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

Image_1_Comprehensive Molecular Analyses of a Six-Gene Signature for Predicting Late Recurrence of Hepatocellular Carcinoma.tif

A larger number of patients with stages I–III hepatocellular carcinoma (HCC) experience late recurrence (LR) after surgery. We sought to develop a novel tool to stratify patients with different LR risk for tailoring decision-making for postoperative recurrence surveillance and therapy modalities. We retrospectively enrolled two independent public cohorts and 103 HCC tissues. Using LASSO logical analysis, a six-gene model was developed in the The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) and independently validated in GSE76427. Further experimental validation using qRT-PCR assays was performed to ensure the robustness and clinical feasible of this signature. We developed a novel LR-related signature consisting of six genes. This signature was validated to be significantly associated with dismal recurrence-free survival in three cohorts TCGA-LIHC, GSE76427, and qPCR assays [HR: 2.007 (1.200–3.357), p = 0.008; HR: 2.171 (1.068, 4.412), p-value = 0.032; HR: 3.383 (2.100, 5.450), p-value <0.001]. More importantly, this signature displayed robust discrimination in predicting the LR risk, with AUCs being 0.73 (TCGA-LIHC), 0.93 (GSE76427), and 0.85 (in-house cohort). Furthermore, we deciphered the specific landscape of molecular alterations among patients in nonrecurrence (NR) and LR group to analyze the mechanism contributing to LR. For high-risk group, we also identified several potential drugs with specific sensitivity to high- and low-risk groups, which is vital to improve prognosis of LR-HCC after surgery. We discovered and experimentally validated a novel gene signature with powerful performance for identifying patients at high LR risk in stages I–III HCC.</p

Table_1_Comprehensive Molecular Analyses of a Six-Gene Signature for Predicting Late Recurrence of Hepatocellular Carcinoma.xlsx

A larger number of patients with stages I–III hepatocellular carcinoma (HCC) experience late recurrence (LR) after surgery. We sought to develop a novel tool to stratify patients with different LR risk for tailoring decision-making for postoperative recurrence surveillance and therapy modalities. We retrospectively enrolled two independent public cohorts and 103 HCC tissues. Using LASSO logical analysis, a six-gene model was developed in the The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) and independently validated in GSE76427. Further experimental validation using qRT-PCR assays was performed to ensure the robustness and clinical feasible of this signature. We developed a novel LR-related signature consisting of six genes. This signature was validated to be significantly associated with dismal recurrence-free survival in three cohorts TCGA-LIHC, GSE76427, and qPCR assays [HR: 2.007 (1.200–3.357), p = 0.008; HR: 2.171 (1.068, 4.412), p-value = 0.032; HR: 3.383 (2.100, 5.450), p-value <0.001]. More importantly, this signature displayed robust discrimination in predicting the LR risk, with AUCs being 0.73 (TCGA-LIHC), 0.93 (GSE76427), and 0.85 (in-house cohort). Furthermore, we deciphered the specific landscape of molecular alterations among patients in nonrecurrence (NR) and LR group to analyze the mechanism contributing to LR. For high-risk group, we also identified several potential drugs with specific sensitivity to high- and low-risk groups, which is vital to improve prognosis of LR-HCC after surgery. We discovered and experimentally validated a novel gene signature with powerful performance for identifying patients at high LR risk in stages I–III HCC.</p

Image_4_Comprehensive Molecular Analyses of a Six-Gene Signature for Predicting Late Recurrence of Hepatocellular Carcinoma.tif

A larger number of patients with stages I–III hepatocellular carcinoma (HCC) experience late recurrence (LR) after surgery. We sought to develop a novel tool to stratify patients with different LR risk for tailoring decision-making for postoperative recurrence surveillance and therapy modalities. We retrospectively enrolled two independent public cohorts and 103 HCC tissues. Using LASSO logical analysis, a six-gene model was developed in the The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) and independently validated in GSE76427. Further experimental validation using qRT-PCR assays was performed to ensure the robustness and clinical feasible of this signature. We developed a novel LR-related signature consisting of six genes. This signature was validated to be significantly associated with dismal recurrence-free survival in three cohorts TCGA-LIHC, GSE76427, and qPCR assays [HR: 2.007 (1.200–3.357), p = 0.008; HR: 2.171 (1.068, 4.412), p-value = 0.032; HR: 3.383 (2.100, 5.450), p-value <0.001]. More importantly, this signature displayed robust discrimination in predicting the LR risk, with AUCs being 0.73 (TCGA-LIHC), 0.93 (GSE76427), and 0.85 (in-house cohort). Furthermore, we deciphered the specific landscape of molecular alterations among patients in nonrecurrence (NR) and LR group to analyze the mechanism contributing to LR. For high-risk group, we also identified several potential drugs with specific sensitivity to high- and low-risk groups, which is vital to improve prognosis of LR-HCC after surgery. We discovered and experimentally validated a novel gene signature with powerful performance for identifying patients at high LR risk in stages I–III HCC.</p

Image_2_Comprehensive Molecular Analyses of a Six-Gene Signature for Predicting Late Recurrence of Hepatocellular Carcinoma.tif

A larger number of patients with stages I–III hepatocellular carcinoma (HCC) experience late recurrence (LR) after surgery. We sought to develop a novel tool to stratify patients with different LR risk for tailoring decision-making for postoperative recurrence surveillance and therapy modalities. We retrospectively enrolled two independent public cohorts and 103 HCC tissues. Using LASSO logical analysis, a six-gene model was developed in the The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) and independently validated in GSE76427. Further experimental validation using qRT-PCR assays was performed to ensure the robustness and clinical feasible of this signature. We developed a novel LR-related signature consisting of six genes. This signature was validated to be significantly associated with dismal recurrence-free survival in three cohorts TCGA-LIHC, GSE76427, and qPCR assays [HR: 2.007 (1.200–3.357), p = 0.008; HR: 2.171 (1.068, 4.412), p-value = 0.032; HR: 3.383 (2.100, 5.450), p-value <0.001]. More importantly, this signature displayed robust discrimination in predicting the LR risk, with AUCs being 0.73 (TCGA-LIHC), 0.93 (GSE76427), and 0.85 (in-house cohort). Furthermore, we deciphered the specific landscape of molecular alterations among patients in nonrecurrence (NR) and LR group to analyze the mechanism contributing to LR. For high-risk group, we also identified several potential drugs with specific sensitivity to high- and low-risk groups, which is vital to improve prognosis of LR-HCC after surgery. We discovered and experimentally validated a novel gene signature with powerful performance for identifying patients at high LR risk in stages I–III HCC.</p

Table1_Gene Expression Profile Reveals a Prognostic Signature of Non–MSI-H/pMMR Colorectal Cancer.DOCX

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

Table2_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