Abstract

   Risk factors vary in terms of the pattern of lung cancer metastasis and
   specific metastatic organs. In this study, we retrospectively analyzed
   the clinical risk factors of tumor metastasis in lung cancer patients
   and used second‐generation gene sequencing to characterize relevant
   gene mutations. The risk factors of different metastatic sites of
   real‐world lung cancer were explored to find the differentially
   expressed genes and risk factors in different metastatic organs, which
   laid a foundation for further study on the metastasis patterns and
   mechanisms of lung cancer. The clinical risk factors of tumor
   metastasis in 137 lung cancer patients who attended our department from
   May 2017 to March 2019 were retrospectively analyzed and grouped based
   on bone metastasis, brain metastasis, other distant metastasis, and no
   metastasis. Single‐ or multi‐factor logistic regression analysis was
   performed to analyze the effect of neutrophil/lymphocyte
   ratio/platelet/lymphocyte ratio/lymphocyte to monocyte ratio on
   platelets (PLTs) and bone metastasis by combining PLT values, age,
   pathology type, gender, and smoking history. Based on the presence or
   absence of bone metastasis, distal metastasis, and PLT values of lung
   cancer, 39 tissue specimens of primary lung cancer were taken for 773
   gene grouping and gene mutation characterization. The tumor mutation
   load, gene copy number instability, microsatellite instability, and
   tumor heterogeneity among different groups were analyzed. Age and PLT
   level were independent risk factors for bone metastasis and distal
   metastasis, but not for brain metastasis. The RB1 gene was mutated
   during bone metastasis, and tumor heterogeneity was less in the
   elevated PLT group. PLT values were an independent risk factor for
   distant metastases from lung cancer other than the brain. Age has a
   significant effect on bone metastasis formation. RB1 gene mutation was
   significantly associated with bone metastasis.

   Keywords: gene mutation characteristics, lung cancer, metastatic sites,
   risk factors
     __________________________________________________________________

   Age and platelet level were independent risk factors for bone
   metastasis and distal bone metastasis, but age and platelet level were
   not risk factors for brain metastasis. The RB1 gene was mutated during
   bone metastasis, and tumor heterogeneity was less in the elevated
   platelet group. Platelet values are an independent risk factor for
   distant metastases from lung cancer other than the brain. Age has a
   significant effect on bone metastasis formation. Mutations in the RB1
   gene are significantly associated with bone metastasis.

   graphic file with name CAM4-11-268-g001.jpg

1. INTRODUCTION

   The spread of tumor to distant organs is the cause of most cancer
   deaths.[36] ^1 The mechanisms of tumor‐stromal interactions have been
   better understood and studied.[37] ^2 Sequence analysis of single‐cell
   tumor genomes allows us to trace the clonal evolution of normal to
   mutated cells in primary tumors and to understand their clonal fate in
   multi‐metastatic polymetastases. Although there is a deep understanding
   of the mechanisms of tumor metastasis, limited progress has been
   presented in the epidemiological study of tumor metastasis.

   Although the general review on metastasis cites data on the
   site‐specific metastasis rates, no data source is provided.[38] ^3 The
   fundamental problem is that population‐based cancer registries focus on
   primary cancers, and few metastases are recorded. Some registries use
   the TNM classification, but the presence or absence of metastases at
   the time of diagnosis is rarely reported and there are no site‐specific
   data.[39] ^4 Most of the "anecdotal" literature on metastasis rates
   cited are derived from clinical experience, but this literature has
   some limitations, such as incomplete follow‐up. However, lung cancers
   often metastasize to bone, brain, lung, and liver, and patients
   generally encounter shortened survival. Therefore, investigation of
   metastatic modalities is very important for patient care.

   A recent study has shown that chronic inflammation may be associated
   with the development of lung cancer, platelets (PLTs) are not only
   thrombophilia effector cells but also mediate host immune responses
   such as innate immunity, acquired immunity and inflammatory
   responses.[40] ^5 Furthermore, increasingly researchers suggest that
   PLTs play a role in promoting tumor cell metastasis during the
   development of tumors. It has been suggested that tumor cells can
   activate PLT through various pathways of adhesion to produce a series
   of bioactive molecules to regulate tumor growth and metastasis, and
   many tumor patients with increased PLT numbers have significantly
   shortened survival than those without increased PLT numbers.[41] ^6 The
   association between PLT and the prognosis of lung cancer metastasis
   remains valuable in clinical research settings.

   Lung cancer metastasis patterns and specific metastatic organs have
   certain patterns. Identifying metastatic risk factors for specific
   organs of lung cancer using multifactorial analysis has received
   attention, but there exist many inconsistencies in the previous
   reports. In this study, we evaluated the clinical risk factors for lung
   cancer metastasis to different organs using logistic analysis. We also
   explored the pattern of lung cancer metastasis and the possible
   molecular mechanisms using lung cancer specimens with 39 routine 773
   gene sequencing, lung cancer specimens were grouped based on different
   metastatic organs and their risk factors, for gene mutation
   characterization.

2. METHODS

2.1. Patient inclusion

   This study included 137 patients with lung cancer who were clinically
   treated at our hospital and pathologically confirmed from May 2017 to
   March 2019. Patients were registered for age, pathological staging, the
   clinical stage, gender, smoking history, blood count, and site of
   metastatic lesions. The normal range of PLT count was
   94 × 10^9–268 × 10^9/L in the check system of our hospital. To deeply
   study the relationship between the increase of PLT count and tumor
   metastasis, the patients were allocated into three
   groups:PLT > 210 × 10^9/L and PLT > 350 × 10^9/L as cut‐off values. NGS
   with fixed panel was performed in all populations. The samples included
   primary lesion tissue or plasma. A total of 79 patients were tested for
   773 gene sequencing and 58 patients for 1406 or 543 gene sequencing.
   Although several panels covered part of the same gene, the specific
   sites covered were not completely the same. For the consistency in
   sample analysis, only 39 tissue samples from primary lung cancer
   lesions tested for 773 gene sequencing were included in this study for
   subsequent mutation characterization. The study protocol and all data
   were approved by the Ethics Committee of Army Medical Center. All
   patients have signed the informed consent.

2.2. Data extraction and analysis

   Clinical data were extracted by Army Medicine Centre and genetic
   testing data were sequenced and analyzed by Genecast Biotechnology Co.,
   Ltd. After the quality test of sequence measurements, the genomic
   positions were first determined using the sequence comparison software
   bwa, with the human genome reference sequence hg19 as a template; Next,
   single nucleotide variation (SNVs), Indels, and copy number variation
   (CNVs) were detected using the widely used detection software and
   algorithms (such as samtools, Vardict, Freebayes, and cnvkit). The
   detection of SNVs, Indels, CNVs A mutation type and frequency is
   inferred primarily from the pileup of sequenced reads, based on the
   support numbers and the quality of sequencing of different bases at
   uniform genomic positions. All mutation types are functionally
   annotated using the software annovar. The original mutations were then
   screened for somatic SNVs according to the following screening criteria
   and then tumor mutation burden (TMB) and tumor heterogeneity (MATH)
   were calculated: (i) located in intergenic or intronic regions; (ii)
   synonymous SNVs; (iii) allele frequencies ≥0.002 in the database exome
   aggregates (ExAC) and genomes; (iv) allele frequencies of 0.05 for
   tumor samples and 0.01 for plasma samples was 0.01; (v) bias mutations
   in reads; (vi) support reads <5; (vii) depth <30.

   Using the cnvkit software, the copy number values and gene values were
   calculated for each case as paired samples, and unstable CNI values
   were calculated for the whole sample based on the CNV values of the
   samples. If the copynumber of a gene was >4, the gene was considered to
   have experienced a CNV Gain. If the copynumber of a gene was <1, the
   gene was considered to have experienced a CNV Loss.

2.3. Statistical methods

   Data analysis was performed using both R3.5.1 and SPSS 23.0. Based on
   the clinical characteristics of the patients, the Mann–Whitney U test
   was used to analyze differences in the distribution of specific blood
   values between the two groups. The chi‐square test or Fisher's exact
   test was used to analyze correlations between important clinical
   factors. Univariate and multivariate logistic regression analysis was
   performed using SPSS to identify predictors for distant metastasis. The
   mutated genes and tumor mutation load (TMB), MATH, microsatellite
   instability (MSI), copy number instability (CNI), and copy number
   variation load (burden of copy number) in clinical samples were
   analyzed using bioinformatics methods, and the Wilcoxon test was used
   to rank and test the mutated genes and TMB in different groups. The
   Wilcoxon test was used to rank and test the variance of mutated genes
   and TMB/MATH/MSI/CNI/Burden of copy number in different populations.
   Statistical analysis was performed using R software. The enrichment
   analyses were carried out using KOBAS 3.0 software.

3. RESULTS

3.1. Correlation of bone metastases with PLT values

   Clinical features of the entire population were shown in Table [42]1. A
   total number of 137 patients was used to evaluate the potential
   association between blood cell counts and distant metastasis. Bone
   metastases seriously affect the prognosis and treatment of lung cancer.
   Therefore, a simple and accessible index was urgently needed for
   evaluating bone metastases. Thus, in this study, we investigated the
   correlation between PLT values and the risk of bone metastases. The PLT
   counts in patients with bone metastasis were significantly higher than
   those without bone metastasis (p = 0.0084, p = 0.0015)
   (Figure [43]1A,B). The percentage of bone metastases was higher in
   patients with PLT > 350 × 10^9/L (60.7%) and
   210 × 10^9/L < PLT ≤ 350 × 10^9/L (57.9%) than that in patients with
   PLT ≤ 210 × 10^9/L (27.3%), showing a statistically significant
   difference (p = 0.007) (Figure [44]1C). Patients with PLT
   values > 210 × 10^9/L developed a higher percentage of bone metastases
   (58.7%) than those with PLT values ≤210 × 10^9/L (27.3%), with a
   statistically significant difference (p = 0.002) (Figure [45]1D).
   Patients aged ≥55 years had a higher percentage of bone metastases
   (57.4%) than those aged <55 years (33.3%) (p = 0.013) (Figure [46]1E).
   Moreover, univariate and multivariable logistic regression analysis
   showed that only age (OR = 2.588, 95% CI: 1.136–5.895, p = 0.024) and
   PLTs (OR = 3.659, 95% CI: 1.526–8.777, p = 0.004) were independent risk
   factors for bone metastasis. (Figure [47]1).

TABLE 1.

   Baseline characteristics of the entire cohort
                  n (%)
   Gender
   Female         57 (41.6)
   Male           80 (58.4)
   Age
   <55            36 (26.3)
   ≥55            101 (73.7)
   Smoking
   No             76 (55.5)
   Yes            61 (44.5)
   Histology
   Adenocarcinoma 108 (78.8)
   Squamous cell  19 (13.9)
   Small cell      7 (5.1)
   Others          3 (2.2)
   T stage
   T1–2           80 (58.4)
   T3–4           57 (41.6)
   N stage
   N0–1           43 (31.4)
   N2–3           94 (68.6)
   Clinical stage
   IA–IIIA        20 (14.6)
   IIIB–IV        117 (85.4)
   Bone metastasis
   No             68 (49.6)
   Yes            69 (50.4)
   Brain metastasis
   No             93 (67.9)
   Yes            44 (32.1)
   Other distant metastasis
   No             107 (78.1)
   Yes            30 (21.9)
   Distant metastasis
   No             36 (26.3)
   Yes            101 (73.7)
   Platelet counts
   <210           32 (23.4)
   210–350        77 (56.2)
   >350           28 (20.4)
   [48]Open in a new tab

FIGURE 1.

   FIGURE 1
   [49]Open in a new tab

   Correlation of bone metastases with multiple clinical factors. (A, B)
   The difference in platelet (PLT) counts between patients with and
   without bone metastasis (p = 0.0084, p = 0.0015). (C) Patients with PLT
   values >210 had a higher percentage of bone metastases (58.7%) than
   those with PLT values ≤210 (27.3%), with a statistically significant
   difference (p = 0.002). (D) The percentage of bone metastasis was
   significantly different among different PLT groups (p = 0.007);
   patients with PLT >350 (60.7%) and 210 < PLT ≤350 (57.9%) had a higher
   percentage of bone metastases than those with PLT ≤210 (27.3%). (E)
   Patients aged ≥55 years had a higher percentage of bone metastases
   (57.4%) than those aged <55 years (33.3%), with a statistically
   significant difference (p = 0.013)

3.2. Correlation of distal metastasis with multiple clinical factors

   In addition to that, PLT values were predictive of bone metastasis, we
   also examined the effect of PLT values on distal metastasis. Patients
   with PLT values > 210 developed a significantly higher percentage of
   distal metastasis (80.8%) than those with PLT values ≤ 210 (51.5%)
   (p = 0.001). Patients aged ≥55 years had more distal metastases (78.2%)
   than those aged <55 years (61.1%), with a statistically significant
   difference (p = 0.045). However, multivariable logistic regression
   analysis showed that only PLT value was an independent risk factor for
   distal metastasis (OR = 3.808, 95% CI: 1.626–8.917, p = 0.002)
   (Figure [50]2).

FIGURE 2.

   FIGURE 2
   [51]Open in a new tab

   Correlation of distal metastases with multiple clinical factors. (A)
   Patients with PLT values > 210 developed a significantly higher
   percentage of distal metastasis (80.8%) than those with PLT
   values ≤ 210 (51.5%) (p = 0.001). (B) Patients aged ≥55 years had more
   distal metastases (78.2%) than those aged <55 years (61.1%), with a
   statistically significant difference (p = 0.045)

   Brain metastasis is another important metastatic mode in tumor
   metastasis. We performed logistic regression analysis as well as
   chi‐square test for clinical factors (PLT value, age, pathological
   staging, gender, and smoking history), respectively, of brain
   metastasis. The results did not reveal a statistically significant
   effect on brain metastasis.

3.3. Effect of NLR/PLR/LMR on distal metastasis

   Firstly, we examined the correlation between high and low PLT count and
   blood count (neutrophil/lymphocyte ratio [NLR]/PLT/lymphocyte ratio
   [PLR]/lymphocyte to monocyte ratio [LMR]). We found the effect of LMR
   on PLT values had a statistically significant difference (p = 0.029).
   However, no statistical difference in the values of NLR/PLR between low
   and high PLT groups was found. Moreover, no statistical difference was
   found between NLR/PLR/LMR and the risk of bone or distant metastasis.
   Logistic regression analyses did not find any statistical difference
   between NLR/PLR/LMR values and low and high PLT groups as well as bone
   or distant metastasis. Taken together, these results suggested LMR
   could not be responsible for bone or distant metastasis (Figure [52]3).

FIGURE 3.

   FIGURE 3
   [53]Open in a new tab

   Results of the analysis of routine blood data. (A–C) There was a
   significant difference in lymphocyte monocyte ratio (LMR) between
   different platelet value groups, with a statistically significant
   difference (p = 0.029). No statistical difference in the values of
   NLR/PLR between low and high PLT groups was found. (D–F) No statistical
   difference was found between NLR/PLR/LMR and the risk of bone
   metastasis. (G–I) No statistical difference was found between
   NLR/PLR/LMR and the risk of distal metastasis

3.4. Analysis of gene mutation characteristics among different subgroups of
bone metastasis/distal metastasis/PLT high and low in lung cancer

3.4.1. Characterization of gene mutations among different subgroups of PLT
high and low PLT in lung cancer

   The somatic SNV & Indel of the PLT‐H (PLT values > 210 × 10^9/L) and
   PLT‐L (PLT values ≤210 × 10^9/L) groups were analyzed by bioinformatics
   methods and statistically analyzed. The TOP10 mutated genes of the two
   groups were partially different, and no statistical differences were
   found. The gene base conversion variants and reversal variants in
   samples of each group were statistically analyzed using bioinformatics
   methods. The PLT‐H group showed Tv>Ti, where the variant type was most
   frequently C>A, followed by C>T; the PLT‐L group showed Ti>Tv, where
   the variant type was most frequently C>T, followed by T>G. We found
   that there was a statistical difference in the number of mutations in
   ARIDIB and NF2 mutations between the PLT‐H group and the PLT‐L group.
   (ARID1B: p = 0.035; NF2: p = 0.035). Next, we focused on the mutations
   in several important cancer driver genes. We found that the mutational
   rate of EGFR/KRAS/ALK/TP53 genes showed no statistical difference
   between the PLT‐L and PLT‐H groups (66.67% vs. 74.07%, p = 0.709). No
   statistical difference was seen in the analysis of SNV variant gene
   KEGG pathway enrichment between the PLT‐H group and the PLT‐L group. No
   statistical difference was seen in CNV between the PLT‐L group and the
   PLT‐H group (Figure [54]4).

FIGURE 4.

   FIGURE 4
   [55]Open in a new tab

   Different tumor samples may exhibit a preference for point mutation
   types. Statistical analysis of gene base conversion variants and
   reversal variants in each group of samples was performed by
   bioinformatics: (A) the PLT‐H group showed Tv>Ti, with the most variant
   types being C>A, followed by C>T; (B) the PLT‐L group showed Ti>Tv,
   with the most variant types being C>T, followed by T>G. (C) the PLT‐L
   group showed a statistically different number of mutations for ARIDIB
   and NF2 mutations compared to the PLT‐H group. The number of mutations
   in the ARIDIB and NF2 mutations was statistically different in the
   PLT‐L group compared to the PLT‐H group. (D) The proportional
   relationship between EGFR/KRAS/ALK/TP53 gene mutations and platelet
   high and low PLT. (E) Analysis of the difference in Somatic CNV between
   the PLT‐H and PLT‐low groups

3.4.2. Gene mutation characteristics of different subgroups of lung cancer
bone metastases

   The somatic SNV&Indel of the two groups of bone metastases were
   analyzed by bioinformatics methods and statistically analyzed. The
   TOP10 mutated genes were partially different between the two groups,
   with no statistical difference. The gene base conversion variants and
   reversal variants were statistically analyzed by bioinformatics methods
   in the samples of each group. Both groups showed Tv>Ti, where the most
   variant types were C>T, followed by C>A. There was a statistical
   difference in the number of RB1 gene mutations in the bone metastasis
   group compared with the no bone metastasis group. Surprisingly, no
   statistical difference was found in the enrichment analysis of the KEGG
   pathway of SNV variants between the bone metastasis and boneless
   metastasis groups. Also, no statistical difference in CNV was observed
   between the bone metastasis group and the no bone metastasis group,
   indicating that single nucleotide variants were not the main causes for
   bone metastasis (Figure [56]5).

FIGURE 5.

   FIGURE 5
   [57]Open in a new tab

   Bone metastases Somatic SNV Ti/Tv statistical analysis. Different tumor
   samples may exhibit a preference for point mutation type. (A) Bone
   metastases Somatic SNV Ti/Tv. (B) No bone metastases Somatic SNV Ti/Tv.
   (C) Differential analysis of bone metastases Somatic SNV. (D) Bone
   metastases Somatic CNV differential analysis

3.4.3. Gene mutation characteristics of different subgroups of distal lung
cancer metastasis

   The somatic SNV&Indel of the two groups with and without distal
   metastasis were analyzed by bioinformatics methods and statistically
   analyzed. We found that the TOP10 mutated genes in the two groups were
   partially different, with no statistical difference was found
   (Figure [58]6).

FIGURE 6.

   FIGURE 6
   [59]Open in a new tab

   Analysis of genetic differences in different subgroups of distal
   metastases from lung cancer. Distal metastasis Somatic SNV Ti/Tv
   Statistical analysis. (A) Distal metastasis Somatic SNV Ti/Tv. (B)
   No‐distal metastasis Somatic SNV Ti/Tv. (C) Differential analysis of
   distal metastasis Somatic SNV. (D) Differential analysis of distal
   transfer Somatic CNV

   After that, we analyzed the point mutation types of the two groups with
   and without distal metastasis. By bioinformatics methods for gene base
   conversion variants and reversal variants in the samples of each group,
   we found that both groups showed Tv>Ti, with the most variant types in
   the group with distal transfer C>T, followed by C>A; while the variant
   types in the group without distal transfer C>T were similar to C>A.
   There was a statistical difference in the number of mutations in AMER1
   and EPHA3 mutations in the group without distal metastasis compared
   with the group with distal metastasis. No statistical difference was
   found in the KEGG pathway enrichment analysis of SNV variants between
   the distal metastasis and no‐distal metastasis groups. No statistical
   difference in CNV was seen between the distal metastasis group and
   no‐distal metastasis group. These results suggested that single
   nucleotide variants were not the main causes for distal metastasis
   (Figure [60]6).

3.4.4. Other genomic structural variants in different subgroups of lung
cancer

   Tumor mutation burden, CNI and MSI have been found to affect the degree
   of tumor metastasis. We found there's no statistical difference in TMB
   between different subgroups. No statistical differences were found in
   CNI between subgroups, as well as in MSI. Interestingly, there was a
   statistical difference in PLT high and low groups in terms of MATH
   (p = 0.04), while no statistical difference was seen in terms of MATH
   between the bone metastasis and distal metastasis groups. No
   statistical difference was seen in the burden of copy number between
   the different subgroups. This result suggests that single nucleotide
   variants were not the main causes for distal metastasis. The structural
   features of genes determine whether genes are prone to recombination.
   Hence the above data suggested that genomic structural variants were
   not the main influencing factors of lung cancer metastasis
   (Figure [61]7).

FIGURE 7.

   FIGURE 7
   [62]Open in a new tab

   Differential analysis of other biomarkers for different subgroups of
   lung cancer. Tumor mutation load (TMB) differential analysis. (A)
   Differential analysis of platelet high and low TMB. (B) Differential
   analysis of TMB in bone metastasis group. (C) Differential analysis of
   TMB in the distal metastasis group. Copy number instability (CNI)
   difference analysis. (D) Differential analysis of high and low platelet
   CNI. (E) Differential analysis of CNI in the bone metastasis group. (F)
   Differential analysis of CNI in the distal metastasis group.
   Microsatellite instability (MSI) difference analysis. (G) Differential
   analysis of high and low MSI in platelets. (H) Differential analysis of
   MSI in the bone metastasis group. (I) Differential analysis of MSI in
   the distal metastasis group. (H) Differential analysis of tumor
   heterogeneity (MATH). (J) Differential analysis of high and low
   platelet MATH. (K) Differential analysis of MATH in the bone metastasis
   group. (L) Differential analysis of MATH in the distal metastasis
   group. Differential analysis of Burden of Copy Number. (M) Differential
   analysis of high and low platelet CN. (N) Differential analysis of CN
   in bone metastasis group. (O) Differential analysis of CN in distal
   metastasis group

4. DISCUSSION

   The relevance of thrombocytosis to tumors has been studied for a long
   time, with Leopold Rees first identified that thrombocytosis was
   associated with solid tumors more than a century ago.[63] ^7 Current
   studies have suggested that PLTs are active in the whole process of
   tumorigenesis (including tumor growth, tumor cell extravasation, and
   cell metastasis.[64] ^8 , [65]^9 , [66]^10 They also play an important
   role in protecting cancer cells from chemotherapy‐induced apoptosis[67]
   ^11 and in maintaining the integrity of tumor vasculature.[68] ^12
   Thrombocytosis in cancer patients is associated with poor survival.
   Gonzalez Barcala et al. have used the increased PLT count as a risk
   factor in assessing the prognosis of lung cancer, they concluded that
   patients with high PLT had a 37% lower survival rate than those with
   low PLT levels.[69] ^13 Gupta et al. have concluded that PLT‐derived
   lysophosphatidic acid was selected by invasive breast and ovarian
   cancer cells as a tumor cell pro‐divider and bone metastasis promoter
   of osteolysis in the process.[70] ^14 PLT‐derived autotrophic factors
   and lysophosphatidic acid promote breast cancer metastasis to bone.[71]
   ^15 , [72]^16 Moreover, PLTs direct the formation of pre‐metastatic
   ecotone in the bone by secreting transforming growth factor‐β and
   matrix metalloproteinase‐1.[73] ^17

   In the present study, PLT value was validated as the independent
   influence factor on bone metastasis and distal metastasis; neither age
   nor PLTs were influence factors of brain metastasis. It is important to
   investigate the mechanism of PLT involvement in tumor metastasis.
   Patients older than 55 years in our study had a higher incidence of
   bone metastasis suggested that bone age may be an important factor
   influencing bone metastasis in lung cancer. However, our results are
   contrary to the findings of Tumor Biol et al., who concluded that
   patients with bone metastases were younger than those without
   metastases,[74] ^18 which may be due to their fewer enrolled cases. In
   addition, in our study, it was suggested that PLT values were an
   independent influence factor of distant metastases of lung cancer other
   than brain and brain metastasis may have different mechanisms than
   metastases from other sites throughout the body. Proteomic analysis
   confirms that cell migration‐inducing and hyaluronan‐binding protein
   (CEMIP) is elevated in the exosomes of brain metastatic cells but not
   in the lung or bone metastatic cells. The uptake of CEMIP+exosomes by
   brain endothelial cells and microglia induces perivascular endothelial
   cell branching and inflammation through the upregulation of Ptgs2, TNF
   and CCL/CxCL cytokines, which promote cerebral vascular remodeling and
   metastasis.[75] ^19

   Moreover, we screened RB1 gene mutations by characterizing different
   subgroups of lung cancer bone metastases. Its regulated retinoblastoma
   protein is a typical tumor suppressor that regulates cell cycle
   progression. Mutational inactivation of the RB1 gene is an oncogenic
   factor in various cancers, including lung cancer. Stathmin‐mediated
   disruption of microtubule dynamics is a key factor that induces the
   combined lethality of RB1‐deficient cancers and suggests that upstream
   factors that regulate microtubule dynamics, such as AURKA could be
   potential therapeutic targets for RB1‐deficient cancers.[76] ^20 In the
   untreated EGFR‐mutated lung adenocarcinomas (LuADs), RB1 is excessively
   altered by inactivation, mainly through complex intragenic
   rearrangements.[77] ^21 In the kras‐driven lung cancers, loss of RB1
   promotes a glycolytic phenotype but does not alter pyruvate oxidative
   metabolism or glutamine inactivation.[78] ^22 It has been suggested
   that EGFR/TP53/RB1 mutant lung cancers have a unique risk of
   histological transformation, with 25% of lung cancers exhibiting ab
   initio small cell lung cancer or eventual small cell
   transformation.[79] ^23 Another meta‐analysis has shown that loss of
   RB1 function leads to a 1.6 two‐fold increase in the mortality of
   patients with osteosarcoma, a significant increase in osteosarcoma
   metastasis, and a notable decrease in osteosarcoma response to
   chemotherapy.[80] ^24 Herein, more presence of RB1 gene mutations in
   bone metastases of lung cancer was correlated to the worse prognosis of
   patients, and RB1 mutations may lead to tumor differentiation and
   growth and promote tumor metastasis. At present, a study has revealed
   that the functional status of immune cell subsets may be conducive to
   tumor distant metastasis. Massimiliano Mazzone et al[81] ^25 found that
   the presence of PoEMs in the lymphatic niche fosters lymphangiogenesis
   and aids cancer dissemination. Our study preliminarily found the
   relationship between the change of RB1 CNV and distal metastasis. The
   mechanism of RB1 on distant metastasis needs further clarification by
   studying the regulation of RB1 CNV on the immune microenvironment.
   However, there are only DNA detection data, no RNA sequencing, and no
   protein level detection, based on the current data, it is impossible to
   analyze the correlation between the immune cells’ activity and the
   distal metastases. Little is known about the association between bone
   metastases and RB1 gene mutations, which deserves in‐depth analysis in
   subsequent studies.

   Searching for easily accessible prognostic indicators has been an
   important part for cancer research. It has been suggested that
   prognostic biomarkers in stage IV non‐small cell lung cancer may
   include NLR, LMR, PLR, and advanced lung cancer inflammatory index
   (ALI).[82] ^26 This study showed that high NLR, high PLR, low LMR, and
   low ALI were significantly associated with poor overall survival (OS);
   high NLR and low ALI were significantly associated with poor OS after
   treatment. The present study, however, reached inconsistent
   conclusions, showing no statistical difference between NLR/PLR/LMR and
   the risk of bone metastases. It has also been reported in the
   literature that the mutation status of EGFR/KRAS/ALK/TP53 genes is
   associated with metastasis in different tissues and organs of lung
   cancer; in lung adenocarcinoma (LADC) patients, KRAS mutation frequency
   showed changes related to the metastatic site, and KRAS mutation was
   associated with significant poor prognosis in cases of bone
   metastasis.[83] ^27 Multifactorial analysis showed that bone metastasis
   was a significant independent negative predictor of OS in patients with
   mutant and wild‐type EGFR.[84] ^28 In this study, the presence of
   mutations in any of the EGFR/KRAS/ALK/TP53 genes was defined as the MUT
   group, and the absence of gene mutations was defined as the WT group;
   the mutation status of the EGFR/KRAS/ALK/TP53 genes was not found to be
   statistically different between the PLT‐L and PLT‐H groups. It is
   suggested that PLT values may not correlate with the mutation status of
   the above‐mentioned genes. It is derived from the regulation of other
   mechanisms (such as the TLR4/MyD88 pathway) to promote tumor bone and
   distal metastasis. Tumor heterogeneity mainly lies in gene changes. The
   PLT‐H group had less tumor heterogeneity in this study, which suggested
   that the single factor PLT involved in lung cancer metastasis was not
   the main reason for tumor heterogeneity.

   The occurrence of SNV in the tumor eventually leads to the change of
   phenotype, which requires the comutation of multiple genes in the same
   or multiple pathways. For example, in ovarian cancer, only the
   simultaneous truncation mutation and missense mutation of ARID1A and
   PI3KCA may cause the growth of malignant metastatic tumor cells. In
   this study, only 37 samples were performed for NGS. It is difficult to
   find the difference of SNV as a binary variable between bone metastasis
   and no bone metastasis groups at the single‐gene level. Our research is
   only a preliminary exploration.

5. CONCLUSION

   In conclusion, PLTs represent a huge biorepository of tumor‐derived and
   bioactive molecules. PLTs help CTC to attach to endothelial cells and
   provide signals to establish the pre‐metastatic microenvironment. It is
   important to investigate the role of PLTs in tumor metastatic spread
   and tumor angiogenesis. In this study, we concluded that age and PLTs
   were independent risk factors for bone metastasis of lung cancer,
   respectively, and there were more RB1 gene mutations in bone metastasis
   and less tumor heterogeneity in the PLT‐H group. However, because the
   clinical sample size in this study was small, these conclusions need to
   be validated by more follow‐up studies.

CONFLICT OF INTEREST

   The authors declare that they have no competing interests.

AUTHORS' CONTRIBUTIONS

   Hua Zou and Jinlu Shan designed this study. Bin Wang and He Xiao
   collected the samples. Jiao Zhang and Dandan Liang performed the
   whole‐exome sequencing of all included samples and statistical
   analyses. Bin Wang and Shu Chen drafted the manuscript. Hua Zou
   provided critical comments, suggestions, and revised the manuscript.
   All authors read and approved the final version of the manuscript.

PATIENT CONSENT FOR PUBLICATION

   The studies involving human participants were reviewed and approved by
   the Ethics Committee of Army Medical Center of PLA. Ethics Committee of
   Army Medical Center of PLA Approval of Medical Research Involving
   People Ethical Ratification No: 2020 (58). The patients/participants
   provided their written informed consent to participate in this study.

ACKNOWLEDGMENTS