Background: Coronavirus disease (COVID-19) related pneumonia is leaded by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which infects host cells through receptors named Angiotensin-converting enzyme 2 (ACE2). Smoking is thought to be related to poor disease prognosis, as a large amount of evidence highlights the negative effects of smoking on lung health and its causal relationship with various respiratory diseases. Methods: We first evaluated the role of ACE2 expression level with tobacco effect. And correlation analyses were used to identify the related genes of ACE2 both in smoker trait and human normal multi-tissues. After intersections, hub genes were later used to further GSEA and co-regulated mechanisms were explored by GeneMANIA. We used UALCAN to perform survival curves of pan-cancers included 33 types of cancers. New clinical model of top co-occurrence cancer type was constructed and validated. Results: Effected by cigarette smoking, the expression level of ACE2 expressed statistically upwards in current smokers compared with never smokers, upwards in groups after acute smoke exposed compared with normal control. But no strong evidence detected in third-hand smoke, as poor amounts of samples, only 4. A little trend of expressing upwards became in groups after third-hand smoke compared with groups after clean air exposing. 4 genes included PIR, ADH7, AKR1C2 and AKR1C3 were identified as ACE2 related genes in smoker trait and human normal multi-tissues. Then, we made the survival curves of pan-cancers in 4 genes. Brain lower grade glioma was the co-occurrence type as both ACE2, PIR and AKR1C3 had the significantly prognostic situation. Later, we made the new clinical prediction model as the C-index was 0.827 and the Area Under Curves (AUCs) of 1-year survival, 2-year survival and 3-year survival were 0.921(95%CI, 0.882-0.961), 0.911(95%CI, 0.860-0.962) and 0.878(95%CI, 0.818-0.939). In inner validation, the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.777(95%CI, 0.640-0.914), 0.916(95%CI, 0.842-0.990) and 0.888(95%CI, 0.768-1.000). In outer validation one, the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.764(95%CI, 0.672-0.856), 0.848(95%CI, 0.783-0.913) and 0.748(95%CI, 0.656-0.841). And in outer validation two the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.740(95%CI, 0.654-0.826), 0.766(95%CI, 0.688-0.843) and 0.794(95%CI, 0.721-0.866). Conclusions: In our study, we compared the ACE2 expression level between smokers and non-smokers crowds and identified associated hub genes. Then we explored the further role of hub genes in tumor fields to identify the correlation and influence between the avoidable host factors such as smoking on COVID-19 contamination and tumor. ACE2 and AKR1C3 are potential prognostic genes for brain lower grade glioma, and we created the web dynamic nomogram and encapsulation app through validations.

Aggressive mature T-cell lymphoma (TCL) is a disease that carries a poor prognosis. The mechanism, at the functional protein level, of the resistance to therapy of TCL has not yet been well clarified. To comprehensively clarify the mechanism, we first reviewed the literature, as mentioned in the Introduction section, on 22 types of representative functional proteins reported as being potentially associated with treatment resistance, mainly in hematologic tumors. Next, we retrospectively analyzed the tumor cell expressions of these 22 functional proteins in 16 randomly selected patients with TCL who had received CHOP (cyclophosphamide, hydroxyl doxorubicin, oncovin, and prednisolone) therapy as first-line treatment. As a result, we propose a new three-group prognostic classification of TCL (the TCL Urayasu Classification) based on the mechanism of resistance to treatment of these tumors. The study subjects included 7 patients (44%) with peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS), 6 patients (38%) with angioimmunoblastic T-cell lymphoma (AITL), and 3 patients (19%) with anaplastic large T-cell lymphoma (ALCL). Immunohistochemistry was performed in paraffin-embedded tumor tissue sections prepared from these patients as antigens using antibodies against the 22 previously reported treatment-resistance-related proteins to determine the tumor cell expression statuses of these proteins. Log-rank test was performed to compare the overall survival (OS) after CHOP therapy of the patients showing positive and negative immunostaining for these proteins, as well as among patients grouped based on other factors. Overall, the median OS of the TCL patients was about 33.5 months. When compared by the disease type, the prognosis was found to be worse in the order of ALCL, AITL, and PTCL. A multivariate analysis showed that expression of glucose-regulated protein 94 (GRP94), a protein that serves as a pro-survival component under endoplasmic reticulum (ER) stress in the tumor microenvironment, was significantly associated with shortened survival. No significant difference was observed for the conventional poor prognostic factors: high-risk International Prognostic Index (IPI) and high-risk Prognostic Index for peripheral T-cell lymphoma, unspecified (PTCL-U) (PIT). However, when GRP94 was combined with six other factors, listed below, as survival-shortening factors, significant differences were observed. The 6 factors were 1) programmed cell death-ligand 1 (PD-L1) and 2) programmed cell death 1 (PD-1), which are involved in immune surveillance; 3) aldo-keto reductase family 1 member C3 (AKR1C3), which is an enzyme that inactivates HO (hydroxyl doxorubicin [H] and oncovin [O]) of CHOP; 4) P53, a tumor suppressor; 5) Glucose-regulated protein 78 (GRP78), an ER stress protein; 6) thymidine phosphorylase (TP), which is an enzyme involved in thymidine catabolism. Based on the combination of GRP94 and the six other factors expressed in the tumors, we propose a new prognostic classification system for TCL (TCL Urayasu classification). Group 1 (group with a relatively good prognosis): GRP94-negative (n = 6; median OS, 88 months; p < 0.01); Group 2 (group with a poor prognosis): GRP94-positive, plus expression of 2 of the 6 aforementioned factors (n = 5; median OS, 25 months; p > 0.05); and Group 3 (group with a very poor prognosis): GRP94-positive, plus expression of at least 3 of the 6 aforementioned factors (n = 5; median OS, 10 months; p < 0.01). Thus, the TCL Urayasu prognostic classification is a simple, useful, and innovative classification that also explains the mechanism of resistance to treatment for each functional protein. Accumulation of more cases and further analyses are expected in the future to enable clinical application, such as use of a stratified treatment approach and treatment using inhibitors.

We conducted a retrospective analysis of the immunohistochemical (IHC) staining GRP94, an ER stress protein on large B-cell lymphoma (LBCL) cells, intracellular p53, and 15 factors involved in the metabolism of CHOP regimen :AKR1C3 (HO-metabolism), CYP3A4 (CHOP-metabolism) and HO efflux pumps (MDR1, MRP1). The study subjects were 42 patients with LBCL at our hospital. IHC staining using antibodies against the 17 factors. The odds ratios by logistic regression analysis using a dichotomous variable of CR, non-CR/relapse were statistically significant: for MDR1, MRP1, and AKR1C3. The overall survival (OS) after R-CHOP was compared by the log-rank test. The four groups showed that Very good (5-year OS, 100%) consisted of 4 patients showed negative IHC staining for both GRP94 and CYP3A4. Very poor (1-year OS, 0%), consisted of 3 patients who showed positive results of IHC for both GRP94 and CYP3A4. The remaining 35 patients comprised two subgroups: Good (5-year OS 60-80%.): 15 patients who showed negative staining for both MDR1 and AKR1C3, Poor: 5-year OS, 10-20%) :20 patients who showed positive staining for either MDR, AKR1C3,,MRP1 or p53. The Histological Prognostic Index (HPI) (The four groups: Very poor, Poor, Good, and Very pood) is a breakthrough method for stratifying patients based on factors involved in the development of treatment resistance.