Broadband Organic Ternary Bulk Heterojunctions Photodetector Based on Non-Fullerene Acceptor with Enhanced Flat-Spectrum Response Range from 200 to 1100 nm

Both flat-spectrum responsivity and high external quantum efficiency (EQE) of bulk heterojunction organic photodetectors (BHJ OPDs) are greatly in demand and still challenging to realize from the ultraviolet (UV) to near-infrared (NIR) regions. In this article, conjugated polymer donor poly­(3-hexylthiophene) (P3HT) and PTB7-Th are blended with a low band gap nonfullerene acceptor (NFA) IEICO-4F to form a ternary BHJ active layer, thereby forming a BHJ OPD with a broadband responsivity spectrum from UV to visible light to NIR region (200–1100 nm). Under 6 V voltage and in the range from 280 to 810 nm, the ternary BHJ OPD shows a relatively flat responsivity spectrum, and the highest responsivity is 1.348 A/W, which is 1.34 times that of the binary BHJ OPD. Specifically, the ternary BHJ OPD achieved the highest EQE at 285 nm and as high as 449.31%. In addition, the ternary OPD detectivity (D*) is 2.65 times that of the binary BHJ OPD. Therefore, ternary BHJ as an active layer provides an effective method to develop BHJ OPDs with an expanded response range, higher responsivity, improved EQE, and broadband spectrum with flat spectral response from the UV to NIR region

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

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

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

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

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

Additional file 1 of CRISPR-Cas9 identifies growth-related subtypes of glioblastoma with therapeutical significance through cell line knockdown

Supplementary Material

Table_6_Construction of a diagnostic signature and immune landscape of pulmonary arterial hypertension.XLSX

BackgroundMolecular biomarkers are widely used for disease diagnosis and exploration of pathogenesis. Pulmonary arterial hypertension (PAH) is a rapidly progressive cardiopulmonary disease with delayed diagnosis. Studies were limited regarding molecular biomarkers correlated with PAH from a broad perspective.MethodsTwo independent microarray cohorts comprising 73 PAH samples and 36 normal samples were enrolled in this study. The weighted gene co-expression network analysis (WGCNA) was performed to identify the key modules associated with PAH. The LASSO algorithm was employed to fit a diagnostic model. The latent biology mechanisms and immune landscape were further revealed via bioinformatics tools.ResultsThe WGCNA approach ultimately identified two key modules significantly associated with PAH. For genes within the two models, differential expression analysis between PAH and normal samples further determined nine key genes. With the expression profiles of these nine genes, we initially developed a PAH diagnostic signature (PDS) consisting of LRRN4, PI15, BICC1, PDE1A, TSHZ2, HMCN1, COL14A1, CCDC80, and ABCB1 in GSE117261 and then validated this signature in GSE113439. The ROC analysis demonstrated outstanding AUCs with 0.948 and 0.945 in two cohorts, respectively. Besides, patients with high PDS scores enriched plenty of Th17 cells and neutrophils, while patients with low PDS scores were dramatically related to mast cells and B cells.ConclusionOur study established a robust and promising signature PDS for diagnosing PAH, with key genes, novel pathways, and immune landscape offering new perspectives for exploring the molecular mechanisms and potential therapeutic targets of PAH.</p

Additional file 1 of PI3K pathway mutation predicts an activated immune microenvironment and better immunotherapeutic efficacy in head and neck squamous cell carcinoma

Additional file 1: Fig. S1. Overall workflow diagram of our research. Fig. S2. Landscape and clinical prognostic value of PI3K pathway mutation in the MD-Anderson cohort as well as heatmaps of immune cells infiltration and boxplots of immunomodulators expression. (A) Oncoplot depicts the landscape of PI3K pathway gene mutation in the MD-Anderson cohort. (B) Multivariate Cox regression analysis of PI3K pathway mutation phenotype in the MD-Anderson cohort. (C-D) Kaplan-Meier survival analysis of OS (C) and DFS (D) between the PI3K pathway mutation and wild groups in the MD-Anderson cohort. (E-F) Assess infiltration abundance of 22 immune cells calculated by CIBERSORT (E) as well as 39 immune cells and stromal cells calculated by xCell (F). (G) Boxplots represent different expression levels of 18 ligand molecules between the two groups. WT, wild type; MT, mutant type; *P < 0.05. Fig. S3. The relationship between PI3K pathway mutation and HPV status and patient prognosis in the TCGA-HNSC cohort. (A) Boxplots represent different expression levels of 19 receptor molecules between the PI3K pathway mutation and wild groups. (B) Composition percentage of HPV status between the PI3K pathway mutation and wild groups. (C) Composition percentage of PI3K pathway mutation status between the HPV-negative and HPV-positive groups. (D) Kaplan-Meier survival analysis of the HPV status in the TCGA-HNSC cohort. (E) Kaplan-Meier survival analysis of PI3K pathway mutation in the HPV-negative group patients of TCGA-HNSC cohort. (F-G) Kaplan-Meier survival analysis of HPV status in the PI3K pathway wild (F) and mutation (G) group patients of TCGA-HNSC cohort. WT, wild type; MT, mutant type; *P < 0.05; **P < 0.01. Table S1. The 29 PI3K pathway genes used to define samples as PI3K pathway mutation or wild groups