The Value of Nutritional Status in the Prognostic Analysis of Patients with AIDS-Related Lymphoma

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung cancer (NSCLC). The expression profile data of lung adenocarcinoma and lung squamous cell carcinoma were downloaded in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. A total of eight survival related long non-coding RNAs (lncRNAs) and 262 survival related mRNAs were filtered. By gene set enrichment analysis, 17 significantly correlated Gene Ontology signal pathways and 14 Kyoto Encyclopedia of Genes and Genomes signal pathways were screened. Based on the clinical survival and prognosis information of the samples, we screened eight lncRNAs and 193 mRNAs by single factor Cox regression analysis. Further single and multifactor Cox regression analysis were performed, 30 independent prognostication-related mRNAs were obtained. The PPI network was further constructed. We then performed the machine learning algorithms (Least absolute shrinkage and selection operator, Recursive feature elimination, and Random forest) to screen the optimized DEGs combination, and a total of 17 overlapping mRNAs were obtained. Based on the 17 characteristic mRNAs obtained, we firstly built a Nomogram prediction model, and the ROC values of training set and testing set were 0.835 and 0.767, respectively. By overlapping the 17 characteristic mRNAs and PPI network hub genes, three genes were obtained: CDC6, CEP55, TYMS, which were considered as key factors associated with survival of NSCLC. The in vitro experiments were performed to examine the effect of CDC6, CEP55, and TYMS on NSCLC cells. Finally, the lncRNAs-mRNAs networks were constructed.

Development and Evaluation of a Nomogram for Predicting the Outcome of Immune Reconstitution Among HIV/AIDS Patients Receiving Antiretroviral Therapy in China

This study aims to develop and evaluate a model to predict the immune reconstitution among HIV/AIDS patients after antiretroviral therapy (ART). A total of 502 HIV/AIDS patients are randomized to the training cohort and evaluation cohort. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis are performed to identify the indicators and establish the nomogram for predicting the immune reconstitution. Decision curve analysis (DCA) and clinical impact curve (CIC) are used to evaluate the clinical effectiveness of the nomogram. Predictive factors included white blood cells (WBC), baseline CD4+ T-cell counts (baseline CD4), ratio of effector regulatory T cells to resting regulatory T cells (eTreg/rTreg) and low-density lipoprotein cholesterol (LDL-C) and are incorporated into the nomogram. The area under the curve (AUC) is 0.812 (95% CI, 0.767∼0.851) and 0.794 (95%CI, 0.719∼0.857) in the training cohort and evaluation cohort, respectively. The calibration curve shows a high consistency between the predicted and actual observations. Moreover, DCA and CIC indicate that the nomogram has a superior net benefit in predicting poor immune reconstitution. A simple-to-use nomogram containing four routinely collected variables is developed and internally evaluated and can be used to predict the poor immune reconstitution in HIV/AIDS patients after ART.

Clinical Characteristics and Outcomes of AIDS-Related Burkitt Lymphoma in China: A Retrospective Single-Center Study

Objectives: Studies on the prognosis and risk stratification of patients with acquired immune deficiency syndrome (AIDS) - related Burkitt lymphoma (AR-BL) are rare. We aim to construct a novel model to improve the risk assessment of these patients. Methods: We retrospectively analyzed the clinical data of 34 patients over the past 10 years and the factors associated with progression-free survival (PFS) and overall survival (OS) were evaluated in univariate and multivariate Cox models. Then, the novel model consisting of screened factors was compared with the existing models. Results: With a 37-month median follow-up, the overall 2-year PFS and OS rates were 40.50% and 36.18%, respectively. The OS of patients who received chemotherapy was better compared with those without chemotherapy (P = .0012). Treatment with an etoposide, prednisone, oncovin, cyclophosphamide, and hydroxydaunorubicin-based regimen was associated with longer OS and PFS compared with a cyclophosphamide, doxorubicin, vincristine, and prednisone-based regimen (OS, P = .0002; PFS, P = .0158). Chemotherapy (hazard ratio [HR] = 0.075; 95% confidence interval [CI], 0.009-0.614) and Eastern Cooperative Oncology Group Performance Status (ECOG PS) 2 to 4 (HR = 4.738; 95% CI, 1.178-19.061) were independent prognostic factors of OS in multivariate analysis and we established a novel prognostic risk stratification model named GZ8H model with chemotherapy and ECOG PS. Conclusion: GZ8H showed better stratification ability than the international prognostic index (IPI) or Burkitt lymphoma IPI (BL-IPI). Furthermore, the C-index of the nomogram used to predict OS was 0.884 in the entire cohort and the calibration curve showed excellent agreement between the predicted and actual results of OS. No human immunodeficiency virus-related factors were found to be associated with OS and PFS of AR-BL patients in our study. Overall, the clinical characteristics and outcomes in AR-BL were shown and prognostic factors for OS and PFS were identified in this study.

Endoplasmic reticulum stress-related signature predicts prognosis and immune infiltration analysis in acute myeloid leukemia

OBJECTIVES

To construct an endoplasmic reticulum stress-related prognostic risk score (RS) model to predict prognosis and perform a preliminary analysis of immune infiltration in patients with acute myeloid leukemia (AML).

METHODS

The whole-genome expression data for AML and endoplasmic reticulum stress (ER stress)-related genes were downloaded from the GEO and GSEA databases, respectively. The samples were divided into death and survival groups, combined with clinical prognosis information. LASSO regression was used to construct a prognostic RS model. The Kaplan-Meier curve method was used to evaluate the association between different risk groups and actual survival prognosis information. A cox regression analysis was used to screen for independent survival prognostic clinical factors and construct a nomogram. CIBERSORT and ssGSEA was used for immune-related analysis.

RESULTS

Eighteen ER-stress related genes were identified and a comprehensive network was constructed. Further, 5 CC, 8 MF, 17 BP, and 2 KEGG pathways were enriched. Ten optimal DEGs were obtained and a prognostic risk model was constructed. Compared to the low RS group, the OS values of the high RS group were significantly lower. A significant correlation between the different risk groups and the actual prognosis was demonstrated. Ten immune cells with significantly different distributions in different risk groups were screened. KEGG enrichment analysis showed that there were 5 signaling pathways in the high-risk group.

CONCLUSIONS

The RS model can effectively predict the prognosis and has clinical implications for the prognosis of AML, combined with the correlation between different RS groups and the immune microenvironment.

Hepatocellular carcinoma immune prognosis score predicts the clinical outcomes of hepatocellular carcinoma patients receiving immune checkpoint inhibitors

OBJECTIVE

The predictive biomarkers of immune checkpoint inhibitors (ICIs) in hepatocellular carcinoma (HCC) still need to be further explored. This study aims to establish a new immune prognosis biomarker to predict the clinical outcomes of hepatocellular carcinoma patients receiving immune checkpoint inhibitors.

METHODS

The subjects of this study were 151 HCC patients receiving ICIs at Harbin Medical University Cancer Hospital from January 2018 to December 2021. This study collected a wide range of blood parameters from patients before treatment and used Cox's regression analysis to identify independent prognostic factors in blood parameters, as well as their β coefficient. The hepatocellular carcinoma immune prognosis score (HCIPS) was established through Lasso regression analysis and COX multivariate analysis. The cut-off value of HCIPS was calculated from the receiver operating characteristic (ROC) curve. Finally, the prognostic value of HCIPS was validated through survival analysis, stratified analyses, and nomograms.

RESULTS

HCIPS was composed of albumin (ALB) and thrombin time (TT), with a cut-off value of 0.64. There were 56 patients with HCIPS < 0.64 and 95 patients with HCIPS ≥ 0.64, patients with low HCIPS were significantly related to shorter progression-free survival (PFS) (13.10 months vs. 1.63 months, P < 0.001) and overall survival (OS) (14.83 months vs. 25.43 months, P < 0.001). HCIPS has also been found to be an independent prognostic factor in this study. In addition, the stratified analysis found a significant correlation between low HCIPS and shorter OS in patients with tumor size ≥ 5 cm (P of interaction = 0.032). The C-index and 95% CI of the nomograms for PFS and OS were 0.730 (0.680-0.779) and 0.758 (0.711-0.804), respectively.

CONCLUSIONS

As a new score established based on HCC patients receiving ICIs, HCIPS was significantly correlated with clinical outcomes in patients with ICIs and might serve as a new biomarker to predict HCC patients who cloud benefit from ICIs.

Prognostic value of nutritional status in patients with human immunodeficiency virus infection-related lymphoma

Objective

To investigate the predictive value of nutritional status on the prognosis of patients with human immunodeficiency virus (HIV) infection-related lymphoma.

Materials and methods

A total of 149 patients with HIV infection-related lymphoma who were admitted to our hospital from August 2012 to May 2022 were selected as research subjects. Based on the patient prognosis, they were divided into a poor prognosis group (n = 30) and a good prognosis group (n = 119). General data from patients in both groups were collected, and the nutritional status of the patients was evaluated using the Controlling Nutritional Status (CONUT) score. Factors affecting the prognosis of HIV infection-related lymphoma were analyzed using univariate and multivariate analyses, and a prediction model was developed based on the analyzed factors. The receiver operating characteristic (ROC) curve was used to analyze the prediction model of the CONUT score alone and included the CONUT score in the prognosis of patients with HIV infection-related lymphoma. The predictive value of the data was assessed, and a survival curve was drawn to compare the survival of patients with different nutritional statuses.

Results

There were significant differences in age, B symptoms, treatment conditions, International Prognostic Index (IPI), pathological stage, Eastern Collaborative Tumor Group physical status score (ECOG PS), CD4+ cell count, β2 microglobulin, and lactate dehydrogenase (LDH) between the poor prognosis group and the good prognosis group (p &lt; 0.05). The CONUT score of the poor prognosis group was higher than that of the good prognosis group, and the difference was statistically significant (p &lt; 0.05). A univariate analysis demonstrated that the age, B symptoms, treatment status, IPI, pathological stage, ECOG PS, CD4+ cell count, β2 microglobulin, LDH, and CONUT score were prognostic factors for patients with HIV infection-related lymphoma (p &lt; 0.05). The results of a multivariate regression analysis demonstrated that the age, B symptoms, treatment status, IPI, pathological stage, ECOG PS, and CONUT score were independent risk factors for the prognosis of patients with HIV infection-related lymphoma (p &lt; 0.05). The prediction model was constructed according to the multivariate Cox regression analysis results. The model formula was as follows: Logit(p) = −10.687 + 1.728 × age + 1.713 × B symptoms + 1.682 × treatment status + 1.810 × IPI + 1.643 × pathological stage + 1.584 × ECOG PS + 1.779 × CONUT score. The ROC curve was used to analyze the predictive value of the CONUT score alone and the predictive model including the CONUT score on the prognosis of patients with HIV infection-related lymphoma. The predictive value of the prognosis of patients with tumors was higher (p &lt; 0.05). According to the results of the ROC curve analysis, the patients were divided into a high CONUT group (CONUT &gt; 6.00 points, n = 31) and a low CONUT group (CONUT ≤ 6.00 points, n = 118) based on the Optimum threshold of the CONUT score. The survival curve showed that the survival rate of the high CONUT group was lower than that of the low CONUT group (p &lt; 0.05).

Conclusion

The poor prognosis of HIV infection-related lymphoma is related to nutritional status, which is an independent risk factor affecting the prognosis of patients and can be used as a practical indicator to predict the prognosis of patients.

Hypoalbuminemia predicts inferior outcome in patients with AIDS-related lymphoma

Background

The prognostic value of serum albumin in acquired immunodeficiency syndrome (AIDS)-related lymphoma (ARL) remains covered.

Methods

We retrospectively analyzed de novo ARL patients from 2013 to 2019 across three centers. Factors correlated with progression-free survival (PFS) and overall survival (OS) were evaluated in Kaplan–Meier, univariate and multivariate Cox proportional hazard models.

Results

A total of 86 ARL patients were enrolled with a median follow-up of 34 months. In the cohort, the OS and 2-year PFS rates were 37.5% and 35.4%, respectively. In multivariate models, older age (PFS, hazard ratios [HR] = 1.035, p = 0.037; OS, HR = 1.034, p = 0.041) and hypoalbuminemia (OS, HR = 0.910, p = 0.038) predicted inferior survival. ARL patients with hypoalbuminemia showed worse OS and 2-year PFS (p = 0.028 and p = 0.01, respectively), which was associated with poor Eastern Cooperative Oncology Group performance status (ECOG PS) and higher International Prognosis Index (IPI) score.

Conclusion

In conclusion, serum albumin at diagnosis is an independent prognostic factor for overall survival in AIDS-related lymphoma.

A novel prediction model to evaluate the probability of CD4+/CD8+ cell ratio restoration in HIV-infected individuals

BACKGROUND

Our study aimed to develop a clinical prediction model to evaluate the possibility of CD4+/CD8+ ratio restoration in HIV-positive individuals.

METHODS

About 1980, HIV/AIDS patients initiated with antiretroviral treatment from 1 January 2013, to 30 December 2016, at Beijing Ditan Hospital and achieved persistent virological suppression during the 4 years follow-up were included in this study. Multivariate Cox proportional regression analysis was used to identify the independent risk factors and establish a predictive model. The model's performance was assessed using the area under the receiver operating characteristic and calibration plots.

RESULTS

Overall, after 4 years of treatment, a total of 455 individuals (22.98%) restored their CD4+/CD8+ ratio (≥1). The area under the receiver operating characteristic was 0.782 and 0.743 in the deriving and validation cohort, respectively. The ultimate model included five indexes: age at AIDS diagnosis, albumin, and syphilis status, and baseline CD4+ and CD8+ values. A nomogram further visualized the model, and the calibration plots indicated high agreement of predicted and observed outcomes.

CONCLUSION

Our prediction model might be practical and easily applied to recognize HIV/AIDS individuals most likely to benefit from modern antiretroviral therapy.