India had witnessed unprecedented surge in SARS-CoV-2 infections and its dire consequences during the second wave of COVID-19, but the detailed report of the epidemiological based spatiotemporal incidences of the disease is missing. In the manuscript, we have applied various statistical approaches (correlation, hierarchical clustering) to decipher the pattern of pathogenesis of the circulating VoCs responsible for surge in the incidences. B.1.617.1 (Kappa) was the predominant VoC during the early phase of the second wave, whereas, Delta (B.1.617.2) or Delta-like (AY.x) VoC constitutes majority ($$>90.17$$ > 90.17 %) of the cases during the peak of the second wave. The correlation plot of Delta/Delta-like lineage demonstrates inverse correlation with other lineages including B.1.617.1, B.1.1.7, B.1, B.1.36.29 and B.1.36. The spatiotemporal analysis shows that most of the Indian states were affected during the peak of the second wave due to the Delta surge, and fall under the same cluster. The second cluster populated mostly by north-eastern states and the islands of India were minimally affected. The presence of signature mutations (T478K, D950N, E156G) along with L452K, D614G and P681R within the spike protein of Delta or Delta-like might cause elevation in the host cell attachment, increased transmission and altered antigenicity which in due course of time has replaced the other circulating variants.The timely assessment of new VoCs including Delta-like will provide a rationale for updating the diagnostic, vaccine development by medical industries and decision making by various agencies including government, educational institutions, and corporate industries.

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Background Clinopathological parameters such as age, residual tumor, grade and stage are often used to predict the survival of ovarian cancer patients, but still the 5-year survival of high grade serous ovarian cancer remains > 30%. Here we established a molecular gene signature-based scoring system using data of ovarian cancer cohorts that could potentially determine the median overall survival of high-grade serous ovarian cancer patients. Methods The data mining and analysis of raw expression data spanning over HGSOC cohorts (n = 4784) deposited in various data repositories were performed. The feature extraction/ selection tool using Cox, LASSO regression was conducted on training data to obtain predicted genes along with the coefficients that contribute to obtaining molecular prognostic score (mPS). The receiver operator characteristics curve were plotted to study prediction efficiency of mPS . findings: The 20 gene-based mPS predicted the 5-year overall survival with 70% efficiency both in training (n = 491) and test datasets (n = 491) and also applicable in training OTTA-SPOT HGSOC samples (n = 3762). The mPS has significant impact (HR [95%CI] = 6.1 [3.65–10.3]; p < 0.0001) on prognosis of HGSOC and prediction is more sensitive and specific as compared to clinopathological parameters: FIGO, age, residual disease. It was found that focal-adheson, Wnt, Notch signaling pathways are significantly upregulated whereas antigen processing and presentation are downregulated in high risk HGSOC cohorts. Interpretation: The molecular prognostic score derived from 20-gene signature is the prognostic marker or the risk classifier of HGSOC. It could be potentially harnessed in clinical settings to determine the overall survival of ovarian cancer.

The clinicopathological parameters such as residual tumor, grade, FIGO score are often used to predict the survival of ovarian cancer patients, but the 5-year survival of high grade serous ovarian cancer (HGSOC) still remains around 30%. In recent years, a gene expression based molecular prognostic score (mPS) was developed that showed improved prognosis in several cancers including ovarian cancer. The feature extraction using LASSO-Cox regression was applied on the training data with 10-fold cross validation to obtain 20 predictor genes along with the coefficients to derive mPS. The mPS based prognosis of HGSOC patients was validated using the log-rank test and receiver operator characteristic curve. The AUC of 20 gene-based mPS in predicting the 5-year overall survival was around 0.7 in both the training (n=491) and test datasets (n=491). It was also validated across HGSOC patients (n=7542), data collected from the Ovarian Tumor Tissue Analysis (OTTA) consortium. The mPS showed significant impact (adjusted HR = 6.1, 95% CI of HR= 3.65-10.3; p <0.001) on prognosis of HGSOC. The performance of mPS for the prognosis of survival of HGSOC was substantially better than conventional parameters: FIGO (adjusted HR=1.1, 95% CI=0.97-1.2, p=0.121), residual disease (adjusted HR=1.3, 95% CI= 1.13-1.4, p<0.001), and age (adjusted HR=1.2, 95% CI= 0.98-1.6, p=0.08). It was found that focal-adhesion, Wnt and Notch signaling pathways were significantly (p<0.001) upregulated, whereas antigen processing and presentation (p<0.001) was downregulated in high risk HGSOC cohorts based on mPS stratification. The molecular prognostic score derived from 20-gene signature is found to be the novel robust prognostic marker of HGSOC. It could potentially be harnessed in clinical settings to determine the overall survival of ovarian cancer. The high risk HGSOC patients based on mPS stratification could be benefited from alternative therapies targeting Wnt/ Notch signaling pathways and also immune evasion.