The Effect of Direct Quenching on the Microstructure and Mechanical Properties of NiCrMo and Cu-Bearing High-Strength Steels

In this work, two types of 590 MPa grade steels, composed of NiCrMo steel and Cu-bearing steel, were processed using traditional offline quenching and tempering and direct quenching (DQ) and tempering. The influence of DQ on microstructural evolution and strengthening mechanisms of these two types of steel was investigated. Grain refinement and dislocation density increase were determined by controlled rolling and following the DQ process in both two types of steel. In Cu-bearing steels, the refined grains and high-density dislocation further promoted the precipitation behavior of Cu-rich particles and alloyed carbides during the tempering treatment. Compared with traditionally quenched and tempered steels, NiCrMo steels after the direct quenching and tempering (DQT) process achieved 106 MPa higher yield strength through grain refinement strengthening and dislocation strengthening, while the Cu-bearing steels after the DQT process achieved 159 MPa higher yield strength through grain refinement strengthening, dislocation strengthening, and precipitation strengthening. The contribution degree of different strengthening mechanisms was quantitatively analyzed. Grain refinement also compensated for the toughness loss caused by the increase in dislocation, leading to an impact energy of 237 J and 248 J at −84 °C for NiCrMo and Cu-bearing steels after DQT, respectively

Investigating the corrosion performance of hull steel with different microstructure in a tropical marine atmosphere

The corrosion behavior of ship steel with different microstructure in a simulated tropical marine atmosphere was systematically investigated. Results show that homogeneous martensite structure obtained by water-quenching process was much more resistant to corrosion than that of ferrite-pearlite. Furthermore, the precipitation of carbide resulting from tempering process promoted the formation of corrosive microcells within the matrix and further intensified the corrosion. Rapid cooling rate led to the enrichment of chromium, which formed protective corrosion products such as FeCr2O4 and Cr(OH)3. This also facilitated the conversion of α-FeOOH and significantly enhanced the corrosion resistance

Analysis of clinical characteristics and pathway differences between LIHC risk groups.

Correlation of risk scores with mRNAsi in the TCGA cohort (A) and the HCCDB18 cohort (B); Difference in risk score distribution between AJCC stage (C) and grade (D) in TCGA cohort; (E) Comparison of the distribution of risk score in HCCDB18 cohort across stages; (F) Pathway differences between risk groups in the TCGA cohort; (G) Comparison of GSEA analysis between risk groups in the TCGA cohort.</p

Construction of a prognostic gene risk model associated with CSCs and its survival analysis.

(A) Venn plot of CytoTRACE and mRNAsi prognosis-related genes; (B) Univariate analysis of 16 prognostic genes associated with CSCs; (C) Multivariate analysis of 4 prognostically critical genes in LIHC; (D) ROC Curves predict prognosis in LIHC patients at 1,3, and 5 years; (E-H) KM (E), DSS (F), DFI (G), and PFI (H) survival curves based on the TCGA-LIHC cohort; (I) Differences in survival status of patients in different risk groups in the TCGA-LIHC cohort; (J-K) ROC curves (J) and KM curves (K) based on HCCDB18 cohorts; (L) Differences in survival status of patients in different risk groups in the HCCDB18 cohort.</p

The number of genes in each module using WGCNA.

BackgroundLiver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC.MethodsThe single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively. Then, we explored the association of stemness indicators (CytoTRACE score and stemness index, mRNAsi) with survival outcomes and clinical characteristics by combining clinical information and survival analyses. Subsequently, weighted co-expression network analysis (WGCNA) and Cox were applied to assess mRNAsi-related genes in bulk LIHC data and construct a prognostic model for LIHC patients. Single-sample gene-set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) analysis were employed for immune infiltration assessment. Finally, the potential immunotherapeutic response was predicted by the Tumor Immune Dysfunction and Exclusion (TIDE), and the tumor mutation burden (TMB). Additionally, pRRophetic package was applied to evaluate the sensitivity of high and low-risk groups to common chemotherapeutic drugs.ResultsA total of four genes (including STIP1, H2AFZ, BRIX1, and TUBB) associated with stemness score (CytoTRACE score and mRNAsi) were identified and constructed a risk model that could predict prognosis in LIHC patients. It was observed that high stemness cells occurred predominantly in the late stages of LIHC and that poor overall survival in LIHC patients was also associated with high mRNAsi scores. In addition, pathway analysis confirmed the biological uniqueness of the two risk groups. Personalized treatment predictions suggest that patients with a low risk benefited more from immunotherapy, while those with a high risk group may be conducive to chemotherapeutic drugs.ConclusionThe current study developed a novel prognostic risk signature with genes related to CSCs, which provides novel ideas for the diagnosis, prognosis and treatment of LIHC.</div

A nomogram formed based on stem cells index-related risk score signature with clinical features.

Univariate (A) and multivariate (B) cox analysis of risk scores and clinical information; (C) A nomogram based on risk scores and clinical stage of AJCC; Nomogram-based calibration curves (D) and decision curves (E); (F) Differential expression of prognostic critical genes across risk groups in the TCGA cohort; (G) Expression distribution of prognostic critical genes in single-cell profile.</p

Profiling of cells in LIHC at the scRNA transcript level and cell stemness analysis.

Cells were clustered using optimal resolution 1. (A) Seven cell types in LIHC were annotated as the primary markers of the cluster; (B) Expression of marker genes in different cell types; Proportion of immune cells (C) and non-immune cells (D) in scRNA-seq data from LIHC; (E) UMAP plot of the distribution of epithelial cells of primary tumors at different stages; (F) CytoTRACE analysis in primary tumor epithelial cells; (G) Percentage of each cell type in different stages; (H) Top 10 Genes related to CytoTRACE score. *p < 0.05.</p

Detailed genes in brown module using WGCNA.

BackgroundLiver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC.MethodsThe single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively. Then, we explored the association of stemness indicators (CytoTRACE score and stemness index, mRNAsi) with survival outcomes and clinical characteristics by combining clinical information and survival analyses. Subsequently, weighted co-expression network analysis (WGCNA) and Cox were applied to assess mRNAsi-related genes in bulk LIHC data and construct a prognostic model for LIHC patients. Single-sample gene-set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) analysis were employed for immune infiltration assessment. Finally, the potential immunotherapeutic response was predicted by the Tumor Immune Dysfunction and Exclusion (TIDE), and the tumor mutation burden (TMB). Additionally, pRRophetic package was applied to evaluate the sensitivity of high and low-risk groups to common chemotherapeutic drugs.ResultsA total of four genes (including STIP1, H2AFZ, BRIX1, and TUBB) associated with stemness score (CytoTRACE score and mRNAsi) were identified and constructed a risk model that could predict prognosis in LIHC patients. It was observed that high stemness cells occurred predominantly in the late stages of LIHC and that poor overall survival in LIHC patients was also associated with high mRNAsi scores. In addition, pathway analysis confirmed the biological uniqueness of the two risk groups. Personalized treatment predictions suggest that patients with a low risk benefited more from immunotherapy, while those with a high risk group may be conducive to chemotherapeutic drugs.ConclusionThe current study developed a novel prognostic risk signature with genes related to CSCs, which provides novel ideas for the diagnosis, prognosis and treatment of LIHC.</div