Development and Validation of a Nomogram for Predicting Survival Based on Ferritin and Transferrin Ratio in Breast Cancer Patients.

BACKGROUND: Our objective is to develop a predictive model utilizing the ferritin and transferrin ratio (FTR) and clinical factors to forecast overall survival (OS) in breast cancer (BC) patients. METHODS: We conducted a retrospective analysis of clinical data from 2858 BC patients diagnosed between 2013 and 2021. Subsequently, the cohort of 2858 BC patients underwent random assignment into distinct subsets: a training cohort comprising 2002 patients and a validation cohort comprising 856 patients, maintaining a proportional ratio of 7:3. Employing multivariable Cox regression analysis within the training cohort, we derived a prognostic nomogram. The predictive performance was assessed using calibration curves, C-index, and decision curve analysis. RESULTS: The final prognostic model included the TNM stage, subtype, hemoglobin levels, and the ferritin-transferrin ratio. The nomogram achieved a C-index of .794 (95% CI: .777-.810). The nomogram demonstrated superior predictive accuracy for OS at 3, 5, and 7 years for BC, with area under the time-dependent curves of .812, .782, and .773, respectively. These values notably outperformed those of the conventional TNM stage. Decision curve analysis reaffirmed the greater net benefit of our nomogram compared to the TNM stage. These findings were subsequently validated in the independent validation cohort. CONCLUSION: The FTR-based prognostic model may predict a patient's OS better than the TNM stage in a clinical setting. The nomogram can provide an early, affordable, and reliable tool for survival prediction, as well as aid clinicians in treatment option-making and prognosis evaluation. However, further multi-center prospective trials are required to confirm the reliability of the existing nomogram.

BackgroundOur objective is to develop a predictive model utilizing the ferritin and transferrin ratio (FTR) and clinical factors to forecast overall survival (OS) in breast cancer (BC) patients.MethodsWe conducted a retrospective analysis of clinical data from 2858 BC patients diagnosed between 2013 and 2021. Subsequently, the cohort of 2858 BC patients underwent random assignment into distinct subsets: a training cohort comprising 2002 patients and a validation cohort comprising 856 patients, maintaining a proportional ratio of 7:3. Employing multivariable Cox regression analysis within the training cohort, we derived a prognostic nomogram. The predictive performance was assessed using calibration curves, C-index, and decision curve analysis.ResultsThe final prognostic model included the TNM stage, subtype, hemoglobin levels, and the ferritin-transferrin ratio. The nomogram achieved a C-index of .794 (95% CI: .777-.810). The nomogram demonstrated superior predictive accuracy for OS at 3, 5, and 7 years for BC, with area under the time-dependent curves of .812, .782, and .773, respectively. These values notably outperformed those of the conventional TNM stage. Decision curve analysis reaffirmed the greater net benefit of our nomogram compared to the TNM stage. These findings were subsequently validated in the independent validation cohort.ConclusionThe FTR-based prognostic model may predict a patient's OS better than the TNM stage in a clinical setting. The nomogram can provide an early, affordable, and reliable tool for survival prediction, as well as aid clinicians in treatment option-making and prognosis evaluation. However, further multi-center prospective trials are required to confirm the reliability of the existing nomogram.

Cost-effective prognostic evaluation of breast cancer: using a STAR nomogram model based on routine blood tests.

Background: Breast cancer (BC) is the most common and prominent deadly disease among women. Predicting BC survival mainly relies on TNM staging, molecular profiling and imaging, hampered by subjectivity and expenses. This study aimed to establish an economical and reliable model using the most common preoperative routine blood tests (RT) data for survival and surveillance strategy management. Methods: We examined 2863 BC patients, dividing them into training and validation cohorts (7:3). We collected demographic features, pathomics characteristics and preoperative 24-item RT data. BC risk factors were identified through Cox regression, and a predictive nomogram was established. Its performance was assessed using C-index, area under curves (AUC), calibration curve and decision curve analysis. Kaplan-Meier curves stratified patients into different risk groups. We further compared the STAR model (utilizing HE and RT methodologies) with alternative nomograms grounded in molecular profiling (employing second-generation short-read sequencing methodologies) and imaging (utilizing PET-CT methodologies). Results: The STAR nomogram, incorporating subtype, TNM stage, age and preoperative RT data (LYM, LYM%, EOSO%, RDW-SD, P-LCR), achieved a C-index of 0.828 in the training cohort and impressive AUCs (0.847, 0.823 and 0.780) for 3-, 5- and 7-year OS rates, outperforming other nomograms. The validation cohort showed similar impressive results. The nomogram calculates a patient's total score by assigning values to each risk factor, higher scores indicating a poor prognosis. STAR promises potential cost savings by enabling less intensive surveillance in around 90% of BC patients. Compared to nomograms based on molecular profiling and imaging, STAR presents a more cost-effective, with potential savings of approximately $700-800 per breast cancer patient. Conclusion: Combining appropriate RT parameters, STAR nomogram could help in the detection of patient anemia, coagulation function, inflammation and immune status. Practical implementation of the STAR nomogram in a clinical setting is feasible, and its potential clinical impact lies in its ability to provide an early, economical and reliable tool for survival prediction and surveillance strategy management. However, our model still has limitations and requires external data validation. In subsequent studies, we plan to mitigate the potential impact on model robustness by further updating and adjusting the data and model.

Clonorchis sinensis infection amplifies hepatocellular carcinoma stemness, predicting unfavorable prognosis.

BACKGROUND: Extensive evidence links Clonorchis sinensis (C. sinensis) to cholangiocarcinoma; however, its association with hepatocellular carcinoma (HCC) is less acknowledged, and the underlying mechanism remains unclear. This study was designed to investigate the association between C. sinensis infection and HCC and reveal the relationship between C. sinensis infection and cancer stemness. METHODS: A comprehensive analysis of 839 HCC patients categorized into C. sinensis (-) HCC and C. sinensis (+) HCC groups was conducted. Chi-square and Mann-Whitney U tests were used to assess the association between C. sinensis infection and clinical factors. Kaplan-Meier and Cox regression analyses were used to evaluate survival outcomes. Immunohistochemistry was used to determine CK19 and EpCAM expression in HCC specimens. RESULTS: Compared to C. sinensis (-) HCC patients, C. sinensis (+) HCC patients exhibited advanced Barcelona Clinic Liver Cancer (BCLC) stage, higher male prevalence and more liver cirrhosis as well as elevated alpha-fetoprotein (AFP), carbohydrate antigen 19-9 (CA19-9), eosinophil, complement 3 (C3), and complement 4 (C4) values. C. sinensis infection correlated with shorter overall survival (OS) (p < 0.05) and recurrence-free survival (RFS) (p < 0.05). Furthermore, Cox multivariate analysis revealed that C. sinensis infection was an independent prognostic factor for OS in HCC patients. Importantly, C. sinensis infection upregulated the expression of HCC cancer stem cell markers CK19 and EpCAM. CONCLUSION: HCC patients with C. sinensis infection exhibit a poor prognosis following hepatectomy. Moreover, C. sinensis infection promotes the acquisition of cancer stem cell-like characteristics, consequently accelerating the malignant progression of HCC. AUTHOR SUMMARY: Clonorchis sinensis (C. sinensis) is a prominent food-borne parasite prevalent in regions such as China, particularly in Guangxi. C. sinensis has been associated with various hepatobiliary system injuries, encompassing inflammation, periductal fibrosis, cholangiocarcinoma and even hepatocellular carcinoma (HCC). A substantial body of evidence links C. sinensis to cholangiocarcinoma, However, the connection between C. sinensis and HCC and the intricate mechanisms underlying its contribution to HCC development remain incompletely elucidated. Our study demonstrates clear clinicopathological associations between C. sinensis and HCC, such as gender, BCLC stage, liver cirrhosis, MVI, AFP, CA19-9, circulating eosinophils and complements. Furthermore, we found that the co-occurrence of C. sinensis exhibited a significant association with shorter OS and RFS in patients diagnosed with HCC. A major finding was that C. sinensis infection promotes the acquisition of cancer stem cell-like characteristics, consequently accelerating the malignant progression of HCC. Our results provide a more comprehensive comprehension of the interplay between C. sinensis and HCC, shedding fresh light on the carcinogenic potential of C. sinensis.

Beta2-Microglobulin as Predictive Biomarkers in the Prognosis of Hepatocellular Carcinoma and Development of a New Nomogram.

Background: Accurate prognosis is crucial for improving hepatocellular carcinoma (HCC) patients, clinical management, and outcomes post-liver resection. However, the lack of reliable prognostic indicators poses a significant challenge. This study aimed to develop a user-friendly nomogram to predict HCC patients' post-resection prognosis. Methods: We retrospectively analyzed the data from 1091 HCC patients, randomly split into training (n=767) and validation (n=324) cohorts. Receiver operating characteristic (ROC) curves determined the optimal cut-off value for alpha1-microglobulin (α1MG) and Beta2-microglobulin (ß2MG). Kaplan-Meier analysis assessed microglobulin's impact on survival, followed by Cox regression to identify prognostic factors and construct a nomogram. The predictive accuracy and discriminative ability of the nomogram were measured by the concordance index (C-index), calibration curves, area under the ROC curve (AUC), and decision curve analysis (DCA), and were compared with the BCLC staging system, Edmondson grade, or BCLC stage plus Edmondson grade. Results: Patients with high ß2MG (≥2.395mg/L) had worse overall survival (OS). The nomogram integrated ß2MG, BCLC stage, Edmondson grade, microvascular invasion (MVI), and serum carbohydrate antigen 199 (CA199) levels. C-index for training and validation cohorts (0.712 and 0.709) outperformed the BCLC stage (0.660 and 0.657), Edmondson grade (0.579 and 0.564), and the combination of BCLC stage with Edmondson grade (0.681 and 0.668), improving prognosis prediction. Calibration curves demonstrated good agreement between predicted and observed survival. AUC values exceeded 0.700 over time, highlighting the nomogram's discriminative ability. DCA revealed superior overall net income compared to other systems, emphasizing its clinical utility. Conclusion: Our ß2MG-based nomogram accurately predicts HCC patients' post-resection prognosis, aiding intervention and follow-up planning. Significantly, our nomogram surpasses existing prognostic indicators, including BCLC stage, Edmondson grade, and the combination of BCLC stage with Edmondson grade, by demonstrating superior predictive performance.

Integrated single-cell and bulk RNA sequencing analysis identifies a prognostic signature related to ferroptosis dependence in colorectal cancer.

Ferroptosis is an iron-dependent form of cell death induced by lipid oxidation with an essential role in diseases, including cancer. Since prognostic value of ferroptosis-dependent related genes (FDRGs) in colorectal cancer (CRC) remains unclear, we explored the significance of FDRGs in CRC through comprehensive single-cell analysis. We downloaded the GSE161277 dataset for single-cell analyses and calculated the ferroptosis-dependent gene score (FerrScore) for each cell type. According to each cell type-specific median FerrScore, we categorized the cells into low- and high-ferroptosis groups. By analyzing differentially-expressed genes across the two groups, we identified FDRGs. We further screened these prognosis-related genes used to develop a prognostic signature and calculated its correlation with immune infiltration. We also compared immune checkpoint gene efficacy among different risk groups, and qRT-PCR was performed in colorectal normal and cancer cell lines to explore whether the signature genes could be used as clinical prognostic indicators. In total, 523 FDRGs were identified. A prognostic signature including five signature genes was constructed, and patients were divided into two risk groups. The high-risk group had poor survival rates and displayed high levels of immune infiltration. Our newly developed ferroptosis-based prognostic signature possessed a high predictive ability for CRC.

Colorectal Cancer Cell Differentiation Trajectory Predicts Patient Immunotherapy Response and Prognosis.

OBJECTIVES: This study aimed to investigate the differentiation state and clinical significance of colorectal cancer cells, as well as to predict the immune response and prognosis of patients based on differentiation-related genes of colorectal cancer. INTRODUCTION: Colorectal cancer cells exhibit different differentiation states under the influence of the tumor microenvironment, which determines the cell fates. METHODS: We combined single-cell sequencing (scRNA-seq) data from The Cancer Genome Atlas source with extensive transcriptome data from the Gene Expression Omnibus database. We obtained colorectal cancer differentiation-related genes using cell trajectory analysis and developed a colorectal cancer differentiation-related gene based molecular typing and prognostic model to predict the immune response and prognosis of patients with colorectal cancer. RESULTS: We identified 5 distinct cell differentiation subsets and 620 colorectal cancer differentiation-related genes. Colorectal cancer differentiation-related genes were significantly associated with metabolism, angiogenesis, and immunity. We separated patients into 3 subtypes based on colorectal cancer differentiation-related gene expression in the tumor and found differences among the different subtypes in immune infiltration status, immune checkpoint gene expression, clinicopathological features, and overall survival. Immunotherapeutic interventions involving a highly expressed immune checkpoint blockade may be selectively effective in the corresponding cancer subtypes. We built a risk score prediction model (5-year AUC: .729) consisting of the 4 most important predictors of survival (TIMP1, MMP1, LGALS4, and ITLN1). Finally, we generated and validated a nomogram consisting of the risk score and clinicopathological variables. CONCLUSION: This study highlights the significance of genes involved in cell differentiation for clinical prognosis and immunotherapy in patients and provides prospective therapeutic targets for colorectal cancer.

Genome-Wide Characterization and Analysis of R2R3-MYB Genes Related to Fruit Ripening and Stress Response in Banana (Musa acuminata L. AAA Group, cv. 'Cavendish').

MYB is an important type of transcription factor in eukaryotes. It is widely involved in a variety of biological processes and plays a role in plant morphogenesis, growth and development, primary and secondary metabolite synthesis, and other life processes. In this study, bioinformatics methods were used to identify the R2R3-MYB transcription factor family members in the whole Musa acuminata (DH-Pahang) genome, one of the wild ancestors of banana. A total of 280 MaMYBs were obtained, and phylogenetic analysis indicated that these MaMYBs could be classified into 33 clades with MYBs from Arabidopsis thaliana. The amino acid sequences of the R2 and R3 Myb-DNA binding in all MaMYB protein sequences were quite conserved, especially Arg-12, Arg-13, Leu-23, and Leu-79. Distribution mapping results showed that 277 MaMYBs were localized on the 11 chromosomes in the Musa acuminata genome. The MaMYBs were distributed unevenly across the 11 chromosomes. More than 40.0% of the MaMYBs were located in collinear fragments, and segmental duplications likely played a key role in the expansion of the MaMYBs. Moreover, the expression profiles of MaMYBs in different fruit development and ripening stages and under various abiotic and biotic stresses were investigated using available RNA-sequencing data to obtain fruit development, ripening-specific, and stress-responsive candidate genes. Weighted gene co-expression network analysis (WGCNA) was used to analyze transcriptome data of banana from the above 11 samples. We found MaMYBs participating in important metabolic biosynthesis pathways in banana. Collectively, our results represent a comprehensive genome-wide study of the MaMYB gene family, which should be helpful in further detailed studies on MaMYBs functions related to fruit development, postharvest ripening, and the seedling response to stress in an important banana cultivar.

Mining lycodine-type alkaloid biosynthetic genes and genetic markers from transcriptome of Lycopodiastrum casuarinoides.

Objective: Lycopodiastrum casuarinoides, a fern of the Lycopodiaceae family, is a traditional Chinese medicine, which has similar efficacy to that of Huperzia serrata in treating rheumatoid arthritis (RA). However, they are different in the contents and compositions of lycopodium alkaloids. In this study, the biosynthesis related genes of lycopodium alkaloids and genetic markers are discovered in L. casuarinoides transcriptome. Methods: The plant of L. casuarinoides was collected and was subjected to the RNA isolation, cDNA library construction, high throughput RNA sequencing and bioinformatics analysis. Results: Totally 124, 524 high-quality unigenes were assembled from RNA sequencing reads, with an average sequence length of 601 bp. Among the L. casuarinoides transcripts, 61,304 shared the significant similarity (E-value < 10-5) with existing protein sequences in the public databases. From 124,524 unigenes, 47,538 open reading frames (ORFs) were predicted. Based on the bioinformatics analysis, all possible enzyme genes involved in the lycodine-type alkaloids biosynthetic pathway of L. casuarinoides were identified, including lysine decarboxylase (LDC), primary amine oxidase (PAO), malonyl-CoA decarboxylase, etc. Sixty-four putative cytochrome p450 (CYP) and 827 putative transcription factors were selected from the transcriptome unigenes as the candidates of lycodine-type alkaloids biosynthesis modifiers. Furthermore, 13,352 simple sequence repeats (SSRs) were identified from 124,524 unigenes, of which dinucleotide motifs AG/CT were the most abundant (50.1%). Meanwhile, we confirmed the amplification effectiveness of 25 PCR primer pairs for randomly selected SSRs. Conclusion: We obtained the comprehensive transcriptomic information from the high throughput RNA sequencing and bioinformatics analysis, which provided a valuable resource of transcript sequences of L. casuarinoides in public databases.

Quantum dots-labeled strip biosensor for rapid and sensitive detection of microRNA based on target-recycled nonenzymatic amplification strategy.

MicroRNAs (miRNAs) have been proved to be potential biomarkers in early cancer diagnosis. It is of great significance for rapid and sensitive detection of miRNAs, particularly with point-of-care (POC) diagnosis. Herein, it is the first time to construct quantum dots (QDs)-labeled strip biosensor based on target-recycled nonenzymatic amplification strategy for miRNA detection. In the system, QDs were served as bright, photostable signal labels, which endow this biosensor with good detection efficiency. Moreover, a target-recycled amplification strategy relies on sequence-specific hairpins strand displacement process without the assistance of enzymes, was introduced to further improve the sensitivity. Meanwhile eliminating the requirement of environment-susceptible enzyme protein makes it easy to preserve and enhances the stability and reproducibility of this sensor. Benefiting from these outstanding characteristics, this platform exhibited a good detection sensitivity range from 2fmol to 200fmol with a limit of 200amol, using only 20µL of sample within 80min. The assay was also 10-fold more sensitive than that with a conventional colloidal gold-based test strip for miRNA detection. Additionally, the analysis of miRNA in various tumor cell extracts was in accordance with the performance of quantitative realtime polymerase chain reaction (qRT-PCR). Clinical tumor samples were also tested, and 16 of 20 samples gave out positive signals, which demonstrated the practical application capacity of the biosensor. Therefore, the proposed biosensor holds great promise for potential POC applications and early cancer diagnosis.