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Colorectal cancer (CRC) is the third most common cancer in the world, with increasing morbidity and mortality. The current diagnosis and treatment plan rely on early detection and comprehensive treatment based on surgery. Still, most patients are in the middle and advanced stage at the time of diagnosis, and the therapeutic effect is very limited. Immunotherapy is a new cancer treatment method. The most commonly used are immune checkpoint inhibitors, specifically anti PD-1/PD-L1 interaction. However, immunotherapy has limitations, with its therapeutic effect closely related to the immune response in the tumor microenvironment. In recent years, the effect of arginine metabolism disorder on the occurrence and development of cancer and tumor immune regulation has attracted wide attention. Studies have confirmed that cancer cells cannot survive without arginine and arginine metabolites. In addition, arginine is essential for the proliferation, differentiation, and survival of tumor-infiltrating lymphocytes. High arginine levels can promote the anti-tumor immune response, thus increasing the effect of tumor immunotherapy. In this paper, the molecular mechanisms of arginine metabolism in the development and immune regulation of CRC are reviewed, with the authors hoping to provide potential evidence for the development of immunotherapy for CRC. Abbreviation CRC = Colorectal cancer, dMMR = Mismatch repair deficient, MSI-H = Microsatellite highly Instability, MSS = Microsatellite stability, TME = Tumor microenvironment, MHC = Major histocompatibility complex, pMMR = Mismatch repair proficient, NOS = Nitric Oxide synthase, NO = Nitric oxide, ASS-1 = Arginine succinate synthetase 1, ASL = Argininosuccinate lyase, ARG = arginase, CAT = Cationic amino acid transporter, AGAT = Arginine-glycine amidinotransferase, ADC = Arginine decarboxylase, ADI = Arginine deiminase, ODC = Ornithine decarboxylase, GCN2 = General control non-depressible 2, mTOR = Mammalian target of rapamycin, HIF-1 = Hypoxia-inducible factor 1, VEGF = Vascular endothelial growth factor, ICI = Immune checkpoint inhibitor, ORR = Objective response rate, DCR = Disease control rate, OS = Overall survival, PFS = Progress free survival, TIL = Tumor-infiltrating lymphocyte, TCM = Central memory T cell

Abstract Background Previous studies have reported that the tumor heterogeneity and complex oncogenic mechanisms of proximal and distal colon cancer (CRC) are divergent. Therefore, we aim to analyze the differences between left-sided CRC (L_cancer) and right-sided CRC (R_cancer), as well as constructing respective nomograms. Methods We enrolled 335 colon cancer patients (146 L_cancer patients and 189 R_cancer patients) from The Cancer Genome Atlas (TCGA) data sets, and 102 pairs of color cancer tissue and adjacent normal tissue (51 L_cancer patients and 51 R_cancer patients) from our hospital. Firstly, we analyzed the differences between the L_cancer patients and R_cancer patients, and then established the L_cancer and R_cancer prognostic models using LASSO Cox. Results R_cancer patients had lower survival than L_cancer patients. R_cancer patients had higher ESTIMATE and immune scores and lower tumor purity. These patterns of expression of immune checkpoint-related genes and TMB level were higher in R_cancer than in L_cancer patients. Finally, we using Lasso Cox regression analyses established a prognostic model for L_cancer patients and a prognostic model for R_cancer patients. The AUC values of the risk score for OS in L_cancer were 0.862 in the training set and 0.914 in the testing set, while those in R_cancer were 0.835 in the training set and 0.857 in the testing set. The AUC values in fivefold cross-validation were between 0.727 and 0.978, proving that the two prognostic models have great stability. The nomogram of L_cancer included prognostic genes, age, pathological M, pathological stage, and gender, the AUC values of which were 0.800 in the training set and 0.905 in the testing set. Meanwhile, the nomogram of R_cancer comprised prognostic genes, pathological N, pathological T, and age, the AUC values of which were 0.836 in the training set and 0.850 in the testing set. In the R_cancer patients, high-risk patients had a lower proportion of ‘B cells memory’, ‘Dendritic cells resting’, immune score, ESTIMATE score, immune checkpoint-related genes, and HLA-family genes, and a higher proportion of ‘T cells follicular helper’, ‘Dendritic cells activated’, and ‘Mast cells activated’. Conclusions We found significant differences between L_cancer and R_cancer patients and established a clinical predictive nomogram for L_cancer patients and a nomogram for R_cancer patients. Additionally, R_cancer patients in low-risk groups may be more beneficial from immunotherapy.

Objective To evaluate temporal lung changes in coronavirus disease 2019 (COVID-19) in high-resolution computed tomography (HRCT) and to determine the appropriate computed tomographic (CT) follow-up time. Methods Eighty-six patients with two or more HRCT scans who were diagnosed with COVID-19 were included. The CT score and major CT findings were evaluated. Results Eighty-two (95.3%) patients had lesions on the initial HRCT scans. Most scans showed bilateral, multifocal lung lesions, with multiple lobes involved and diffuse distribution. For fifty-seven patients with type I (progress compared with the initial CT score), the CT score reached a peak at 12 days and the nadir at 36 days. For twenty-nine patients with type II (no progress compared with the initial CT score), the lowest CT score was reached at 23 days. On the final HRCT scans (>21 days), patients with a reticular pattern were older than those without a reticular pattern. Conclusion The appropriate follow-up time of CT scans is during the second week (approximately 12 days) and the fourth to fifth weeks (approximately 23–36 days) from the onset of illness. These times could help reduce the CT radiation dose and show timely changes in the course of the disease by CT.

BackgroundAs the current outbreak of COVID-2019 disease has spread to the other more than 150 countries besides China around the world and the death number constantly increased, the clinical data and radiological findings of death cases need to be explored so that more physicians, radiologists and researchers can gain important information to save more lives.Methods73 patients who died from COVID-19 were retrospectively included. The clinical and laboratory data of the patients were extracted from electronic medical records. The clinical data, inflammation-related laboratory results, and CT imaging features were summarized. The laboratory results and dynamic changes of imaging features and severity scores of lung involvement based on chest CT were analyzed.ResultsThe mean age was 67±12 years. The typical clinical symptoms included fever (88%), cough (62%) and dyspnea (23%). 65% patients had at least one underlying disease. GGO with consolidation was the most common feature for the five lung lobes (47%-53% among the various lobes), with total severity score of 12.97±5.87 for the both lungs. The proportion of GGO with consolidation is markedly increased on follow-up chest CT compared with initial CT scans, as well as the averaging total CT scores (14.53±5.76 vs. 6.60±5.65; PConclusionChest CT findings and laboratory test results were worsening in patients who died of COVID-19, with moderate positive correlations between CT severity scores and inflammation-related factors of leucocytes, neutrophils, and IL-2R. Chest CT imaging may play an more important role in monitoring disease progression and predicting prognosis.