Coronavirus disease 2019 (COVID-19) is a multi-systemic illness that became a pandemic in March 2020. Although environmental factors and comorbidities can influence disease progression, there is a lack of prognostic markers to predict the severity of COVID-19 illness. Identifying these markers is crucial for improving patient outcomes and appropriately allocating scarce resources. Here, an RNA-sequencing study was conducted on blood samples from unvaccinated, hospitalized patients divided by disease severity; 367 moderate, 173 severe, and 199 critical. Using a bioinformatics approach, we identified differentially expressed genes (DEGs), alternative splicing (AS) and alternative polyadenylation (APA) events that were severity-dependent. In the severe group, we observed a higher expression of kappa immunoglobulins compared to the moderate group. In the critical cohort, a majority of AS events were mutually exclusive exons and APA genes mostly had longer 3'UTRs. Interestingly, multiple genes associated with cytoskeleton, TUBA4A, NRGN, BSG, and CD300A, were differentially expressed, alternatively spliced and polyadenylated in the critical group. Furthermore, several inflammation-related pathways were observed predominantly in critical vs. moderate. We demonstrate that integrating multiple downstream analyses of transcriptomics, from moderate, severe, and critical patients confers a significant advantage in identifying relevant dysregulated genes and pathways.