ABSTRACT
BackgroundLupus is a heterogenous diseases which results in significant premature mortality. Most studies have evaluated risk factors for lupus mortality using regression models which considers the phenotype in isolation. Identifying clusters of patients on the other hand may help overcome the limitations of such analyses.ObjectivesThe objectives of this study were to describe the causes of mortality and to analyze survival across clusters based on clinical phenotype and autoantibodies in patients of the Indian SLE Inception cohort for Research (INSPIRE)MethodsOut of all patients, enrolled in the INSPIRE database till March 3st 2022, those who had <10% missing variables in the clustering variables were included in the study. The cause of mortality and duration between the recruitment into the cohort and mortality was calculated. Agglomerative unsupervised hierarchical cluster analysis was performed using 25 variables that define SLE phenotype in clinical practice. The number of clusters were fixed using the elbow and silhouette methods. Survival rates were examined using Cox proportional hazards models: unadjusted, adjusted for age at disease onset, socio-economic status, steroid pulse, CYC, MMF usage and cluster of the patients.ResultsIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting.Out of 2211 patients in the cohort, 2072 were included into the analysis. The median (IQR) age of the patients was 26 (20-33) years and 91.7% were females. There were 288 (13.1%) patients with juvenile onset lupus. The median (range) duration of follow up of the patients was 37 (6-42) months. There were 170 deaths, with only 77 deaths occurring in a health care setting. Death within 6 months of enrollment occured in in 80 (47.1%) patients. Majority (n=87) succumbed to disease activity, 23 to infections, 24 to coexisting disease activity and infection and 21 to other causes. Pneumonia was the leading cause of death (n=24). Pneumococcal infection led to death in 11 patients and SARS-COV2 infection in 7 patients. The hierarchical clustering resulted in 4 clusters and the characteristics of these clusters are represented in a heatmap (Figure-1A,B). The mean (95% confidence interval [95% CI] survival was 39.17 (38.45-39.90), 39.52 (38.71-40.34), 37.73 (36.77-38.70) and 35.80 (34.10-37.49) months (p<0.001) in clusters 1, 2, 3 and 4, respectively with an HR (95% CI) of 2.34 (1.56, 3.49) for cluster 4 with cluster 1 as reference(Figure 1C). The adjusted model showed an HR (95%CI) for cluster 4 of 2.22 (1.48, 3.22) with an HR(95%CI) of 1.78 (1.29, 2.45) for low socioeconomic status as opposed to a high socioeconomic status (Table 1).ConclusionIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting. Disease activity as determined by the traditional activity measures may not be sufficient to understand the true magnitude of organ involvement resulting in mortality. Clinically relevant clusters can help clinicians identify those at high risk for mortality with greater accuracy.Table 1.Univariate and multivariate Cox regression models predicting mortalityUnivariateMultivariateVariablesHazard ratio (95% Confidence interval)P valueHazard ratio (95% Confidence interval)P valueCluster1Reference-Reference-20.87 (0.57, 1.34)0.5320.89 (0.57, 1.38)0.59831.22 (0.81, 1.84)0.3371.15 (0.76, 1.73)0.51342.34 (1.56, 3.49)<0.0012.22(1.48, 3.22)<0.001Socioeconomic statusLower1.78 (1.29, 2.45)<0.001Pulse steroidYes1.6 (0.99, 2.58)0.051MMFYes0.71 (0.48, 1.05)0.083CYCYes1.42 (0.99, 2.02)0.052Proliferative LNYes0.99 (0.62, 1.56)0.952Date of birth age0.99 (0.98, 1.01)0.657CYC- cyclophosphamide, MMF- Mycophenolate mofetilFigure 1.A. Agglomerative clustering dendrogram depicting the formation of four clusters. B.Heatmap depicting distribution of variables used in clustering C. Kaplan-Meier curve showing the survival function across the 4 clusters[Figure omitted. See PDF]REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone eclared.
ABSTRACT
Background: The non-receptor tyrosine kinase, SRMS (Src-related kinase lacking C-terminal regulatory tyrosineand N-terminal myristoylation sites) is a member of the BRK family kinases (BFKs) that represents an evolutionarilyconserved relative of the Src family kinases (SFKs) and is shown to restrain autophagy in mammalian cells. Geneenrichment analyses using the Ingenuity Pathway analyses (IPA) of SRMS interacting partners predicted viralinfection as a significantly increased cellular processes. However, the correlation between SRMS and prognosis inLUAD (lung adenocarcinoma) and LUSC (lung squamous cell carcinoma) or its impact on COVID-19 isunderstudied. Methods: We analyzed the expression levels of SRMS, and the correlation between SRMS and immune infiltrationlevel in LUAD and LUSC was investigated using TIMER database. Open Target platform was used to analyze theassociation of SRMS with disease. In addition, we used GSE30589 databases to elucidate the changes of SRMSexpression in vitro after SARS-CoV infection in Vero E6 and MA-104 cells. Results: SRMS was elevated in LUAD and LUSC compared to normal tissues. The expression of SRMS wasnegatively correlated with the level of immune infiltration of CD8+T cells in LUAD and LUSC. We found that theexpression of SRMS increased in vitro after SARS-CoV infection in Vero E6 and MA-104 cells. Conclusion: SRMS expression increased significantly in LUAD and LUSC, and this elevated SRMS was negativelycorrelated with immune infiltration at tumors. SARS-CoV infection increased the SRMS expression. This suggeststhat patients with LUAD and LUSC may be more susceptible to SARS-CoV-2 due to increased SRMS levels thatinhibit autophagy and alter tumor microenvironment correlated with reduced immune infiltration, which in turn mayworsen the prognoses of LUAD and LUSC patients after SARS-CoV-2 infection.