested that MSSAS performed better than age, gender, and the T, N, M, and AJCC stages, adjuvant chemotherapy, tumor mutation burden, neoantigen, and TP53, KRAS, BRAF, and PIK3CA mutations. The clinical validation was executed to further ensure the robustness and clinical feasibility of this signature. In conclusion, MSSAS might be a robust and promising biomarker for advancing clinical management of non–MSI-H/pMMR CRC.</p

Table3_Gene Expression Profile Reveals a Prognostic Signature of Non–MSI-H/pMMR Colorectal Cancer.XLSX

Studies have demonstrated that non–MSI-H/pMMR colorectal cancer (CRC) has a worse prognosis and relapse rate than microsatellite instability-high (MSI-H)/mismatch repair deficient (dMMR) CRC. Hence, searching for a novel tool to advance the prognostic management of non–MSI-H/pMMR CRC is vital. In this study, using three independent public cohorts and a clinical in-house cohort, we developed and validated a microsatellite stable–associated signature (MSSAS). The initial signature establishment was performed in GSE39582 (n = 454). This was followed by independent validation of this signature in The Cancer Genome Atlas–CRC (n = 312), GSE39084 (n = 54), and in-house cohort (n = 146). As a result, MSSAS was proven to be an independent risk factor for overall survival and relapse-free survival in non–MSI-H/pMMR CRC. Receiver operating characteristic analysis showed that MSSAS had a stable and accurate performance in all cohorts for 1, 3, and 5 years, respectively. Further analysis suggested that MSSAS performed better than age, gender, and the T, N, M, and AJCC stages, adjuvant chemotherapy, tumor mutation burden, neoantigen, and TP53, KRAS, BRAF, and PIK3CA mutations. The clinical validation was executed to further ensure the robustness and clinical feasibility of this signature. In conclusion, MSSAS might be a robust and promising biomarker for advancing clinical management of non–MSI-H/pMMR CRC.</p

Balloon dilation procedure in Group A.

An obvious coarctation was shown 4 weeks later in Group A (a); Balloon was dilated to terminate coarctation at week 4 (b); Coarctation was terminated after dilation (c).</p

Rabbit aortic aneurysm model with enlarging diameter capable of better mimicking human aortic aneurysm disease

<div><p>The self-healing phenomenon can be found in the elastase-induced abdominal aortic aneurysm (AAA) model, and an enlarging AAA model was successfully induced by coarctation. Unfortunately, aortic coarctation in these enlarging models is generally not found in human AAA disease. This study aimed to create an experiment model of enlarging AAA in rabbits to better mimic human aortic aneurysm disease. Eighty-four male New Zealand white rabbits were randomly divided into three equal groups: two aneurysm groups (A and B) and a SHAM group. Aneurysm group rabbits underwent extrinsic aortic stenosis below the right renal artery and received a 10-minute incubation of 60 μl elastase (1 unit/μl). Absorbable suture was used in Group A and nonabsorbable cotton thread was used in Group B. A sham operation was performed in the SHAM group. Aortic diameter was measured after 1, 3, 7, and 15 weeks; thereafter animals were sacrificed for histopathological, immunohistochemical and quantitative studies. Two rabbits died at 29 and 48 days, respectively, after operation in Group B. All aneurysms formed and enlarged progressively by 3 weeks in the Aneurysm groups. However, diameter enlargement in Group A was significantly lower than that in Group B at 7 weeks. Aneurysm groups developed intimal hyperplasia; intima-media thickness (IMT) increased significantly by week 7, and aortic media thickness and intima-media ratio (IMR) increased significantly by week 15. Marked destruction of elastin fibers and smooth muscle cells (SMCs) occurred 1 week later and increased progressively thereafter. Intimal hyperplasia and SMCs content in Group A increased significantly by week 15 compared with Group B. Aneurysm groups exhibited strong expression of matrix metalloproteinases 2 and 9 and RAM11 by week 1, and decreased progressively thereafter. In conclusion, this novel rabbit AAA model enlarges progressively without coarctation and is capable of better mimicking human aortic aneurysm disease.</p></div

Profiles of elatin content change by EVG staining.

<p>Elastin fibers were destroyed markedly by week 1, and elastin increased progressively thereafter. **<i>p</i> < 0.01, ***<i>p</i> < 0.0001. Original magnification ×400.</p

Smooth muscle cells changes and the profiles of content change.

<p>SMCs were destroyed markedly after 1 week, but increased progressively thereafter. SMCs content in Group A increased significantly by week 15 compared with Group B. **<i>p</i> < 0.01, ***<i>p</i> < 0.0001. Original magnification ×400.</p

NSCs injury after OGD/R is accompanied with miR-200a up-regulation.

(A) Primary NSCs were exposed to OGD/R conditions, and the expression level of miR-200a was then measured by qRT-PCR analysis. (B) NSCs were transfected with vector or shRNA against miR-200a, and the transfection efficiency was verified by detection of miR-200a expression also using qRT-PCR. ** P P < 0.001.</p

Profiles of aortic lumen perimeters.

<p>Media thickness increased significantly by week 15 in Group A (a); Intimal hyperplasia increased significantly by week 7 in the Aneurysm groups. Group A increased significantly by week 15 compared with Group B. (b); IMT increased significantly by week 7 in Group A (c); IMR increased significantly by week 15 in the Aneurysm groups (d). * <i>p</i> < 0.05, ** <i>p</i> < 0.01, *** <i>p</i> < 0.0001. IMR = intima-media ratio; IMT = intima-media thickness.</p