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This paper is based on the research hypothesis that the development of the digital economy can enable urban carbon emission reduction. We use the panel data of 275 prefecture-level cities in China from 2011 to 2019, the static panel-data interaction-effect model, and the panel-threshold model to verify the non-linear impact mechanism and heterogeneity of the digital economy in industrial structure upgrading affecting urban carbon emissions. The results demonstrate the following insights. First, due to the heterogeneity of industries, an increase in the proportion of the tertiary industry cannot reduce urban carbon emissions. Second, the digital economy has an inverted U-shaped adjustment effect on the process of industrial structure upgrading, affecting urban carbon emissions. Consequently, the integration and development of the tertiary industry and the digital economy can achieve urban carbon emission reductions. Finally, the digital economy has a double threshold effect on the process of industrial structure upgrading, affecting urban carbon emissions. The carbon-emission-reduction effect of industrial structure upgrading only appears after the scale of the digital economy crosses the first threshold. As the scale of the digital economy continues to increase, the carbon-emission-reduction effect of industrial structures is likely to continue increasing significantly. We recommend that local governments achieve urban carbon reduction by encouraging the development of high-end service industries and strengthening digital infrastructure.

Improving energy efficiency is an effective way to achieve carbon neutrality in the context of growing advocacy for a green economy in both academia and government. In this study, we analyzed the impact mechanism of the manufacturing agglomeration on total factor energy efficiency. Based on this work, we identified 30 provinces (autonomous regions and municipalities) in China using 2002 to 2017 panel data. We used the data envelopment analysis game cross-efficiency model to calculate the interprovincial current state of energy efficiency. We constructed a spatial Durbin model and used an adjacent space weight matrix, geographic distance weight matrix, and economic distance weight matrix to study the impact of manufacturing agglomeration on total factor energy efficiency. The results showed that under different spatial weights, the interprovincial total factor energy efficiency had a significant spatial dependence; under the three spatial weights, we identified a U-shaped relationship between manufacturing agglomeration and total factor energy efficiency. Industrial agglomeration had obvious spatial spillover effects on total factor energy efficiency, and the spillover effects under the weight of geographic distance were higher than other spatial weights. During the sample period, the direct, indirect, and total effects of the manufacturing industry had an impact on energy efficiency. It all had a negative number indicating that there was a crowding effect in manufacturing agglomeration, and it had an inhibitory effect on energy efficiency.

DNA damage response and repair (DDR) gene alterations increase tumor-infiltrating lymphocytes, genomic instability, and tumor mutational burden (TMB). Whether DDR-related alterations relate to therapeutic response and prognosis in lung cancer lacking oncogenic drivers remains unknown. Pretherapeutic cancer samples of 122 patients [86 non-small cell lung cancer and 36 small cell lung cancer (SCLC)] harboring no EGFR/ALK alterations were collected. Through whole-exome sequencing, we outlined DDR mutational landscape and determined relationships between DDR gene alterations and TMB or intratumoral heterogeneity. Then, we evaluated the impacts of DDR gene alterations on therapeutic response and prognosis and established a DDR-based model for prognosis prediction. In addition, we investigated somatic interactions of DDR genes and immunomodulatory genes, immune expression patterns, immune microenvironment, and immune infiltration characteristics between DDR-deficient and DDR-proficient samples. Samples from cBioportal datasets were utilized for verification. We found that deleterious DDR gene alterations were closely associated with higher TMB than proficient-types (p < 0.001). DDR mechanisms attach great importance to the determination of patients’ prognosis after chemotherapy, and alterations of base excision repair pathway in adenocarcinoma, nucleotide excision repair in squamous carcinoma, and homologous recombination pathway in SCLC tend to associate with worse progression-free survival to first-line chemotherapy (all p < 0.05). A predictive nomogram model was constructed incorporating DDR-related alterations, clinical stage, and smoking status, with the area under curve values of 0.692–0.789 for 1- and 2-year receiver operating characteristic curves in training and testing cohorts. Furthermore, DDR-altered tumors contained enhanced frequencies of alterations in various genes of human leukocyte antigen (HLA) class I pathway including TAP1 and TAP2 than DDR-proficient samples. DDR-deficient types had lower expressions of STING1 (p = 0.01), CD28 (p = 0.020), HLA-DRB6 (p = 0.014) in adenocarcinoma, lower TNFRSF4 (p = 0.017), and TGFB1 expressions (p = 0.033) in squamous carcinoma, and higher CD40 (p = 0.012) and TNFRSF14 expressions (p = 0.022) in SCLC. DDR alteration enhanced activated mast cells in adenocarcinoma (p = 0.044) and M2 macrophage in squamous carcinoma (p = 0.004) than DDR-proficient types. Collectively, DDR gene alterations in lung cancer without oncogenic drivers are positively associated with high TMB. Specific DDR gene alterations tend to associate with worse progression-free survival to initial chemotherapy.

BackgroundPeripheral blood biomarkers to immunotherapy have attracted more and more attentions owing to noninvasive nature. This study was designed to identify a panel of tumor associated autoantibodies (TAAbs) in plasma to predict the clinical outcome of ICIs-based treatment in advanced NSCLC patients and correlation between TAAbs and checkpoint inhibitor pneumonitis (CIP) would also be investigated.Materials and MethodsBaseline plasma was collected from patients with advanced NSCLC before receiving ICIs-based treatment. ELISA was used to detect concentration of autoantibodies. Clinical efficacy was evaluated according to RECIST v1.1.ResultsWe have identified a panel of five-TAAbs to predict responses of ICIs-based treatment in a discovery cohort (n = 37), and confirmed its predictive value in a validation cohort (n = 129). In the validation cohort, the positivity of this 5-TAAbs panel was significantly associated with better response (ORR: 44.4% vs. 13.6%, P < 0.001) and longer PFS (7.6 vs. 3.3m, P < 0.001). This significant association was remained in subgroup of patients treated with combination therapy (ORR: 43.8% vs. 13.7%, P = 0.004,PFS: 6.7 vs. 3.7m, P = 0 .017). Furthermore, this 5-TAAs panel worked better in patients who received subsequent-line treatment (ORR: 42.4% vs. 7.7%, P = 0.001, PFS: 6.2 vs. 3.0m, P = 0.004) than those received first-line treatment (ORR: 46.7% vs. 35.7%, P = 0.345, PFS: NR vs. 10.48m, P = 0.146). In addition, the CIP incidence in patients with 5-TAAbs positive was significantly higher comparing to negative patients (20.4% vs. 5.9%, P = 0.015).ConclusionOur 5-TAAbs panel is a potential predictive biomarker for responses and toxicities to ICIs-based treatment in patients with advanced NSCLC.