Discussion on key technologies of big data in financial budget performance management in low-carbon economy

Reducing energy consumption, pollution and waste emissions are the basic elements of the concept of low-carbon economy. There is an inseparable link between low-carbon economy and financial work in enterprises. In terms of financial work, it is an indispensable element in the development of enterprises. In order to help enterprises improve their financial performance with pertinence, comparability and applicability, this paper selects clear enterprise performance evaluation indicators for analysis from the financial perspective. It aims to help enterprises save energy in a low-carbon economic environment. In this study, the performance evaluation system and financial allocation method of Anhui enterprises’ financial expenditure are studied. In the empirical analysis, 10 ordinary enterprise undergraduate colleges in Anhui Province are selected as samples. This model covers the performance evaluation scope of most financial expenditures of general enterprises in Anhui Province, and is analyzed in the process of DEA model analysis. In the process of DEA model analysis, it can better explain the input-output performance of Anhui enterprises. In this paper, financial expenditure performance evaluation indicators designed based on principal component analysis, data envelopment analysis and other analysis methods can focus on reflecting the input and output of enterprises. It can realize the standardization of evaluation results and better compare the efficiency of financial capital expenditure between enterprises. In other words, the concept of low carbon in enterprises has given a certain standard for the development of financial work. The financial department of the enterprise must fully implement the concept of low carbon during the budget period. Only in this way can we effectively promote the development of enterprises towards low-carbon and environmental protection. Finally, it will lay a solid foundation for the sustainable development of the enterprise

A review of the therapeutic role of the new third-generation TKI olverembatinib in chronic myeloid leukemia

Several tyrosine kinase inhibitors (TKIs) have been developed as targeted therapies to inhibit the oncogenic activity of several tyrosine kinases in chronic myeloid leukemia (CML), acute lymphoid leukemia (ALL), gastrointestinal stromal tumor (GIST), and other diseases. TKIs have significantly improved the overall survival of these patients and changed the treatment strategy in the clinic. However, approximately 50% of patients develop resistance or intolerance to imatinib. For second-generation TKIs, approximately 30%–40% of patients need to change therapy by 5 years when they are used as first-line treatment. Clinical study analysis showed that the T315I mutation is highly associated with TKI resistance. Developing new drugs that target the T315I mutation will address the dilemma of treatment failure. Olverembatinib, as a third-generation TKI designed for the T315I mutation, is being researched in China. Preliminary clinical data show the safety and efficacy in treating CML patients harboring the T315I mutation or who are resistant to first- or second-line TKI treatment. Herein, we review the characteristics and clinical trials of olverembatinib. We also discuss its role in the management of CML patients

Identification and validation of a novel cuproptosis-related gene signature in multiple myeloma

Background: Cuproptosis is a newly identified unique copper-triggered modality of mitochondrial cell death, distinct from known death mechanisms such as necroptosis, pyroptosis, and ferroptosis. Multiple myeloma (MM) is a hematologic neoplasm characterized by the malignant proliferation of plasma cells. In the development of MM, almost all patients undergo a relatively benign course from monoclonal gammopathy of undetermined significance (MGUS) to smoldering myeloma (SMM), which further progresses to active myeloma. However, the prognostic value of cuproptosis in MM remains unknown.Method: In this study, we systematically investigated the genetic variants, expression patterns, and prognostic value of cuproptosis-related genes (CRGs) in MM. CRG scores derived from the prognostic model were used to perform the risk stratification of MM patients. We then explored their differences in clinical characteristics and immune patterns and assessed their value in prognosis prediction and treatment response. Nomograms were also developed to improve predictive accuracy and clinical applicability. Finally, we collected MM cell lines and patient samples to validate marker gene expression by quantitative real-time PCR (qRT-PCR).Results: The evolution from MGUS and SMM to MM was also accompanied by differences in the CRG expression profile. Then, a well-performing cuproptosis-related risk model was developed to predict prognosis in MM and was validated in two external cohorts. The high-risk group exhibited higher clinical risk indicators. Cox regression analyses showed that the model was an independent prognostic predictor in MM. Patients in the high-risk group had significantly lower survival rates than those in the low-risk group (p < 0.001). Meanwhile, CRG scores were significantly correlated with immune infiltration, stemness index and immunotherapy sensitivity. We further revealed the close association between CRG scores and mitochondrial metabolism. Subsequently, the prediction nomogram showed good predictive power and calibration. Finally, the prognostic CRGs were further validated by qRT-PCR in vitro.Conclusion: CRGs were closely related to the immune pattern and self-renewal biology of cancer cells in MM. This prognostic model provided a new perspective for the risk stratification and treatment response prediction of MM patients

A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM’s great heterogeneity and incurability, risk assessment and treatment choices are still difficult.Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes.Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study.Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients

Identification and validation of a platelet-related signature for predicting survival and drug sensitivity in multiple myeloma

Background: Significant progress has been achieved in the management of multiple myeloma (MM) by implementing high-dose therapy and stem cell transplantation. Moreover, the prognosis of patients has been enhanced due to the introduction of novel immunomodulatory drugs and the emergence of new targeted therapies. However, predicting the survival rates of patients with multiple myeloma is still tricky. According to recent researches, platelets have a significant impact in affecting the biological activity of tumors and are essential parts of the tumor microenvironment. Nonetheless, it is still unclear how platelet-related genes (PRGs) connect to the prognosis of multiple myeloma.Methods: We analyzed the expression of platelet-related genes and their prognostic value in multiple myeloma patients in this study. We also created a nomogram combining clinical metrics. Furthermore, we investigated disparities in the biological characteristics, immunological microenvironment, and reaction to immunotherapy, along with analyzing the drug susceptibility within diverse risk groups.Results: By using the platelet-related risk model, we were able to predict patients’ prognosis more accurately. Subjects in the high-risk cohort exhibited inferior survival outcomes, both in the training and validation datasets, as compared to those in the low-risk cohort (p < 0.05). Moreover, there were differences in the immunological microenvironments, biological processes, clinical features, and chemotherapeutic drug sensitivity between the groups at high and low risk. Using multivariable Cox regression analyses, platelet-related risk score was shown to be an independent prognostic influence in MM (p < 0.001, hazard ratio (HR) = 2.001%, 95% confidence interval (CI): 1.467–2.730). Furthermore, the capacity to predict survival was further improved when a combined nomogram was utilized. In training cohort, this outperformed the predictive value of International staging system (ISS) alone from a 5-years area under curve (AUC) = 0.668 (95% CI: 0.611–0.725) to an AUC = 0.721 (95% CI: 0.665–0.778).Conclusion: Our study revealed the potential benefits of PRGs in terms of survival prognosis of MM patients. Furthermore, we verified its potential as a drug target for MM patients. These findings open up novel possibilities for prognostic evaluation and treatment choices for MM

Prognostic significance of β2-microglobulin decline index in multiple myeloma

PurposeTo assess the prognostic significance of β2-microglobulin decline index (β2M DI) in multiple myeloma (MM).Methods150 MM patients diagnosed with MM were enrolled in this study. Cox proportional hazards model was used to analyze the uni- and multivariate prognosis in training cohort (n=105). A new combined prognostic model containing β2M DI was built up based on the data in training cohort. The validation group was used to verify the model.Resultsβ2M DI showed significant correlation with prognosis in both uni- and multivariate analyses and had a good correlation with complete response (CR) rate and deep remission rate. The ROC and calibration curves in validation cohort (n=45) indicated a good predictive performance of the new model. Based on the median risk score of the training group, we classified patients into high- and low- risk groups. In both training and validation groups, patients in the low-risk group had longer overall survival (OS) time than that in the high-risk group (p<0.05).Conclusionβ2M DI is a good predictive index for predicting treatment response and survival time in MM patients. The prognostic model added with β2M DI showed a better correlation with OS

Expression of PTEN, p53 and P-glycoprotein in Non-small cell Lung Cancer and Their Predictive Values

Background and objective Multiple drug resisting (MDR) phenotype is the sign of intrinsic or acquired resistance and is the key factor which leads to chemotherapy failure. It has been proven that PTEN, p53, P-gp expressions were related to drug resistance and prognosis of the patients with lung cancer. The aim of this study is to analyze the association of the expression of PTEN, p53, P-gp with postoperative survival in patients with non-small cell lung cancer (NSCLC), and to explore the relationship between the characteristics and drug resistance of NSCLC patients. Methods A total of 61 patients with NSCLC were followed up. Immunohistochemical staining using polyclonal PTEN, p53 and P-gp antibody were performed on paraffinimbedded specimens from 61 patients with NSCLC and 20 specimens of tumor-surrounding normal lung tissue were used as control. Results The PTEN expression was significantly lower in NSCLC than in tumor-surrounding normal lung tissue and the expression of p53 and P-gp were in the opposite aspects (P<0.05). Expression of PTEN was positively related with histology, clinical stage, lymphatic metastasis, p53 expression was positively related with gender, lymphatic metastasis and the three kinds of protein expression were related with prognosis (P<0.05). The expression of PTEN was related with p53 expression (r=-0.282, P<0.05), but the expression of PTEN and p53 were not related with P-gp in NSCLC (P<0.05). Conclusion To some extent, the expression level of PTEN, p53 and P-gp may predict the effect of radiotherapy, chemotherapy and prognosis of NSCLC